AGRO: Adversarial discovery of error-prone Groups for Robust Optimization
Authors: Bhargavi Paranjape
Pradeep Dasigi
Vivek Srikumar
Luke Zettlemoyer
Hannaneh Hajishirzi
Venue: ICLR 2023 poster (2023)
Keywords: robust optimization
distributionally robust
slice discovery
error analysis
adversarial learning

Abstract: Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust optimization (G-DRO) can alleviate this problem by minimizing the worst-case loss over a set of pre-defined groups over training data. G-DRO successfully improves performance of the worst group, where the correlation does not hold. However, G-DRO assumes that the spurious correlations and associated worst groups are known in advance,  making it challenging to apply them to new tasks with potentially multiple unknown correlations. We propose AGRO---Adversarial Group discovery for Distributionally Robust Optimization---an end-to-end approach that jointly identifies error-prone groups and improves accuracy on them. AGRO equips G-DRO with an adversarial slicing model to find a group assignment for training examples which maximizes worst-case loss over the discovered groups. On the WILDS benchmark, AGRO results in 8\% higher model performance on average on known worst-groups, compared to prior group discovery approaches used with G-DRO. AGRO also improves out-of-distribution performance on SST2, QQP, and MS-COCO---datasets where potential spurious correlations are as yet uncharacterized. Human evaluation of ARGO groups shows that they contain well-defined, yet previously unstudied spurious correlations that lead to model errors.

Published as a conference paper at ICLR 2023
AGRO: ADVERSARIAL DISCOVERY OF PROOE- GROUPS FOR ROBUST OPTIMIZATION
Bhargavi Paranjape' Pradeep Dasigi² Vivek Srikumar
Luke Zettlemoyer" 1,4
Hannaneh Hajishirzi!..
Universiity of Washington, 2 Allen Institute of Artificial Intelligence, University of Utah
*Meta AI
ABSTRACT
Models trained via empirical risk minimization (ERM) are known to rely spu- rious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust opti- mization (G-DRO) can alleviate this problem by minimizing the worst-case loss over a set of pre-defined groups over training data. G-DRO successfully improves performance of the worst-group, where the correlation does not hold. However, G-DRO assumes that the spurious correlations and associated worst groups are known in advance, making it challenging to apply it to new tasks with potentially multiple unknown spurious correlations. We propose discovery for Distributionally Robust Optimization-an end-to-end approach that jointly identifies error groups and improves accuracy on them. AGRO equips G-DRO with an adversarial slicing model to find group assignment for training examples which maximizes worst-case loss over the discovered groups. On the WILDS benchmark, AGRO results in 8% higher model performance on average on known worst-gr poosts,roups, compared to prior group discovery approaches used with G-DRO. AGRO also improves -iit--idiitritttion performance on SST2, QQP, and MS-COCO datasets where potential spurious correlations are as yet uncharacter- ized. Human evaluation of ARGO groups shows that they contain well-defined, yet previously unstudied spurious correlations that lead to model errors.
Adveesarraai Group
INTRODUCTION
Neural models trained using the empirical risk minimization principle (ERM) are highly accurate on average; yet they consistently fail on rare atypical examples that are unlike the training data. Such models may end up relying on spurious correlations (between labels and ieeeinndeenndet features), which may reduce empirical loss the training data but do not hold outside the training distribution (Koh et al. 2021 Hashimoto et al. 2018). Figure Ishwss examples of such correlations in the MultiNL. and C CelebA datasets. Building models that gracefully handle degradation under distributional shifts is important for robust optimization, domain generalization, and fairness (Lahoti al. 2020 Madry et al. 2017). When the correlations are known and training data can be partitioned into dominant and rare groups, group distributionally robust optimization (G-DRO. [Sagawa et al. 2019) can efficiently minimize the worst (highest) expected loss over groups and improve performance on the rare group. A key limitation of G-DRO is the need for pre-defined partitioning of training data based on a known spurious correlation; but such correlations may be unknown, protected or expensive to obtain. In this paper, we present AGRO Adversarial Group discovery for Distributional Robust Optimization- an end-to-end unsupervised optimization technique that jointly learns to find error-prone training groups and minimize expected loss on them.
Prior work on group discovery limits the space of discoverable groups for tractability. For exam- ple, [uu et al. (2022) use prior knowledge about the task to find simple correlations e.g. presence of negation in the text is correlated with the contradiction label (Figure I]. However, such task-specific approaches do not generalize tasks with different and/or unknown (types of) spurious correlations.
* Work done during internship at Allen Institute of Artificial Intelligence. Correspondence to: Bhargavi Paranjape <bparan www.@eww......c.c.ce..e was.wainngooon.edu

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Published as a conference paper at ICLR 2023
Dominant groups
Known worst-groups
Groups discovered by AGRO
sungfassse
CelebA
Women
MuitiNLT
(+5 The cconomy could still better. never been betber
(P) There was wilntu memment
(P)/Mariuus enegy-efflcient menoures have saved taxoayers nearl y $1.
much more sight. (M1)tru, are lacated the southhoee of very impressioe sight.
a
Contradition Negation
Ensailment Negation
Modifter "only" Neutral
Artoynmm wi Entalimant
Figure I: Groups discovered by AGRO on CelebA image classification dataset and MultiNLJ sentence pair classification dataset.
Approaches using generalizable features are semi-supervised (Sohoni et al. 2020 Liu et al. 2021) in that they assume access to group information for group assignments is costly and can lead to cascading pipeline errors. In contrast, AGRO is completely unsupervised and end-to-end while making no assumptions about the nature of the task and availability of additional supervision.
held-out dataset. However, obtaining supervision
To address these challenges in AGRO, we construct new parameterized grouper model that produces a soft distribution over groups for every example in the training data and is jointly trained with the task model. We introduce two key contributions to train this model. Firstly, the grouper model does not make task-specific assumptions about its inputs. Instead, it relies on computationally extracted features from the ERM model including: (a) predictions and mistakes of the ERM model on training and validation instances, and (b) pretrained dataset-agnostic representations and representations fine-tuned on the task data. Secondly, AGRO jointly optimizes the task model and grouper model. We formulate zero-sum game between the grouper model that assigns instances groups and the robust model which seeks to minimize the worst expected loss over the set of inferred groups Specifically, while G-DRO optimizes the robust model to minimize the worst group-loss the grouper model adversarially seeks a probabilistic group assignment such that the worst group-loss is maximized.
On four datasets in the WILDS benchmark (Koh al. 2021) (MultiNLII CivilComments, CelebA, and Waterbirds), AGRO simultaneously improves performance on multiple worst-groupp] corresponding previously characterized spurious correlations, compared to ERM and G-DRO with known group assignment. AGRO also improves worst-group performance over prior approaches that find spurious correlations and groups by 8% on average, establishing a new SOTA for such methods on two of the WILDS datasets. On natural language inference, sentiment analyses, paraphrase detection and common-object classification (COCO), AGRO improves robustness to uncharacterized distributional shifts compared to prior approaches, demonstrated by gains in out-of-distribution datasets for these tasks. Ablations on different parts of the framework underscore the need for a generalizable feature space and end-to-end optimization. We develop novel annotation task for humans to analyze the discovered AGRO groups distinguishing group members from random examples and perturbing them to potentially change model predictions. We find that humans identify existing and previously unknown features in AGRO groups that lead to systematic model errors and are potentially spurious, such the correlation between antonyms and contradiction in MultiNLI, or the correlation between hats, sunglasses and short hair with non-blondes in CelebA. Our code and models are public")
RELATED WORK
Group distributionally robust optimization GDDRO is a popular variant of distributionally robust optimization (DRO) (Ben-Tal Tal et al. 2013) Duchi et al. 2016) that optimizes for robustness over various types of sub-population (group) shifts: label-shifts (Hu et al., 2018), shift in data-sources or domains (Oren et al. 2019), or test-time distributional shifts due to spurious correlations in training
Largest group-wise loss in minibatch
? A group of examples where the spurious correlation between feature and label does not hold. https://// / thub. com/ /bha wtwp://aaargaappaaaaaaa...ep
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data (Sagawa al. 2019). Santurkar al. (2020); Koh et al. (2021) introduce benchmark for robustness to group shifts for measuring progress in this space. There has been significant recent progress this benchmark using optimization techniques apart from G-DRO- Zhang & Ré (2022);
Zhang et al. 2 Piratla et al. (202T); Kirichenko et al. (022). However, all these techniques assume that groups environments are available.
Recent work that focuses on group discovery include: (a) GEORGE
(Shooni et al.)
2020), which
clusters ERM representations to discover groups, (b) JTT (Liu al.
2021) and LiF (Nam al.)
2020), which use misclassified classes as groups, Nam et al. (2022) use annotations for spurious features on a small subset to build learned group identifier, and (d) EIIL (Creager et al. 2021 and
PGI (Ahmed et al. (2200), which infer groups that maximally violate the invariant risk minimization
(IRM) principle (Arjovsky et al., 2019). EIlI. is especially relevant to our work: it adversarially maximizes the IRM objective to discover groups. However, the approach is currently limited to two-class problems, and moreover, is challenging to apply for large datasets of the kind we study in this work. Importantly, all these techniques are at least partially supervised, memberships are available on a validation set for hyperparameter tuning or model selection. This is erucial resource, since ERM-optimized model with access such set is also competitive baseline (Gulrajani & Lopez- Paz, 2020). Another closely related work on distributional robustness for group fairness is ARL (Lahoti al. 2020), which adversarially trains a network to find instance weights for re-weighting of training examples. One of the core difference between AGRO and ARL the instance-
they assume group
group-based re-weighting, Model errors have been found be systematic, i.c.. occurring in groups (Oakden- Rayner et al. 2020). ARL precludes post-hoc analysis of such patterns due to instance-level modeling.
