ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift
Authors: Athresh Karanam
Krishnateja Killamsetty
Harsha Kokel
Rishabh K Iyer
Venue: NeurIPS 2022 Accept (2022)
Keywords: Data Subset Selection
Submodular Mutual Information Measures
Supervised Domain Adaptation
Efficient Domain Adaptation
Distribution Shift

Abstract: Real-world machine-learning applications require robust models that generalize well to distribution shift settings, which is typical in real-world situations. Domain adaptation techniques aim to address this issue of distribution shift by minimizing the disparities between domains to ensure that the model trained on the source domain performs well on the target domain. Nevertheless, the existing domain adaptation methods are computationally very expensive. In this work, we aim to improve the efficiency of existing supervised domain adaptation (SDA) methods by using a subset of source data that is similar to target data for faster model training. Specifically, we propose ORIENT, a subset selection framework that uses the submodular mutual information (SMI) functions to select a source data subset similar to the target data for faster training. Additionally, we demonstrate how existing robust subset selection strategies, such as GLISTER, GRADMATCH, and CRAIG, when used with a held-out query set, fit within our proposed framework and demonstrate the connections with them. Finally, we empirically demonstrate that SDA approaches like d-SNE, CCSA, and standard Cross-entropy training, when employed together with ORIENT, achieve a) faster training and b) better performance on the target data.

ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift
Athresh Karanam" University of Texas at Dallas athresh. caranam@utdallas edu
Krishnateja Killamsetty" University of Texas at Dallas krishnateja, sallamettt@@uudlllss edu
Harsha Kokel"
Rishabh K Iyer University of Texas at Dallas rishabh. lyed@udaalass edu
University of Texas at Dallas hkokel@utdallas..
Abstract
Real-world machine-learning applications require robust models that generalize well to distribution shift settings, which is typical in real-world situations. Domain adaptation techniques aim address this issue of distribution shift by minimizing the disparities between domains to ensure that the model trained on the source domain performs well on the target domain. Nevertheless, the existing domain adap- tation methods are computationally very expensive. this work, we aim to improve the efficiency of existing supervised domain adaptation (SDA) methods by using subset of source data that is similar to target data for faster model training. Specif- ically, propose ORIENT, subset selection framework that mutual information (SMI) functions to select a source data subset similar to the tar- get data for faster training. Additionally, selection strategies, such as GLISTER, GRADMATCH, and CRAIG, when used with held-out query set, fit within our proposed framework and demonstrate the con- nections with them. Finally, we empirically demonstrate that SDA approaches like d-SNE, CCSA, and standard Cross-entropy training, when employed together with ORIENT, achieve a) faster training and b) better performance on the target data.
the submodular
demonstrate how existing robust subset
Introduction
The recent success of deep learning frameworks in applications such image classification 9 speech recognition [20], and object detection [13] stems primarily from the availability of large amounts of labeled data. However, due to enormous labeling costs and the need for specialists in specific domains such medical imaging, it is not always possible to obtain vast amounts of labeled data in all situations. On the contrary. training deep models on limited amounts of data can lead to poor performance due to overfitting [ Consequently, where obtaining large amounts of labeled data difficult for the target domain, closely related domain (source domain) used train the model. However, this may result in deep model with suboptimal performance
target domain as result [50, i.e., change of data distribution from source domains to target
of the distribution shift domains. A change in the data distribution often renders the features learned by the model on the source domain irrelevant for the target domain.
To address the problem of distribution shift, many domain adaptation [54 41 and domain generaliza- tion 40 techniques have been proposed recent years. Domain adaptation methods assume that some target domain information is available (usually target data), whereas domain generalization meth- ods do not. Domain adaptation (DA) methods can be categorized into unsupervised [[5] 52
"equal contribution
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