Slice Discovery Methods (SDMs) A related problem to robust optimization is automatic slice discovery idDiitentifying underperforming data slices for error analysis of deep-learning models. Prior work on SD has used data with tables and metadata (Polyzotis et al. 2019)) Sagadeeva & BBoehm 2021]. SDMs on unstructured data (Kim et al. 20199] Singla et al. 2021 Eon et al. (022) rely on embedding inputs, dimension reduction, and clustering. Recently, Eyuboglu et al. (2022) propose
DOMINO, an error-aware mixture-mode based clustering of representations on the evaluation set. While SDMs can help determine patterns in model failures and making them human-comppehhnnibble.
they have not been applied for distributional robustness to group (slice) shifts.
3 METHOD
3.1 PRELIMINARIES
Problem Setup We consider the typical image/text classification problem of predicting labels E )/ from input x E X. Training data ID is drawn from the joint distribution P(X,)") In state-of-the-art classification models, inputs are typically encoded using multi-layered pretrained transformers (Vaswani et al., 2017: ; He et al., 2021: Bao et al. 2021) We use g(x) and h(z)) to represent input encoding before and after fine-tuning these encoders on D.
ERM principle Given a model family θ and a loss function ( θ x X' x y - R4 empirical risk minimization aims to find model 0 E O that minimizes empirical loss over data drawn i.i.d from yı 0)]. While ERM models achieve high accuracy i..d. evaluation data on average, they often underperform when the test distribution shifts significantly from the training distribution (Madry et al. (017). ERM also underperforms on a biased sample of the test set where spurious correlation is absent (Koh et al. 2021)
the empirical distribution P. FFRM == argmingge argminggeBkke.))->P
G-DRO for Robust optimization Group-DRO (Sagawa et al. 2019) is a category of distributionally robust optimization (DRO, Duchi et al., 2016), where training distribution P is assumed be mixture of m groups, and each training point (z, y) comes from one group g E g. G-DRO minimizes the empirical worst-group risk R()) i.c. worst (highest) empirical loss over m groups:
Ò == arg min (2(0) max ((s))))(( [xx y 0)] leB
gEğ
where p(z. vlg)(gg is the empirical distribution of training data over groups With sufficient regular- ization over 6, G-DRO enables training models that are robust to test-time worst-group distributional shifts. In practice prior work adopts the efficient and stable online greedy algorithm (Oren et al. 2019)
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to update 9. Specifically, worst-group risk R(Ø) == max (x.)),((. (z. y: 0)]. The un- certainty set Q are categorical group distributions that are a-covered by the empirical distribution over groups Ptraén Le. Ptrain (g) := [g, g]vi DI/ID| and Q {q : ((9) S Ptrain (g)ain(g/aa Vg). Effectively, this algorithm up-weights the sample losses by 1 which belong to the a-fraction of groups that have the worst (highest) losses, where E (0, 1). Details of this algorithm pre- sented in Appendix A.2 G-DRO is limited to scenarios where both spurious correlations and group memberships are known, which is often not the case.
Algorithm I: Online greddy algorithm for AGRO
Data: o, m: Number of groups. Minimum w and maximum W' weights on group loss; EMA: Expected moving avorage for 9.2. R do
Initialize historical average group losses f. 1 historical estimate of group probabilities gtrainlo);
for 1,.,, Ti do
D For group g 1. 1... m. (g) + EMA(_ -BB (g)
Pily ((x,, ye; 00 (1,
grratu(t) (g) c EMA p(t)
1))
b Son g'rasa(t) in order of decreasing _ lop a fruction groups in A (g) if E A che un;
P Update model parameters :(() g(t-1) > (g) E!_! 1, (1r Ue, 0(1-11))
end for
I..... T2 do For group g
p(t plols pl 1), (g) ا pi P1le M6: 0(0), ((t-1)
p (rain(t)) (g) < Σ |B|
Sort p (rain(()) order decrensing (0) lop a fractioon groups in A (g) a g else
b Update model parameters di gf9)) ((-1)) MA 19 (g) I! r:l-- ((x,, ve. ((0)))
end
end
Error slice discovery DOMINO (Eyuboglu et al. 2022) shows that systematic mistakes made by models due to reliance on spurious correlations can be exposed via clustering of the model's representations X. predictions Y and reference labels Y. DOMINO learns an error-awart aware mixture model over Y.Y. via expectation maximization, finding m error clusters over the evaluation potentially be used for group assignment since the examples in cluster are coherent (i.e. united by a human-une uaman-understandablle concept and model prediction) and suggestive of specific feature that the model exploits for its predictions. However, overparameterized neural models often perfectly fit the training data, resulting in zero errors (Zhang al. 2021 i.e. Y = Y.
set. Such a clustering
3.2 AGRO: ADVERSARIAL DISCOVERY OF GROUP FOR ROBUST OPTIMIZATION
AGRO combines insights from group distributionally robust optimization and error slice discovery, introducing novel end-to-end framework accomplish both objectives. We formalize the problem of group discovery for robustness, where g is an un-observed latent variable to be inferred during the training process. We replace discrete group memberships 1 (g; g);g G with soft-group assignment, i.c. a probability distribution over groups Pi,, := p(g) fi; o):g E G. P.., refers to the probability that the i-th example belongs to the g-th group and realized by a neural network q with parameters é. This enables modeling co-occurring spurious features and overlapping groups The input to the grouper model q is a dite--iieenionall feature vector fi E that can potentially encode the presence of spurious correlations in example i (described in section 3.3).
AGRO jointly trains the parameters of the robust task model, 0, and the grouper model, Φ, by setting up zero-sum game between the robust model and the grouper model. The optimization occurs over R alternating game rounds-thh the primary round that updates the task model and the adversary round that updates the grouper parameters ø. Parameters that are not optimized in a given round are frozen. In the primary round, the optimizer seeks to minimize the worst-group loss (exactly the G- DRO objective), while in the adversary round, it seeks to find group assignment that maximizes the worst-group loss. In doing so, the adversary finds a group assignment that is maximally informative for the primary, since produces loss landscape over groups that is highly uneven. This forces the primary model, in the next round, to aggressively optimize the worst empirical loss to even out the landscape. With Ptrain (g) : EiED Pt.g/ [TD] and uncertainty set Q is defined before, the AGRO optimization objective is:
0, AAGRO == argmingge a (argmax se (R(0)= = max (ew.. (())))
(1)
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Primary Round In round r, the primary classifier finds the best parameters @ that minimize the worst-group loss based on the current dynamic group assigmments provided by Φ in round r y- 1. Updates to I are similar to the online greedy updates used by Oren et al. (2019) i.c. up-weight the loss of a fraction of groups with the highest loss, then minimize this weighted loss.
Adversary Round In round r ++1 1, the adversarial grouper updates & and learns soft assignment of groups that maximizes the loss of the worst-group (highest loss group). In practice, we adopt the converse of the greedy updates made in the primary round, i.e. down-weight the loss of a fraction of groups with the highest loss, and then maximize this weighted loss.
For stable optimization, we iterate over Ti minibatches of training data update 0 in the n4t round and iterate over Z/2 minibatches to update & in the 1+1 round. Algorithm presents the pseudo-code for AGRO. Implementation details, along with hyper-parameter values for 2i, T2, a, m and R, are described in Appendix A In the first primary round, we start with random group assignment (i.e. random initialization for s), which amounts to training an ERM model. @ initialized with a pretrained mansformer-basee encoder and MLP classifier. In the first adversary round, we adopt different initialization for Φ. which explained in Section 4 The grouper model & takes as input a feature vector Sa E F for each training example Tyg. Next, we describe our choice of F.
3.3 FEATURES FOR GROUP DISCOVERY
Most prior work uses some knowledge about the task (Wu et al., 2022 Gururangan et al. 2018). data collection process (Poliak et al., 2018) or metadata information (Koh al. 2021 1) for group discovery. Instead, we develop an unsupervised end-to-end group discovery method that does not rely on any task-specific knowledge, We do using general purpose features fi JF that can potentially indicate the presence or absence of a spurious correlations.
ERM features and errors We use features from weaker ERM models (i.e., under-trained, smaller capacity ones), which have also been used in prior work for automatic group discovery Creager et al. (202T) Sohoni al. (2020). DOMINO (Eyuboglu et al. 2022) additionally clusters model represen- tations and errors to generate coherent error slices on held-out data. However, overparameterized models often perfectly fit training data. To estimate model errors on training data, we apply K-fold cross-validation to get model predictions on held-out training instances in every fold. Specifically, for a training example I; assigned to the k-th fold's held-out set, we compute the following features: the model's 's fine-tuned representations h((z)), the reference label y/, and the vector of prediction probabilities over labels [p(ū. 1 V%) E }}. where 8₂ is the fold-specific ERM classifier.
Pretrained features Recent work has shown that large tranffommr-baseed models trained web-scalt data learn general features for images which improve on out-tf-distribution data (Wortsman et al.) 2022a b). Pretrained models are less likely encode dataset-specifie features in their representations. Following this observation, we also include pretrained representations ((z:) of the input z;. In sum, the group discovery features f, for training example x; are: the representations g(3;) and M(xs). the label y; and the probabilities (p(%) E y/}.
EXPERIMENTAL SETUP
Datasets with known spurious correlations We evaluate performance on four datasets in language and vision from the popular WILDS benchmark (Koh et al. (02I) to study in-the-wild distribution shifts. We select tasks that contain more than one known worst- porst-group and report on the top three known worst-groups (referred to as KWGs) from these datasets, chosen based on the rate of correlation between feature and label. Table T briefly describes these known worst-groups by dataset. More details about tasks descriptions, statistics, and pre-processing can be found in Appendix A.
Datasets without known spurious correlations Our main contribution is a fully unsupervised end-to-end technique for group discovery and robustness, which can be applied off-the-shelf on a new dataset with no additional annotation. To evaluate this, we train AGRO for robustness to spurious correlations in datasets with possibly unknown biases and evaluate its generalization on OOD datasets, where such correlations may not hold. We train models on two datasets from the GLUE benchmark (Wang et al.) 2018)) Stanford Sentiment analysis (SST2) and Quora question paraphrase detection (QQP), and a modified version of image classification on the MS-COCO dataset (Lin al. 2014).