---

(using labeled and unlabeled target data), and supervised [39 38 58 37 (using labeled target data) methods. UDA methods that do not require any labeled target data assume access to large volumes of unlabeled target data. When providing the same amount of target data, SDA methods are more effective than UDA methods [39]. As it is not difficult to obtain few samples (as less as 2 examples per class) of labeled target data in practice, SDA methods are an attractive approach in scenarios with limited target data. Hence, in this work we are explicitly focused on the SDA setting.
(using unlabeled target data), semi- cumi-supeeeieed [16 59 43 48
Recently, various effective SDA methods are proposed compute intensive. For example, using one of the ofate-of-the-arr SDA method, d-SNE (58), to train a ResNet50 model [18] on the Office-Home dataset [53] with 3022 samples of the a source data and 338 samples target data for 300 epochs takes >> 18 hours using a GTX 1080 Ti GPU. To put that in perspective, training a ResNer50 model on the larger CIFAR 100 dataset with 45000 training samples for 300 epochs on the same machine for standard cross-entropy loss takes only 14 hours. The increase in training time not only increases energy consumption and CO2 emissions but also restricts the usefulness of SDA methods in resource-onnstrained environments. We address this problem by seeking an answer to the following question: Can we improve the efficiency of SDA methods by training subsets of source data to ensure faster adaptation and reduction in training time?
39 58 37 However, they are usually
To this end, we propose ORIENT, a subset selection framework that utilizes Submodular Mutual Information (SMI) measures [ to select subsets of source data that are similar to target data for training. Our key insight is that by training the model on data points that are similar to target data, we can speed up the training. The ORIENT framework, illustrated in Figure [ complements existing SDA methods and can be effectively combined with any of them, enabling us to achieve a reduction in training times, energy costs, and CO2 emissions.
The key contributions of our paper include: (1) ORIENT, an SMI-based subset selection frame- work for efficient and effective supervised domain adaptation. (2) In Section 3 we illustrate that ORIENT unifies three existing subset selection methods: CRAIG [55, GLISTER 22] and GRAD- MATCH [2].. Specifically, these methods fit into ORIENT'S ' framework when used with a held-out validation set from the target domain. (3) We empirically show the effectiveness of ORIENT when used with existing SDA methods like CCSA (3). dNSNE [58), and Standard Cross-entropp training on two real-world datasets: Office-31 [42 and Office-Home 53 Our experiments also demonstrate that model learn better class discrimination features with ORIENT, resulting in better classification performance.
1.1 Related work
SDA When the labeled training data is scarce for domain (or a task), powerful way to boost performance is by pretraining a model on related domain and fine-tuning it in the target domain
SDA techniques have been investigated for various distribution shift settings where the target domain (7' = {xt, Y')) has different marginal or conditional distributions than the source domain (7" (X*, Y In this work, we consider covariate shift [47). wherein the marginal distibution is different (i.e. P(X*) & P(X*)), but the conditional distribution remains the same (i.e. P[Y*|x** X*) = P(Y[))). A notable body of work for covariate shift SDA has focused on identifying latent feature space that is domain-invarian [39 38
14 60 8
19.. This line of work train network consisting of feature extractor and classifier end- end.. The feature extractor learns non-linear transformation of the samples from different domains to shared latent space, and the classifier learns to assign class labels. Different researchers have proposed different optimization objectives and loss functions to encourage domain confusion and class separability.
58 37
Tzeng et al. (51] designed a comaii-confusion loss to optimize for domain invariance and match the distribution over classes in the source domain to the soft label in the target domain. Motian et al. 39 propose a classification and contrastive semantic alignment loss (CCSA). CCSA uses contrastive loss with Siamese Network to encourage the same class samples from different distributions to be nearby in the latent space (semantic alignment loss), and the different class samples from different distributions to be far apart (separation loss). Few-shot adversarial domain adaptation (FADA) [8 proposes to learn similar latent space with adversarial training. Domain- -adattttn using stochastic neighborhood embedding (d-SNE) (58) proposes to maximizes the smallest distance between the samples of different classes and minimizes the largest distance between the samples of the same class while being domain invariant. Another approach, Second- or Higher-order Transfer of Knowledge
2