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Dutaser
Inputs
Labels
Worst Groups
MultNLI
Sentences
Essail, Contradici, Neutral
KWGI: Negation
Negation Contradict, KWG2; Lesical overlap Entil
Waturbirds
Image
Waterbird. Landbird
KWG1: Waterbird w/ land buckground. KWG2: Landbird w/ water buckgrounds
CelebA
Image
Blonde, Non Blende
KWGI: Male Blondes, KWG2: Blondes wearing
KWG3: Blondes
Comments
Comment
Toxic, Non
KWGI: Toxic comment mentioning black, KWG2: LGBTQ
religions
Table 1: Inputs, labels and known worst-groups in WILDS datasets.
COCO-MOD.
We evaluate on out-of-distribution (OOD) datasets for these tasks, like IMDb (Maas al. 2011 for SST2, PAWS (Zhang et al. 2019) for QQP, and SpatialSense (Yang et al. 2019 9) for
MS-COCO.
Hyperparameters For 0, we use transformer-based (Vaswani al. 2017) pretrained models that
have been shown to be robust to rare correlations (Tu et al.] 2020). including DeBERTa-B Base (He 2020) for text classification and BEIT (Bao al. (021)) for image classification. á is an 2-layer
al.
MLP with softmax actívation layer of size m (the number of groups). Appendix [A.4 and [A.6.3)
further detail hyperparameter tuning, particularly for AGRO-specific hyperparameters ar and m.
Initializing Φ A random initialization to grouper á can result in degenerate group assignment, where all examples are assigned the same group. Such an assignment still results a small proportion of groups (one group) having the maximum loss. This reduces the ERM solution. To mitigate this behavior, we initialize the grouper model's parameters in the first adversary round to predict the same group memberships the clusters from DOMINO (Eyuboglu et al., 2022), which was described in Section Our ablation results in Section 6 show that AGRO improves over directly using DOMINO slices as groups for G-DRO. Details about the pretraining of are in Appendix A.2
Model Selection Unlike prior work on automatic group discovery (e.g. Liu et al. 2021 Creager et al. 202T), do not assume access group annotations the validation set based correlation to tune hyperparameters or to choose the best model checkpoints. We develop new strategy for model selection based on the learned grouper model Φb We select the best model based on the combined performance of a-fraction of the worst-performing groups discovered by AGRO, For fair comparison with the baselines described below, we similarly use the worst- worst-group available to each baseline. In Section 6 we relax this assumption for all methods (AGRO and baselines) and use KWGI of each dataset for model selection.
known spurious
Baselines We compare against previous automatic group discovery approaches used with G-DRO:
JTT: Liu et al. (02T)) retrain on training data errors made by a weaker ERM model. Model selection is based on errors made on the validation set.
GEORGE: (Shooi al. (2020) cluster the training and validation sets based on ERM features and loss components. The worst-group the validation set is used for model selection.
EIIL: Creager et al. (2021) propose to train a group discovery model to adversarially maximize the invariant risk minimization objective. Upon examination of their experimental details, we find that for all large datasets (that we report on), they use labels as groups, since EIIL is hard to scale. We follow the same procedure to compare EIIL with AGRO. For small datasets Waterbirds and SST2, we use EIIL as is. The class with the worst validation performance is used for model selection.
G-DRO: Following (Sagawa et al. 2019), optimize G-DRO based group assignment for known spurious correlation, and use the same assignment on the validation set for model selection. G-DRO serves upper bound the worst-group performance for this spurious correlation. Specifically, we use KWGI to run G-DRO on all datasets.
ERM: ERM models trained for longer and with larger batch size already improve worst group performance (see Appendix A.6), making for a strong baseline.
5
RESULTS
5.1 AGRO IMPROVES ROBUSTNESS TO MULTIPLE SPURIOUS CORRELATIONS
AGRO learns a soft group assignment over training data, with the goal of improving worst- worst-group accuracy over multiple co-occurring correlations. We evaluate this hypothesis the selected datasets

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Published as a conference paper at ICLR 2023
Group Accuracy
ERM
G-DRO
JTT
GEORGE
EML
AGRO
MultiNLI
Full evaluation set
87.0(.0)
84,80.55
79.6(1.2)
58,4(.99
86.4(.7
85.31(.5)
KWGL.Nogation-Coo
75.8(.09)
84,2(.9)
71.9(20)
54.3(0.7)
76.1(1)
82.29((23)
KWG2:Lexical Overlap-Ent.
45.5(.05)
43.1(1.0)
48.2(1.4)
21.0(1.)
47.00.55)
51.8(95)
Waterbirds
Full evaluation set LWGI:WaterbrrssLLand
97.75(1.)
96.3(.9
96.4(1.3)
97.8(.8)
96.5(1.)
97.7(1.1)
90.2(.7)
97.7(.8)
83.5(2.0)
96.2(1.0)
78.8(8)
96.2(1.2)
KWG2:Landbrrds Water
97.4(.8)
93.4(1.2)
96.5(1.5)
95.9(1.4)
84.6(.5)
96.1(1.0)
CelebA
Full evaluation set KWGI-Males-Blonde
95.5(.4)
94.0(1.3)
92.9(1.0)
94.7(1.5)
94.50.91)
95.59(1.)
39.6(.23)
86.8(1.9)
75.8(.8)
57.7(3.0)
55.5(.5)
69.2(1.2)
WVG2:EEyelaasse-BBontt
53.8(888)
82.7(.8)
82.7(1.6)
61,5(2.5)
78.8(.)
82.5(.99)
KWG3: Big Kose-Blonde
69.4(.78)
92.5(.7)
88.9(.67)
77.82.1)
82.1(.81)
87.10(1.6
Civilcomments
Full evaluation set KWGI:Black- Toxic -
92.9(.4)
82.0(1.6)
73.9(1.0)
90.1(.08)
88.0(.01)
91.2(1.9)
58.3(.55)
71.7(1.7)
40.7(2.3)
$2.80.9)
88.7(.88)
72.0(.))
53.1(.31)
83,8(81)
43.6(3.1)
64.2(L))
78.4(.92)
66.2(1.5)
KWG3: Other Religions-Toxic
53.1(.67)
82.10.91)
34.0(1.8)
60.4(41)
84.4(.6)
66.7(.8)
Table 2: Average and WG performance on WILDS Aatasets.AGRO is better at multiple worse groups
and also consistently improves over ERM, unlike prior work. Best KWG performance bolded and second- best underlined. (+) indicates variance across 3 random seed runs.
MultiNII
SST2
Dataset
ERM
GEORG.
EIIL
AGRO
Dataset
ERM
GEORG.
EIIL
AGRO
PI
80.27
78.27
81.53
81.52
SST2
92.90
87.10
66.39
92.01
LI
88.45
81.84
87.88
89.24
Senti 140
88.58
83.67
53.99
85.16
ST
76.75
71.21
75.93
76.29
SemEval
83.90
89.1
72.14
88.58
HANS
74,58
66.83
67.06
73.12
Yelp
91.60
91.9
84.05
92.01
WaNLI
60,82
55.94
59.86
63.12
ImDB
85.97
85.74
64.50
86.78
SNLI
83.21
80.75
83.00
84.78
Contrast
85.25
83.60
56.76
86.68
ANLI(33)
30.17
31.92
29.00
34.92
CAD
90.78
89.95
58.20
90.37
Avg%
-4.68
-2.27
3.04
-1.20
-26.30
0.50
QQP GEORG.
COCO-mod GEORG.
Dataset
ERM
EMI.
AGRO
Dataset
ERM
EMI
AGRO
QQP PAWS
91.17
88.24 30.87
82.73
91.58
COCO-mod
89.14
87.52 87.25
79.28
89.26
38.08
43.72
41.8
Spatial
89.89
64.4
91.33
Table 3: Accuracy on out-of ditt----iisiitttinn datasets (columns) for 4 tasks with unknown spurious correlations. AGRO improves over ERM by .5-10%, while baselines underperform.
from WILDS (Table Table reports accuracy on the full evaluation and on multiple worst- groups. Additional results using weaker pretrained models are in the Appendix A.6
Across datasets, AGRO improves worst-group accuracy by 25.5% average over the ERM baseline without hurting the overall task accuracy. In particular, it improves across multiple known worst- groups for each dataset. On all datasets except CelebA, for KWGI, AGRO gets within 2% of the accuracy obtained by G-DRO with ground truth groups. This shows that ARGO's unsupervised group discovery and robust optimization performs nearly as well as using the best manually designed groups. No other baseline consistently improves worst-group accuracy GEORGE, JTT and EIIL have mixed results across datasets. Their performance depends on specific properties of the test datasets, such high spurious correlation ratio (in CelebA) severe label imbalance (in Civilcomments). These results indicate that AGRO is most suitable for new datasets whose key properties unknown. Section 5.2 explores this further.
all tasks. Approaches like
On MultiNLI, DeBERTa-basee models achieve the best reported worst-group performance by both the ERM model and the G-DRO model (trained using KWG1 assignments). The relative gap between

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worst and average accuracy persists with weaker model like RoBERTa (see Appendix A.6 AGRO has an 8% improvement over the next highest baseline on this task (EIIL), GEORGE, which uses loss components for clustering groups and for model selection, underperforms even in-domain data, potentially from over-fitting to groups with label noise. The worst group performance of AGRO establishes new state of the art for group robust optimization approaches that also find groups. On Waterbirds, the ERM model finetuned using BEIT has competitive worst-group accuracy, surpassing the performance gap of a weaker model like ViT Appendix A.6)). We hypothesize that stronger pretrained model is generally less susceptible to spurious lationstions (Wortsman et al. 2022a). On both worst groups, AGRO outperforms ERM and has comparable performance with the next best baseline GEORGE, once again achieving SOTA.
CelebA and Civilcomments have overlapping groups i.e. images with multiple facial attributes and comments with mentions of multiple demography labels, respectively. Consequently, the performance of G-DRO with ground-truth groups for KWG1 also improves performance on other worst groups. On the Male-Blonde group in CelebA, AGRO outperforms the ERM model by 75%. It also outperforms GEORGE and EIIL on this group. Owing to a high rate of spurious correlation in the dataset (male:non-male blondes ratio is 0.02), re- weighting misclassified points (JTT) proves to be an effective grouping mechanism. Civilcomments has a significant label imbalance (a ratio of 0.13 for toxic non-toxic, the lowest for all datasets considered in this work). Consequently. using the label-as-group heuristic (EIII. performs the best among other baselines, followed by AGRO.
5.2 AGRO IMPROVES ROBUSTNESS TO DISTRIBUTION SHIFTS ON NEW TASKS
Since AGRO is fully unsupervised, it can be used off the shelf to improve distributional robustness on a new task. To evaluate this, we apply AGRO to SST2, QQP, and COCO-MOD, datasets where spurious correlations and worst-groups are unknown. Models trained on these datasets are tested on out-of-distribution (OOD) datasets, where spurious correlations learned in-distribution may not hold. we report average and duitt--distribution accuracies achieved by AGRO and two best performing baselines, GEORGE and EIIL. We also report OOD performance for MultiNLI.
In Table 3
striking observation across tasks and datasets is that baselines generally underperform ERM on general
robustness. We hypothesize that this may be because of over-fitting to noisy groups with high losses. G-DRO can be effective at improving OOD robustness if distributional shifts in the groups correspond to distributional shifts at test time. We hypothesize that the baselines overfit to groups in the in in-domain data in unexpected ways due to their simplified grouping heuristics, resulting in poor OOD generalization. On MultiNLI. AGRO outperforms ERM by 3.0% on average across OOD datasets. On COCO-MOD, AGRO outperforms ERM by 2% on the OOD dataset. On SST2, AGRO improves performance over ERM most datasets by 0.5-5.5% except Senti140 (Twitter sentiment classification dataset). On QQP, AGRO outperforms other baselines, including ERM. The JTT baseline on QQP gives the best results on -oltt-ddistriuttion data, but the cost of in-domain performance. We hypothesize that AGRO's OOD gains stem from reduced reliance on dataset-specific correlations.
ANALYSIS
We present detailed analysis to determine which aspects of AGRO contribute the most to its strong unsupervised performance and do qualitative study of AGRO groups.
Feature Ablation Table 4 (right) reports the impact of adding different features used in AGRO on the MultiNLI dataset. We find that using error-aware clustering of features (i.e. DOMINO in Row 3) as group assignments improves gorst-group performance compared to clustering based on features alone (i.e. only using f((r;)). Using adversarial group discovery along with these features especially boosts performance on the worst group, underscoring the importance of our end-to-end framework. Finally, adding pretrained features to AGRO gives us the best performance on MultiNLL. Ablation results for Waterbirds are in Appendix [A.6.4
Model Selection Table (left) reports the results using knowledge of worst group during evaluation for model selection. We report worst- worst-group group performance(of KWGI) of AGRO and all baselines for MultiNLI by selecting model checkpoints based on that worst group. The worst-group performance of all baselines increases. For AGRO, our selection strategy from Section iis as effective as using the known worst group for selection (the results remain unchanged). AGRO and the baselines EIIL. and
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Algorithm
AvgAcc
KnownWG
Features
AvgAcc
KnownWG
ERM
86.63
77.96 (+2.9%)
ERM
86.28
74.01
JTT
81.87
74.27 (+3.3%)
Feature clustering
84.15
74.14
GEORGE
85.44
76.03 (+40.0%)
DOMINO clustering
85.65
76.82
EIIL
86.38
77.45 (1.8%)
AGRO
85.39
79.24
AGRO
85.31
82.29 (Same)
AGRO + pretrained features
85.97
80.64
G-DRO
84.81
84.20 (Same)
Table 4: Improvement in accuracy on WGI of MNLI when it is used for model selection (left).
Ablation of features in F and ablation for adversarial learning over clustering (right).
WG1: Qualifiers like "all, only, solely" with Neutral class.
P: Chapter 5 focuses on synergistic combinations control retrofits on a single unit.
H:Chapter 5 focuses selely on synergy (Neutral + Entail)
P:but that that's my that's 's probably my main hobby that sewing and reading books that's about it
HeSeennng and reading books
my enly hobbies. (N -> E)
PuNuns are in their habits and Israeli women in military uniforms.
H:Al| Isracli women are in the military, which is why they wear military uniforms. (N - E)
WG2: Antonyms P and with Contradiction class.
Stand behind
H: Stand so that is in front behind you. (Contradict + Entail)
P: Look for the Cecil Hotel on the western end of the square.
H: You can see the Cecil Hotel from the eentre west of the square. (C -+ F)
P: Get there early in the morning before the crowds arrive
H: You should get there shortly after before they open to beat the crowds. (C ->
Table 5: Perturbation patterns on worst groups discovered in AGRO, Class flips in ()
GEORGE have competitive worst- gorst-group accuracies. These observations hold on the Waterbirds data well (A.6). Our selection strategy can potentially also be used with other group robust optimization techniques, which we will explore future work.
Small-scale human study Appendix A.8 has examples of slices discovered by AGRO several datasets. The core goal of AGRO is to discover error-prone groups without prior knowledge of spurious correlations. Human expertise is needed ascertain whether model exploits any spurious correlations in the discovered slices (Eisenstein 2022). Soliciting human feedback is challenging since models may exploit spurious correlations that do not correspond to interpeettere or identifiable features. We design two annotation tasks to obtain feedback indirectly. The first annotation task evaluates coherency. Annotators are shown four instances from a group identified by AGRO, along with two other test instances, only one of which is from the same group. The annotators are asked to identify the test instance that belongs to the group. The second annotation task follows the first, where the annotators are asked to perturb the instance chosen to be part of the AGRO group in such a way that the task model'ss 's prediction would change. This exercise can implicitly surface the common feature humans think exists in the slice. We conduct the human study on 5 slices each from MNLI, SST2 and CelebA. More details of the human annotation setup can be found in Appendix A.7 For Task we find that inter-annotator agreement has score of 0.7823 Fleiss Kappa. Humans were able to identify the correct group member 70. % of the time. Some examples of the flips provided by human annotators in Task 2 are presented in Table
CONCLUSION
We studied the challenge of applying G-DRO to new tasks with multiple, unknown spurious corre- lations and groups and present Optimization an end-to-end approach that jointly identifies error- arror-poone groups and improves model accuracy these groups. AGRO's adversarially trained grouper model finds soft group assignment that is maximally informative for G-DRO. Compared to prior work, AGRO is a more effective off the shelf optimization technique for aully-ensupervised group discovery and robustness
Group discovery for Distributionally Robust
tasks.

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A APPENDIX
A.1 ADDITIONAL RELATED WORK
Other Robust Optimization techniques Other approaches for robustness to group distributional
shifts include data augmentation and ensemble learning. (Liu et al. [022]] Wu al.
2022) (for
MultiNLI) and (Plumb et al., 2021) (for COCO) use data augmentation. However, they make task-specific assumptions about the types of features that can be spurious generate additional data. Product-of-expertss (PoeeertssPo)---eee PoE)dbased (Hinton 2002)approaches like (Sanh et al., 2020; Mahabadi al. 2019; He et al., 2019) use biased model to construct robust ensemble. The biased model is typically constructed using task-specific information. Since Since AGRO finds error-prone groups that potentially encode model biases, these groups can be used for data-augmentation or PoE approaches in future work.
A.2 ALGORITHMS AND IMPLEMENTATION DETAILS
G-DRO Algorithm 2 presents the online greedy algorithm used in G-DRO. Note that group
assignment over training data is based on predefined spurious correlation. EMA refers to exponential weighted moving average. i.e. EM A(v1, v2) Vvi 7) where y (0, 1) and a E (0, 1). A is the a fraction of groups with the highest loss. Specifically, based on the group proportions (t)
Ptrain- A consists of those groups with high loss values that make up a-fraction of the dataset. The greedy up-weighting scheme is based on the -tyle implementation of G-DRO from Zhou et al. (2021) and (Sagawa et al. (2019). They seek optimize:
PG-DRO == arg min m == max E (oa (22, y; 0)]} q
#E0
The uncertainty set Q are group distributions that are a-covered by Ptrain. which is the empirical distribution of groups in training data. Specifically, Ptrain (g) is the proportion of training examples that belong to group
Ptrain (g) = (1: = g)vi E DJ/[D|
and
= :q:: S Ptrain (g) / f4A
In their code, Zhou et al. 2021) recognize that Ptrain (g) can't 't be exactly computed since optimization occurs in mini- bani-batches. [[g; = g]vi E D is therefor estimated based on the number of examples of group in minibatch (of size B). In order to have unbiased estimate for Ptrain (g) they maintain expected moving average ptrain (g). Thus,
Perain (g) + EMA([1 = g) E Pirasin (9))
In implementation, the optimization up-weights the sample losses by which belong to the a-fraction of groups that have the worst (highest) losses, where a E (0, 1). This algorithm is presented in Appendix A.2 All the other groups are down-weighted by a minimum group weight, w. We set w = 0.1 following prior work. We tune a based on known worst-group accuracy on each dataset.
DOMINO (Eyuboglu et al. 2022) propose mixture model that jointly models input embeddings. class labels, and model predictions. This encourages slices that are homogeneous with respect to error type (e.g. all false positives), while also being coherent with respect to human-interpretablee features. The model assumes that data is generated according to the following generative process: each example is randomly assigned membership to a single slice among & possible slices according to categorical distribution S ~ Cat(ps) with parameters ps E (p E R4 Dte
List = 1}. Given membership in slice j. the embeddings are distributed normally Z (() = 1 NN(ul) ΣO) with parameter mean (ج E dRd and covariance m00 E Sd (the set of symmetric positive definite d x d matrices), the labels vary as categorical Y
Cat(P() with parameter p(j) E {p R Ei = 1}. and the model predictions also vary as a categorical Y|SU) = ~ Cat(Pu)) with parameter p(j) e (p E R : Ei= P. = 1}. The embedding, label, and prediction are all independent conditioned on the slice.