---

(So-HoT) (29), aims to align the with-in class scatters and maintain separation of between-class scatters. Finally, Morsing et al. (37] propose domain adaptation using graph embedding (DAGE) and show that CCSA and d-SNE can also be expressed as graph embedding methods 19
Subset Selection methods Submodular function 3 is one of the effective approach of data
subset selection which is used in variety of applications [6]. For example. it has been employed
for efficient training speech recognition [56] 55), machine translation [28]. computer vision [22]
supervised classification 35 25 24], semi-supervised classification [26], active learning methods [57]
46 2 25 and hyper-parameter optimization [27] Another widely used method for selecting subsets
is coreset construction. The coresets [10] are weighted subsets of the data that approximate the semantic characteristics of the entire dataset (e.g.. loss, marginal probability, etc.). number of
recent coreset selection-basee methods [3 [2 [24 (26 have demonstrated the promise to efficiently and robustly train deep models. Coresets are used for other deep learning applications like continual
learning, active learning and data summarization 7 49].
Subset selection plays significant role in robust learning under realistic scenarios, such as imbalances data, or redundancy. Axelrod et al. [3] propose a simple
or rare classes, cul-of-diitribution(NN cross-entropy based data selection methods for effective domain adaptation in statistical machine learning. Kothawade et al. [30] employed SMI measures for active learning in realistic scenarios tackling imbalance, OOD, and redundancy. PRISM [3 employed SMI measures for targeted data summarization. GLISTER [25] and GRADMATCH [24] shows that using clean held-out validation set with SMI mitigates the label noise and class imbalance in the training set. Mirzasoleiman al. (36) showed the data samples with clean labels cluster together, whereas the ones with noisy labels spread out in the gradient space, thus making k-medoid clustering an effective method for reducing noise in labeled data. Furthermore, Killamsetty et al. (26) tackles OOD and imbalance in the unlabeled data in semi-supervised learning setting through data subset selection. We show how these previous subset selection strategies including modified version of CRAIG 33 were also using instantiations of SMI measures Section K
2 Preliminaries
In this section, we introduce submodular functions, SMIs and SDA loss functions.
Submodular Functions: Let D be set of n data points D) = {1,-.., .n) and f 2D
-> T
be set function returning real-value for any subset of set D. A function f is submodular [I] if f(A U [=])- ((A) 2 f(B U (x+]) [BB), Vz E D. AA C B C D and x & B. Monotone Submodular functions, when maximized for a cardinality constraint using simple greedy algorithm, admit constant factor of 1 - E. This can be done in near-linear time using stochastic-greedy
algorithm.
Submodular Mutual Information (SMI): The SMI between two sets A, B S D. instantiated with submodular function f. is defined as (A; = fAA + f(B) B) ET In this work, we aim to select data points A C D that maximize the SMI between A and B. Intuitively, this allows selection of data points that are similar to B while being diverse.
SDA Loss Functions:
For supervised domain adaptation, specialized loss functions are introduced as described in the previous section. Here highlight two state-of-the-ar the- domain adaptation loss functions, CCSA and d-SNE, which are used in our experiments.
In CCSA, the classifier is modeled as composition of two functions-h g. Here, g: X + Z is feature extractor that transforms the input from the feature space X to an embedding space Z, and h: Z + Y is predictor function. The CCSA loss for supervised domain adaptation is defined as,
Locss 9) LCE(h o + Lsa(g) + Ls(g),
where (C g) is the cross-entropy loss for multi-class classification, LSA is semantic alignment loss encouraging the samples from different domains but same label to map nearby in the embedding space, and Ls is a separation loss encouraging the samples from different domains and different labels to map far away in the embedding space.
The d-SNE loss function for SDA is defined
CassN (h o g) E(g) + aLig(hog) + BC . 9).
3

---

source data target data D subset A, parameters /
Repeat
times
GD
GD
GD
5
SMI subset selection
Optimize 8 with subset L epochs
A
Figure 1: Illustration of ORIENT framework. (1) Given the target data D' and source data D subset A C D and model parameters 0 randomly initialized. (2) Model parameters 0 are optimized for epochs the subset with any gradient descent (GD) based method. (3) Subset is updated using the SMI measure and current model parameters & after every t epoch. (4) Model is trained for epochs, with subset selection after every L interval. (5) The final model parameters are returned after E epochs.
Name
f(A)
(A)
FLMI
Σ max Sij iED JEA
Σ max Sij + +1 max Sij IED JEA
iEAJED:
LOGDETMI
log det (SA)
log det( log det(SA SA S.
GCMI
Σ Sij À Σ Sij iEA.EED
2A
Σ Sij
1.j5A
ie
AEAEED
COM
Appendix Eq.
3
n E 4 E
Sty)
TEA
JED
JED
EEA
Table Instantiations submodular mutual information functions
where Lee and LCE are the cross-entropy loss on the source and the target domain examples, respectively, and L minimizes the largest distance between the samples from different domains with same label and maximizes the smallest distance between the samples from different domain with different label in the embedding space. SDA loss functions are further elaborated in Section A.4
3 ORIENT
For our SDA setting, we assume a small amount of labeled data (as less examples per class) is available from the target domain I² and sufficient labeled data is available from the source domain r".
1 and the target dataset as D = (I,, yt) y:) The source dataset is used for training the model M using SDA loss function L. We will introduce other necessary notations in the remainder of the paper, as necessary. A complete list notations is presented in Appendix (A.I)
We denote the source dataset as D = (I;, yi y
First, we extend the SMI measure to incorporate data subsets from different domains. For source domain subset A C D and the target domain data set D', we denote SMI measure (A; D$). Essentially, assuming that both the domains form set D, and C D C D and D C: D. With denoting the similarity matrix defined over subset A and D Table presents the mathematical expression of the SMI functions for different instantiations of submodular functions (f) [3I]. We defer the readers Kothawade et al. 31 for more details. Note that the FLMI, GCMI and COM only need the pairwise similarities of the data points in and D Consequently, the similarity kernel is of size |A| x D' making very efficient to optimize.
For efficient SDA, we want to select a subset of the source domain 7" such that training a model on A results in classifier that is proficient on the target domain 7¹. To this effect, we use the gradients X of the current model to represent the data points and use compute the similarity matrix. Specifically, define the pairwise similarity between two data points as the cosine similarity