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Representations
Fold Errors, Learned Representations
Training data
ERM Model
Wx
Fg
softmax
Pretrained Model
ERM Model
softmax
he
Pp
y = hg (X)
=Po(x, y.
ERM Model
Loss(0.
Loss(o,
Robust Model
Adversarial Grouper
Figure 2: Adversarial Group discovery for Distributionally Robust Optimization (AGRO) framework
The mixture model is parameterized by Φ [ps {u Σ P() Pu)l The log- log-likeldhodd over the validation dataset D, is given as follows and maximized using expectation-mant maximizaaion:
((0) = _ log Σ P(S 1)P(Z 1
1)) ho -/ 1)7
w1
where is hyperparameter that balances the trade-off between coherence and under-performance and (x;) the class logits predicted by the ERM model on example zi. y set I for all the datasets considered in this work.
AGRO
The AGRO optimization objective is as follows:
0, ò AGRO arg max min (R( := max (e y 0)}}} (e))
(3)
Φ
#E
The uncertainty set Q are group distributions that are a-covered by Ptrain similar to G-DRO. However, Ptrain (g) the proportion of training examples that belong to group g.
Q = {q ((g) & Ptrain (g)/a Vg}
In the case of AGRO, Ptrain (g) is computed based on group assignments probabilities output by the adversary model & trained in the previous round. Specifically, for minibatch
" |B]
Perain (g) < EMA
PPerain (g))
where pfgl,f,, ((t-1)) is the probability of example j in the minibatch belonging to group g. which is estimated by the adversary model ó.
Primary Round optimize 0, we up- up-weights the mini-batch sample losses by which belong to the a- a-fraction of groups that have the worst (highest) losses, where E 1). Afl the other groups are down-weighted by minimum group weight, w. set ur 0.1 following prior work. We tune based on known worst-group accuracy on each dataset.
compute probability distribution over m groups for instance i, p(()) using the grouper model the previous iteration, (tt-1), This soft group assignment is then used to compute weighted loss and soft group count for the minibatch in iteration f. As described in 3.2 in order to train the grouper model the adversary round, we reverse the greedy weighting procedure adopted in G-DRO. Therefore, if AA is the fraction of groups with the highest loss, the a-fraction of groups in A are down weighte by a, while all the other groups are up-weighted by a maximum group weight, We set W 1.0, which find works for all the datasets considered in this work.
Secondary Round
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Iterative optimization We set the number of rounds to R 1, since we found no further gains with more rounds of AGRO and high computational cost. The computational cost for further rounds is considerable since the feature space FF needs to be re-computed for the new robust model 6. The optimization process is further simplified by setting 71 and 722 to a certain number of epochs. Since we first begin with training the primary model in round O, we start with random group assignment, which basically amounts to training a weaker ERM model for I'1 epochs. After this we train the adversary for T2 epochs. After one round of training the primary and adversary model, we again train the primary model for the same number of epochs that we use to train strong ERM model baseline that we compare against. Hyperparameterss m and a are tuned for each dataset separately, which we explain in greater detail in Appendix A.4
Initializing Φ In the adversary round, the grouper model learns a soft assignment of groups that maximizes the loss of the worst-group (highest loss group). In practice, this means down- wwenhiiggn the loss of a fraction of worst-loss groups and then maximizing this weighted loss. If á is initialized randomly, the maximization results in degenerate solution aall examples are forced into one group. This solution trivially maximizes the loss over small a fraction of groups, since all examples are forced into those a groups. In order to prevent this degeneracy, we provide an initialization to that already creates an uneven loss distribution over groups. DOMINO clusters examples based on model errors, labels and representations. Consequently, they possess two properties that can a good initializing for Φ - (a) Coherence, slices that have similar features are grouper together, and (b) Homogeneous errors, slices with the same predicted and reference label are generally grouper together. Thus, we train the grouper MLP parameters & predict cluster memberships provided by DOMINO on training data (which we obtain using K-fold training). Specifically, we minimize the KL-divergence between the probability distribution over groups generated by the grouper model and the probability distribution over groups predicted by the generative mixture model trained by DOMINO. We pre-train the grouper model for 10 epochs, with a batch size of 256.
Constructing F Figure is visualization of the feature generation pipeline used to create features for the grouper model Si E FVx; E D.
Algorithun 2: Algurithm Online greedy algorithm for G-DRO Oren al. 2019))
Data: az m: No. of groups
Initialize historical average group losses L. 20));; historical estimate of group probubilities 5 grrain(0);
for I..... T do
b For group y E 1,,m;
(100 EMA(I(F;, ,,: (x) g. L ((--1((g);;
g strain(t) <- EMA samples of group in B, of gtrainctt-)) 1));
D Sort (aniprrn((()
order of decrensing f,0, sarted group indices in x:;
is the top o-fraction groups with highest loss;
of (9mz)) = g E A us
b Update model parnmetens 6;
ole) g(t-1) THe ElSI (g Allse, vc. olt-1)y
end
A.3 TRAINING AND EVALUATION DATASETS
A.3.1 WILDS BENCHMARK
We experiment with 4 datasets from the WILDS benchmarkKKh et al. (021) with multiple potential spurious correlations. Each is described in greater detail here. For more detail on each task, we refer the reader to original paper that used them for G-DRO- Sagawa et al. (201)).
MultiNLI Williams et al. (2017) task is to classify a pair of sentences as one of entailment, neutral, contradiction. We follow the train/dev/test split in (Sagawa et al., 2019), which results in 206,175 training examples. (Gururangan et al. 2018) identified that negation words in the second sentence are spuriously correlated with the contradiction labels. Accordingly, examples are grouped based on if the second sentence contains any negation word in the list (nobody, nothing, no, never). Test/validation data follows same group-class distribution train. In addition to the negation spurious correlation that is tested in the standard benchmark, we also test for the correlation between lexical overlap and the entailment label. To construct groups for this correlation, we consider token-level overlap (numher
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of common token-types between premise and hypothesis normalized by hypothesis length) 0.9 to
contain the lexical overlap heuristic. In order to measure robustness to general distributional shifts on MNLI. we also evaluate on the following uutoof-distribution datasets following (Wu et al. 2022). The NLI adversarial test benchmark Liu et al. 2020b) that tests for the following heuristics/short cuts that models may exploit: (a) Partial Inputs (PI) Heuristics Gururangan al. (2018): [ al. (2020a);
(b) Inter-sentences (IS) Heuristics, specifically the syntactic diagnostic dataset HANS McCoy et al. (2019); (c) Logical Inference Ability (LD) Glockner et al. (2018); Minervini & Riedel (2018); and (d) Stress-test (ST) Naik et al. (2018). We also evaluate on other crowdsourced NLI datasets like SNL[Bowmai et 2015), WaNL et (2022) and Adversarial NLI Nie al. (2019).
WaterBirds WaterBirds task is predict if an image contains water-bird land-bird and contains two groups: majority, minority in ratio 62: : in training data. The minority group has reverse backkround-foreground coupling compared to the majority. Test/validation data has equal proportion of each group.
CelebA CelebA Liu et al. (2018) task is to classify a portrait image of a celebrity as blonde/non- blonde, examples grouped based the gender of the portrait's subject. The training data has only 1, 387 male blonde examples compared to 200 total examples. Test/validation data follows same group-class distribution train.
Commmnossents CivilComments- WILLSWWLDDSSSS Borkan et al. (2019) task is to classify comments as toxic/non-toxe Examples come with eight demographic annotations: (white, black, LGBTQ, TQ, muslim, christian, other religions, male and female) based on if the comment mentioned terms that are related to the demographic. This leads to a significant group overlap, where the same comment will have several demography words once. Label distribution varies across demographics. In the benchmark train examples are grouped on black demographic, and testing is done on the worst group accuracy among all 16 combinations of the binary class label and eight demographics. particular, we report on the three worst groups i.c. groups with rarest co-occurrence rate between demography label and class label LGBTQ-toxic, other religions-toxxr and black-toxic.
A.3.2 OTHER DATASETS
To demonstrate the effectiveness of AGRO on new datasets where spurious correlations may be uncharacterized as of yet, we experiment with two additional text classification tasks and 1 image classification task. Since the worst-gree group based on a known spurious correlations is not known, we instead measure performance While AGRO does not target those specific group shifts and is only guaranteed to provide robustness group distributions that it finds in-domain, we believe that generally reducing reliance on spurious correlations may lead to improvements on other group shifts as well.
-utttf-distribution data which can contain unknown group- shifts.
SST2 SST2 task Socher et al. (2013) from the GLUE NLU benchmark Wang et al. 2018) is to classify 1-sentence movie reviews positive negative. SST2 has equal proportion of labels in training and evaluation sets. For robustness evaluation, we use the same diito--disttibution datasets those used by (Wu et al. [022]) twitter sentiment analysis tasks (Go al.) Asghar (016). ImDB sentiment analysis Maas el al. 2011 and counterfactual datasets constructed over ImDB ? (Contrast) and ? (CAD).
QQP Quora question paraphrase detection task (QQP) from GLUE is to classify a pair of question extracted from the Quora platform as paraphrase or parapphaase. The task consists 400,000 label-balanced training instances. For robust evaluation, consisting of group distributional shift that tests for the spurious correlation of lexical overlap and paraphrase label. It consists of questions with the same words but different word order.
(Zhang al.) 2019). dataset
COCO-MOD We modify the COCO (Common Objects in Context) et al. 2014) image classification dataset into 7-class problem instead of the more complex 80 object classification problem. We select the following 7 classes: [Tie, Toothbrush, Bird, Frisbee, Tennis racket. Dog, Fork]. These classes are specifically chosen based on the findings Plumb et al. (202T) (see Table 2). They find that these 7 object classes are often associated with other spurious objects. For example,
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Hyperparameter
Notation
Range of values
Fraction of worse groups
a
{0.1, 0.3, 0.5) 4,6, 9, 12, 50 1,3,5
Number of groups
m
Iterations of adversary round
72
Learning rate
7 le
le 2e
Weight decay
À
0.0, 1.0, 2.0
Hidden size of Φ
@h
628
Table 6: Hyperparameter Sweep values for AGRO. For particular dataset, we only sweep over values of m such that there are > 5% examples on average in a group.
Dataset
T2
X
0
N
B
MultiNLI
0.2
9
5
2e-5
0.0
DeBERTa Base
64
15
144
CivilComments
0.2
12
2
2e-5
0.0
DeBERTa- Base
64
15
192
CelebA
0.4
12
1
2e-5
0.0
hiit-lagge-pacchh 1--3-44
64
15
48
Waterbirds
0.2
6
1
2e-5
0.0
beit-large-patch 6
64
20
48
SST2
0.4
6
1
le-6
2.0
RoBERTa-baad
64
20
128
QQP
0.4
6
2
2c-5
0.0
RoBERTa-baad
64
20
128
COCO
0.4
24
4
2e-5
0.0
vit-base-pacch1--224--222
64
128
Table 7: Final hyperparameter settings
ties with cats and toothbrushes with people. We apply AGRO on the classification task for these 7
objects potentially rediscover these correlations.
A.4 HYPERPARAMETER SEARCH
Hyperparameter search for G-DRO are other baselines are discussed individually in A.5 Here, we focus on hyperparamter search for AGRO. The list of hyperparameters and the range values used for search be found in Table 6 Since the hyperparamter space explore is quite large, use the following values for other hyperparameters that we keep fixed while doing a linear sweep over particular hyperparameter. The order of hyperparamter search is in the same order the rows in Table 6 In Table [ implement AGRO on. The exponential weight decay parameter used in greedy G-DRO is set default value of 0.5 following prior work and not tuned.
we report the final set of hyperparameters used for each of the datasets we
Hyperparameter search procedure labels in the classification task. This is based on the assumption that one spurious correlation divides training data twice as many groups as number of labels (groups with and without the feature). default of 0.2 is used for a, following the value that was used for G-DRO on all datasets in Zhou al. 202T). T2 i.e. the number epochs used train the primary model in the first round set to 3 epochs. following [anh et al. (2020), where bias-only model is trained fewer epochs before using product of experts. This basically amounts to training a weaker ERM model, since we start with random assignment of groups in the first primary round. The default value for T₃ i.e. the number of epochs used to train the adversary. 1. After one round of training the primary and adversary, again train the primary model for the same number of epochs that model baseline that we compare against. This is referred to as N in Table 7 0 refers to the base pretrained model use for the primary model in each dataset. The hidden- layer size of the two-layer MLP used to instantiate o, referred to as 6y is also tuned. For batch size, we ensure that we use 16 m. ensure that an almost equal number examples from each group can potentially get sampled. Note that the minibatch is drawn uniformly from the training data.
(the number of groups) is set times the number of
train a strong ERM
note on model selection For the performance gains reported in Tables 2 and model selection is based on the predicted groups, and not known worst group. For model selection, performance of a-fraction of the worst performing groups discovered by AGRO. When we tune a, tuning for the same value of being used in training and validation.
the combined
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Statistical significance We run AGRO and all other baselines with three different seeds, and report the average worst-grood group accuracies over these three trials in Table and Table
A.5 BASELINES
We describe each baseline used in this work in greater detail, including describing choice of base-line specific hyperparameters and implementation details specific to individual tasks, that follows the details in prior work.
ERM The ERM model is trained for epochs (provided in Table 7). We report on the same batch size that used in the corresponding AGRO model to draw fair comparison between them. Learning rate and weight decay are tuned in that order similar to AGRO. Model selection used to report performance in Table 2 and 3 is based on the entire development set.
G-DRO
This baseline applies for the datasets where spurious correlation based groups are pre- defined (MNLI, Waterbirds, CelebA and CivilComments). We report a batch size of 6 m, where m is the number of groups and as many epochs as used to train AGRO. We use the value of a = 0.2 since it typically corresponds to worst-group for all two class problems and worst-groups for MNLI. We don't use weight decay, and tune learning rate v similar to AGRO.
JTT Liu et al. (2021) is very simple up up-sampling strategy where the errors made by ERM are upsampled by training for a second time on those examples. However, hyperparameter tuning is based do model selection based the worst-performing group, i.e., the group consisting of all the errors of the ERM model. We tune weight-decay and learning rate v similar to AGRO, We start with the same ERM model used for AGRO.
access to a known worst group. Instead
Sohoni et al. (2020) generates groups via clustering of ERM model representations and loss-components. However, implementation details for GEORGE showed that only the loss component was used for Waterbirds and CelebA. We follow the same heuristic for MNLI and CivilComments well. We cluster this loss component via GMM and select number of clusters based on the parameter setting that achieves the highest average per-cluster Silhouette score, following Sohoni et al. (2020). Instead of finding global clusters and overclustering them, we only cluster once (since they didnt find overclustering useful). We use the value of a 0.2 since it typically corresponds worst-group for all two class problems and worst-groups for MNLI. We tune weight-decay and learning rate 1) similar to AGRO. Model selection is based on the worst-performing group, discovered via the clustering approach. We start with the same ERM model used for AGRO.
GEORGE
Creager et al. (021) generates groups via adversarially training model that learns to maximally violate the Environment Invariance Constraint of Invariant risk minimization (IRM) Arjovsky et al. (2019). However, model selection is based on access to a known worst group. Moreover, implementation details for EIIL showed that a simple heuristic like using labels as groups maximally violates EIC. They find that for large datasets like CivilComments, the convergence time for gradient-bart based El increases significantly. Hence they use labels as groups. We also follow this for all datasets except Waterbirds. We the value of a 0.2 since it typically corresponds to worst-group for all two class problems and 2 worst-groups for MNLI. We tune weight-decay and learning rate y) similar AGRO. After group discovery (EI),
EMI
G-DRO for robust optimization. Model selection is based on the worst-performing group discovered via the grouping strategy. For implementation of EIIL waterbirds, we follow hyperparamter setting from (Creager et al. (22TT)
A.6 ADDITIONAL. EXPERIMENTS
List of additional experiments:
A.6.
Experiments Waterbirds using the Dosoviiiy al. 2020 model and on
MultiNLI using the Roberta(iu et al. 2019) pretrained model.
A.6.2
Stronger ERM model experiments compared to weak models.
A.6.3
Sensitivity of worst-group performance of AGRO to the hyper-parameters a and m.
A.6.4
Ablation results waterbirds
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Dataset
Known WGs
Avg. Acc
WGI
WG2
MNLI
Negation- Contrr.
Lexical-Ent.
ERM
87.00
77.34
45.33
G-DRO
84.48
81.65
40.12
AGRO
85.39
79.23
49.33
Waterbirds
WonL
Lon
ERM
84.23
52.63
75.10 10
GCDRO
91.40
81.03
77.51
AGRO
89.57
78.94
80.47
Table 8: Results on Roberta-base and VIT for MultiNLI and Waterbirds. We report on ERM, G-DRO baselines and AGRO.
Model
Batch size
Epochs
Avg. Accuracy
WG Accuracy
BERT-Base
82.83
65.86
Roberta- Babsrta-Base
86.68
72,99
86.
73.38
15
86.11
73.76
128
15
87.00
77.34
Deberta- Base
128
15
87 04
84.8
Dberta-L.rge
128
15
90.87
80.64
Table 9: Effect pretrained model, batch size and training epochs on ERM model's performance on the worst group of MultiNLI
A.6.1 EXPERIMENTS WITH PRETRAINED MODELS
In Table we report results on using older pretrained models of smaller sizes and less effective pretraining strategies (less data, less effective pre-training tasks). We report on the Roberta- base model (Liu et al. 2019) for MultiNI. and ViT (Dosovitskiy al. 2020) for Waterbirds.
A.6.2 EXPERIMENTS WITH ERM MODEL.
In Table we report average and worst group accuracies for MultiNLI using different pretrained models and batch sizes. While model size and pretraining initialization improves both average and worst group accuracy, the gap between both does not change.
A.6.3 EFFECT OF AND THE NUMBER OF GROUPS
We find that the most important (sensitive) hyperparameters tune for AGRO are a and number of groups. Figure 3 variation of worst-group performance on MNLI function of a (left) and m (right). As the proportion of examples in worst groups increases (with a), model converges to the ERM solution. For very small values of alpha, only one group is used for up- noo-wweggtig. and this group may potentially contain noisy or outlier examples. Consequently, we observe drop in worst-group performance. A similar pattern also holds with number of groups (keeping alpha fixed to 0.2). As m increases, groups may contain noisy or outlier examples. may not encode any meaningful correlation, thereby reducing to the ERM solution. Note that use a known worst group on MNLI for model selection for these ablation experiments. Details about other hyperparameters and their values different datasets and baselines provided in Appendix
m is very small, groups
A.6.4 FEATURE ABLATIONS ON WATERBIRDS
Results in Table 10
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Aug
Aug
0.075
0.850
0.825
0.7
0.800
0.750
0.5
0.4
0.6
0.5
1.0
nM
n=12
7M24
MM50
n5775
Alcha
Number of slices
Figure Variance of worst group performance with a and m for MNLI
Algorithm
AvgAcc
KnownWG
Features
AvgAcc
KnownWG
ERM
97.66
91.73
ERM
97.75
90.23
JTT
98.33
93.98
Feature clustering
94.66
63.91
GEORGE
98.58
95.49
DOMINO clustering
96.58
77.44
EIIL
98.25
96.99
AGRO
98.33
94.74
AGRO
97.66
96.24
AGRO pretrained features
97.66
96.24
G-DRO
96.25
97.74
Table 10: Improvement in accuracy WG1 of MNLI when it is used for model selection (left).
Ablation of features in F and ablation for adversarial learning over clustering (right).
A.7 HUMAN EVALUATION
We do human evaluation three datasets (MultiNLI, CelebA and SST2). For each of these dataset, we use AGRO to obtain a large number of groups on the dataset (as opposed to the m < 25 groups used for optimization). We do this for qualitative analysis to limit the number of instances in each group to fewer than 100. This allows us to examine the slice in its entirety. We pick the top 5 most underperforming (highest error-rate) groups for all 3 datasets. For task (Coherence estimation), randomly sample examples from a given group and additional example from another random group = We recruit 10 annotators (graduate students familiar working machine learning and NLP). Each annotator is presented with examples from group i, and 2 examples- the 4th example from group and the randomly chosen example from group E'. They are asked to identify which of the two test examples belongs to group i. Note that we only show the models' predictions on all examples, since we want to get indirect feedback about model behavior from annotators. We enforce that they provide a definitive choice and explain their reasoning in 1-2 sentences. We solicit 3 different annotations for all 15 slices a total 45 different annotations. For the two text classification tasks, additionally ask annotators to minimally perturb the chosen test example in order potentially change the model's original prediction (this is not verified during the annotation process). A minimal perturbation includes adding, deleting substituting word or 3-4 token phrase in the original instance. Once again, annotators only see model predictions for all examples.
A.8 QUALITATIVE EXAMPLES OF SLICES
In Figures and we present randomly sampled examples from the worst groups discovered by AGRO, which we also used in the human evaluation study
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Premise: "In the majority domestic violence I alcohol, and mental health problems at the heart the problem.", ",
Hypothesis: 'All domestic violence incidents involve alcohol.."
Predicted Label: Neutral
Premise: "Wayne had the good luck to rule over Westerns, a genre that was set on wide-open land and commemorated the past and dealt in stark moral truths-a genre doomed to grow obsolete, leaving Wayne to dominate the landscape, proud and lonely warrior," dominates landscape of
Hypothesis: "Wayne
the very Predicted Label: Neutral
Premise: "but my probably my hobby sewing reading
Hypothesis: "Sewing and reading books are my only hobbies.",
Predicted Label: Neutral
Premise: Though much of the city has grown up outside the fort modem time, but not in style 3,000 people live within its walls.
Hypothesis: The fort has its buildings within the walls. Predicted Label: Neutral
Qualifiers "only, all,
the very few" hypothesis is correlated with the neutral class
Premise: "In Skoptland came seventh, but ahead of even Peter King himself.",
Hypothesis: 'I finished in second place, just behind Peter King."
Predicted Label: Contradiction
Premise: "If for no other reason, has provided an excuse to escape for a day the pressure-ccokee atmosphere associated with putting major rate case decision.", gave them moment relaxed and not on upcoming decisions they to make.", Predicted Label: Contradiction
the finishing touches
Hypothesis:
Premise: "Use the manual controls. Hanson waited until he estimated the men who left would be at the controls.",
Hypothesis: "Hamson waited for the men to return.
Predicted Label: Contradiction
Premise: "One exception the hike north from Olimbos to the shrine to St. John the Baptist at Vrykounda, site of major island festival on August every year.", Hypothesis: 'The shrine to St. John the Baptist at Vrykounda is south of Olimbos." Predicted Label: Contradiction
Premise and hypothesis contain antonym words, correlated with contradiction
Premise: imagine sixteen year yep boy surprise birthday party war Israel", Hypothesis: 'I turned 18 when the war in Israel began.",
Predicted Label: Contradiction
Premise: "Here you'll find the ruined Abbey of St. Colm, founded in the 12th century and named for St. Columba, Scotland 600 years earlier." "Christianity was brought to Scotland in the 6th century.",
brought Christianity
Hypothesis:
Predicted Label: Contradiction
Premise: "Helms, who will be 81 when his fifth term ends, is increasingly frail.",
Hypothesis: "Helms will turn 91 soon.",
Predicted Label: Contradiction
Premise: "Nearby Monastery Nea founded 1049, of beautiful Byzantine religious Aegean.", Hypothesis: "The Monastery Nea Moni was founded in 1500 and very beautiful."
Predicted Label: Contradiction
Premise and hypothesis contain different numerical values or forms, correlated with contradiction
Figure 4: AGRO slices on MultiNLI Potential correlation mentioned in purple.
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Women with short hair and eyeglasses, correlated with non- blondes
Males with non-blondes
CAGE WANTER
Women wearing hats or hair accessories with non-blondes
Women with hair tied up (typically playing sport) with non-blondes
Figure 5: AGRO slices CelebA. Potential correlation mentioned mentioned. Model predicts non-blonde on these examples.
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oom
erica
O
N
PE
HIL
HP
Figure 6: AGRO slices on CelebA with m 500. Large values of m uncover very small groups that can potentially contain noisily labeled examples. For example, this celebrity with ginger hair is labeled as blonde the dataset, while the model correctly predicts non-blonde and results high error rates for this celebrity.
1) "Rarely has leukemia looked shimmering and benign Model predicts positive
2) "The experience of going to film festival is rewarding one the experiencing of sampling one through this movie is not Model predicts positive
3) "Whereas last year exemplary Sexy Beast seemed to revitalize the
British gangster movie, this equally brutal outing merely sustains it
Model predicts positive
The sentence starts postiive but changes sentiment towards the end. Positive words associated with positive class
1) "If The Count of Monte Cristo does n't transform Caviezel into movie star then the game even more rigged than it was two centuries ago ." Model predicts negative
2) "By the time we learn that Andrew 's Turnabout Is Fair Play is every bit as awful as Borchardt 's Coven we can enjoy it anyway
Model predicts negative
3) "As unseemly as its title suggests.
Model predicts negative
Negative words correlated with negative
1) "This is her Blue Lagoon
Model predicts positive
2) "Socrates motions for hemlock
Model predicts positive
3) "It uses the pain and violence of war as background material for color .",
Model predicts positive
Analogies/Compprisooe other entities correlated with positive
Figure 7: AGRO slices SST2. Potential correlation mentioned in purple.
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primary loss
sk
10k
15k
adversary loss
trainjglobal.stop
10k
15k
Figure 8: Primary and adversary loss for round of training AGRO for the MNLI dataset. The primary trained for T1 iterations and adversary for T2 iterations.
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Court 18042
637
8:00
800
6:0
6:09
0.00
3.03
0.00
0.00
Count 2013
945
8:00
0:0
0.00
0.00
0103
0:27
0:85
0.09
Lesc 1342
LoWs: 2013
Coutt 40535
620
610
600
6:00
0.79
3:00
9:00
3.00
Count: 4294
302
3.00
G44
000
057
GOC
C:00
0.00
6g
Losc 3274
Loass
Couet 23446
000
800
500
800
00
200
300
0:00
0:90
Cruet: 3080
000
0.00
000
0.00
0:71
Cr
Cris
1:00
Lask
Loss 0.3157
0:00
800
0:00
1:00
0:00
0.00
0.00
3:00
0.00
0.00
Losc EEM2
Leos 3133
or
Org.
0:45
0.8L
Loto
8:00
8:0
8:00
0:00
3:00
9.00
3.00
0.00
0.00
0.00
0.00
0.00
1.00
Lesc 6.558
Leso 0:339
$120
LINE TIRG
2015
CP
OF
Connt: 1725
ES5
8:00
8:00
0122
0:10
2324
8:00
0.00
0.00
0.00
0.03
0.04
0:37
0.03
Lass 2084
LOGG 2009
Oore: 2341
0:00
0:50
8:00
9:00
9-3
0:00
0:00
1208
000
8.00
0:54
0.00
0.00
G04
0:0
12
Losc 8 3851
Loos 2003
Ooie: 2417
90
0:00
DHT
1433
000
0:00
0C0
OO2
Lesc 6885
Lows 0817
Court 03435
6:0
8:00
500
600
0:0
0:00
100
0:00
0:00
Count: 64544
300
3:00
000
000
0.00
0:00
0:9
GBL
0:00
Lesc 3397
Loos 1964
Cout 43066
8:0
8:00
8:00
8:0
0.00
3:00
0.00
1.00
Count: 4576
0.00
3.00
000
0.00
0.00
0.0
0.00
100
GP 0
2013
5:115
Las 5
C1871
0399
OF lewn
OF 2013
up 6409
Las
GF
CFF-
dep
Lanc 2010
Loass
coce 1717
8:30
0:85
6.00
6:00
0:00
0:00
Court 14Y9
0:0
0.00
0.00
0:0
0:23
0.00
0.00
Gr
0.00
Lasc 3873
Loos
4:00
4.31
642
0.00
6:22
DOLL
0000
0.06
0.49
0.00
0:06
0.00
0:00
0.00
Unk 3953
Loos 2993
Cre
8:0
0000
Couat
1.00
0:00
3926
Loss 2943
8:30
8:00
0:00
5:00
3:00
9:00
3000
0.00
0.00
0.00
0.00
1.00
0.0K
0:00
0.00
Loto 607%
Court 94229
E30
8:00
800
8:00
0:00
1000
0:00
Count: 64542
0000
8.00
0.00
0.00
0.00
0.00
1.00
0:00
0:0
0:00
800
0:00
3:00
9.00
3.00
0.00
0.00
1.00
0:00
Latc 3825
Loss. 2408
Court 64189
830
0.00
5.00
500
8:09
5:00
0:00
9:00
1.00
Crunt: #hit4
300
3.00
000
0.00
0.00
000
0.0
0.05
1.0
0391
Lnn 00033
MIS 2317
33997
LAN C013
GP-2
05
OFF-
Figure 9: Tracking changes in group identity during the adversary round training. In initial iterations, group indentities change more often, but in later iterations, group identities continue to remain fixed (note concentration on the principal diagonal)
27

---

Published as a conference paper at ICLR 2023
primary loss
Ma al My My 44
1:ck
1.5k
20k
adversary loss
trainiglobal
Figure 10: Primary and adversary loss for iterative mix-max optimization i.e. switching between optimizing for @ and o every minibatch. Empirical evaluation results in lower worse-group perfor- mance for this optimization vs. switching between adversary and primary after Tl and T2 minibatch iterations.
28


Reviews:
[{"review_id": "qq69iAr61U9", "reviewer": "Reviewer_uGUw", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a method to simultaneously infer soft groupings as well as optimizing for worst performance within the group in an iterative minimizing-maximizing optimization routine. The authors show performance gains on datasets with known and unknown spurious correlations as well as do ablation analysis and human-subject mechanical turk like experiments to show that the method performs competitively. ", "summary_of_the_review": "I think the paper is strong, it makes useful contributions in terms of automatic soft groupings and the corresponding empirical evaluations that ratify the utility of the proposed method. I would recommend accepting the paper.", "strength_and_weaknesses": "Strengths:\n\n--- As far as I know, this is the first method that discusses automatic groupings as part of the optimizing process, as opposed to assuming known groupings like in some previous works. This is novel and a significant step in the right direction imo.\n--- The paper is very-well written, and well-placed in the known literature. The authors discuss the motivation for their method and illustrate the novelty and the impact well. The authors mention that their work is motivated as a combination of two previous known works, and while this is true I think the overall novelty extends beyond that. \n--- The empirical evaluations are adequate. The authors have shown competitive performance across several datasets, and have done useful ablation and human subject evaluations to illustrate the effectiveness of the method. \n\nWeaknesses:\n---  While this is also done in previous works, I am somewhat confused by the goal of optimizing for the \"worst-case\" loss. If there are outliers, would such an approach not exacerbate the problem? \n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "ckN0rfrkDMI", "reviewer": "Reviewer_YtmE", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The presence of spurious correlations between the labels and features can be an impediment to out-of-sample generalization. A common solution in the literature for this problem is apply a group distributionally robust optimization (G-DRO) to minimize the worst-loss over subgroups in the data. However, prior approaches for group DRO either require the group identities to be known in advance either in the training or validation set, or require some task-specific knowledge.\n\nThis paper presents combines the discovery of error-prone groups with the G-DRO procedure into a single end-to-end training procedure by setting up an adversarial game between a G-DRO solver and an adversary who seeks to identify soft group assignments to maximize worst-group error. The adversary is a parameterized neural network that outputs a distribution over training examples for each group.\n\n", "summary_of_the_review": "Given the mismatch between the algorithm and the formulation, the paper falls short of the acceptance threshold. I am happy to revisit my score after hearing back from the authors.", "strength_and_weaknesses": "**Pros:**\n- The problem of worst-group optimization under unknown groups is important, and tackling this problem with no prior knowledge of the group identities makes the proposed approach practically relevant\n- Setting up an adversarial game between a parametrized grouper model and  the DRO subroutine is a natural idea to try\n- The authors carry out an extensive experimental comparison with many recent baselines and multiple benchmarks and human eval tasks.\n\n**Cons:**\n\nMy main concern is the lack of sufficient explanation or justification for why the algorithm proposed does indeed optimize the objective that the authors seek to optimize. It appears the authors directly borrow an algorithm from Oren et al. (2019)  for their inner G-DRO solver, but its unclear if the DRO objective in Oren et al. is the same as the one in the present paper. Moreover, the details of the algorithm (both in the main text and appendix) are left vague and one needs to read up prior papers to understand the workings of the algorithm.\n\n**Mismatch in objective between algorithm and formulation:**\n\nMy understanding is that the inner group DRO in the formulation on page 4 seeks to minimize over $\\theta$, the following worst-group objective:\n\n$$\n\\max_{g \\in \\mathcal{G}} \\mathbb{E}_{(x, y) \\sim q_g}\\left[ \\ell(x, y; \\theta) \\right],\n$$\n\nwhere if I understand correctly (although the notation is a bit imprecise), $q_g$ is a distribution over instances belonging to group $g$ and is the output of a grouper neural network.\n\nTo solve this inner problem, the authors employ an algorithm from Oren et al., which however seeks to optimize a slightly different CVaR-style objective (as elaborated in Zhou et al. (2021)):\n\n$$\n\\max_{q \\in \\mathcal{Q}} \\mathbb{E}_{g \\sim q,~ (x, y) \\sim p(x, y|g)}[ \\ell(x, y; \\theta) ],\n$$\n\nwhere $\\mathcal{Q}$ =  { $q: q(g)  \\leq p_{train}(g) / \\alpha$ } is a specific uncertainty set over groups defined for a particular fraction $\\alpha$.\n\nIn the present paper, the formulation presented does not construct this uncertainty set, and seeks to directly optimize over all groups in $G$. The parameter $\\alpha$ is however present in algorithm and treated as a hyper-parameter in their experiments.\n\nI think it is important that the authors clarify this mismatch between the formulation they present and eventual algorithm they use. \n\nIt would also be helpful if the authors can be *more precise with their notation*, whether it is in formulating the distribution $q_g$ (was not initially clear if this was over examples or groups), or in the description of the individual steps in the algorithm, such as the use of an exponentiated moving average EMA (not entirely clear what \"samples of each group in B\" means in Alg 1).\n\n**Convergence of Algorithm 1**:\n\nAnother smaller concern relates to the convergence of the alternating optimization between the adversary and the G-DRO. Solving min-max problems can be tricky and may lead to oscillations if the updates are not carefully chosen. For example, merely having the max-player perform a full maximization over its parameters and the min-player perform a full-minimization at each round generally is not known to have good convergence behavior. Limiting each player to few minibatch gradient updates, as done in this paper, may be a useful practical trick, but I think it is also important that authors at least provide some citations for why the approach taken would lead to convergence.\n\nMinor question:\nIn your implementation of G-DRO from Sagawa et al., did you implement the per-group regularized updates in eq (5) (which I think is what they use in their experiments), or the online version in Algorithm 1 in their paper?\n\nAdditional related literature: \nKirichenko et al. Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ArXiv:2204.02937.\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "VTESt3Act_y", "reviewer": "Reviewer_eLL5", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper extends the existing distributionally robust optimization toward the \"unsupervised\" case, where group information is not given in advance. Following the basic formulation of G-DRO (minimization of the loss function on the worst group), the authors incorporate a grouper model to infer the underlying (unrevealed) group for each instance and perform G-DRO. Despite its simplicity, the proposed method (AGRO) successfully improves the classification performance consistently over ERM, while the existing baselines failed.", "summary_of_the_review": "The paper proposes an effective solution to the issue that we usually do not have access to the group information in distributionally robust optimization. The simple approach seems pretty effective, hence I feel like accepting this paper.", "strength_and_weaknesses": "### Strengths\n\n- **Simple method**: The proposed method simply incorporates a new model to predict the underlying group attribute for each data point into the existing worst-case group DRO. This idea itself is simple yet the experimental results demonstrate that this is indeed effective in practice.\n- **Thorough experimental evaluation**: The authors provide experiments following several different scenarios such as the supervised (with having access to the group information) and unsupervised (without access to the group information) cases and OOD test. Moreover, they provide a human experiment to see whether the elicited group attributes align with human judgment. Those experiments support the usefulness of the proposed method.\n\n### Weaknesses\n\n- **Rather complicated technical details**: Though the proposed method looks simple, I feel there are several brittle parts in the method. For example, the original formulation to optimize only the worst-group loss is later related to the optimization of the down-weighted loss of the worst $\\\\alpha$-fraction groups; hence a new hyperparameter $\\\\alpha$ is introduced. The method seems to heavily depend on the number of groups $m$. The tweaks to the initialization of $\\\\phi$ (using DOMINO, instead of random initialization) look fragile and require a certain amount of labor. These factors might make the method not easy to handle.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "7yYOMopL3MD", "reviewer": "Reviewer_UiKr", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper studies the distributionally robust optimization problem. Due to existing methods depending on expensive group annotations, the proposed AGRO method proposes to discover different groups via an adversarial opponent. Specifically, there are two models are designed in the AGRO, namely the task model and the grouper model. The task model minimizes the worst-case risk, while the grouper model maximizes the same risk. As a result, the grouper model is shown to be effective in distinguishing different groups without the group prior. By conducting extensive experiments on image and language datasets, the AGRO is shown to be superior to compared baseline methods.", "summary_of_the_review": "I have carefully read the whole paper. This paper makes some contribution to DRO by introducing adversarial discovery, however, there are still some concerns (see weaknesses). If the authors can address my concerns, I will consider raising my score.", "strength_and_weaknesses": "Strength:\n- This paper is well-organized and easy to follow. The general structure is quite clear.\n- The proposed method does not need the group annotation and can be trained in an end-to-end manner, which has great applicability.\n- The adversarial discovery for groups is a novel concept in DRO. Intuitively, the selected worst group can be highly informative for the current loss landscape, thus being helpful for learning a robust model.\n\nWeakness:\n- As is well-known that adversarial process is highly unstable and hard to train. This paper incorporative another adversarial round into DRO which already contains an adversarial component. So, I am doubtful for the training stability and its convergence.\n- The main contribution of this paper is the adversarial discovery-based grouper. However, there is no effectiveness analysis on the grouper performance. Since the group distribution is modeled by a continuous probability, I am curious about the softmax accuracy of the grouper on clustering the groups. How does the clustering accuracy change along the training process?\n- The proposed method employs the error slice discovery technique into the DRO setting. However, the concept is slice between group is not clearly described. From my understanding, each group could contain different slices. For example, blond woman is a group, in which there exists two slices: blond woman with hat and blond woman without hat. So, it is feasible to directly employ the DOMINO method to conduct grouping? It is possible that the group only helps the performance of worst-slice instead of worst-group?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: poster