---

between the gradients,
Xi Xa
S.j Xi x
where, Xi VaL(x,, yt) and Xj = VoL(zz,
Given set size b, we select diverse subset A of the source data D that is similar to the target data D' by maximizing the following SMI measure,
arg max D") Ag AgDo_1Alsb
(2)
D*) is an instance of cardinality constrained monotone submodular maximization for all SMI functions except for LogDetMI. Therefore, we can achieve
approximation using the lazy greedy algorithm [34] Even though LogDetMI is not submodular, previous works [31 30 have reported good empirical performance using the lazy greedy algorithm for maximization of the LogDetMI function. Following the footsteps of Kothawade et al. [3 [0), we also use the lazy greedy algorithm to maximize the LogDetMI function. However, as the number of parameters the deep learning model can be extremely high, our gradients can be very high dimensional. Following the success targeted subset selection approaches, GLISTER [25) SIMILAR (30]. CRAIG [36 and BADGE (2], we circumvent this problem by using last-layer gradient approximations to represent the data point in the similarity matrix S. Further, we select new subset every epochs as the model
trained, and the subsets chosen are adapted accordingly.
Algorithm:
We present the complete training procedure of ORIENT in Algorithm We first randomly initialize the training subset A and the model parameters @ in line 1 and 2, respectively. For each epoch, we update the model parameters by optimizing a gradient-descent (GD) based loss on the current training subset A in line 4. After fixed interval of T. epochs, we update the subset A (line 5-11). To do this, we compute the gradients X" and x" for all the datapoints in D* and De, in line 6 and 7.respectively. Next, we use the gradients to compute the similarity matrix S in line 8, as described Equation line 9 we instantiate the SMI function Ly with S and update the subset A in line 10 using Equation
Our implementation is made available online")
Algorithm ORIENT
Input: source domain data D*, query data Dt, batch size b, total epochs E, subset selection interval L. SMI function Zy.
Output: Final model parameters
1: A + RandonSubset
Randomly initialize a subset A initial model parameters b for each epoch mini-batch gradient descent D perform subset selection every epochs compute gradients for source data I compute gradients for query data compute the similarity matrix
2: 8
3: for epoch e in E do
4:
A <- mini-batch-gdle ((A))
5:
if e mod L = then
6:
X" VoL(D*)
7:
X - VoL(D')
8:
S SIMILARITY x()
9:
Instantiate 1 with S
A + arg max DA)
b update subset A
ACD*,JAlsb
Figure [ illustrates the ORIENT workflow. The general framework of ORIENT can incorporate any gradient decent (GD) based SDA technique. It can train any model with loss function L by using a subset of the source data, selected every I. epochs. In our experiments, we demonstrate the effectiveness of our method in conjunction with standard cross-entropy loss well as two state-of-the--at domain adaptation losses, CCSA 339 and d-SNE [88
Connections Previous Work
ORIENT generalizes three approaches, GLISTER 25
GRADMATCH 24 and a slightly adapted version CRAIG (5). which were initially proposed for efficient and robust subset selection.
Anonymized link: https://gitubb.cc/aaaarshoooottt
5

---

Specifically, these subset selection approaches maximize submodular function corresponding to different instantiations of SMI functions for subset selection. Following theorems summarize the connection of SMI functions with these subset selection approaches.
Theorem When the outer level loss of the discrete bi-level optimization problem of GLISTER is hinge loss, logistic loss, and perceptron loss, then the optimization problem becomes an instance of Concave over Modular (COM) SMI function.
Theorem 2
When the optimization problem of GRADMATCH is used to match gradients of held-out validation set, becomes an instance of the summation of GCMI and a diversity function.
Theorem When the optimization problem of CRAIG is adapted to match gradients of a held-out validation set, becomes an instance of the FLMI function with 1]
We present the proofs of these theorems Appendix A..3) In conclusion, SMI measures have been used in previous subset selection strategies for robust learning to deal with the class imbalance and noisy labels in training data sets and achieved comparable performance to current state-of-the--. robust supervised learning approaches.
4 Experimental Evaluation
Our experiments explicitly aim at answering the following questions,
Q1: Does the proposed ORIENT approach substantially reduce the training time while maintain- ing comparable performance to training complete source dataset?
Q2: Can the proposed ORIENT approach augment the existing domain adaptation approaches?
We evaluate our proposed approach on two domain adaptation datasets: Office-31 42 and Office- Home [53] Office-31 dataset consists of 4110 images of 31 object categories from three different domains: Amazon (A), DSLR (D), and Webcam (W). For our experiments we used all the images from the source domain in the training set (D*) and two examples of each category in the query set (D'), Office-Home dataset consists of 10812 images of 65 object categories from four different domains: Art (A), Clipart (C), Product (P), and Real-world (R). Here, the target data (D') consists of about 20% of the images from the target domain. We use = 20, that is, we sample the subset after every 20 epochs and use 0.3%, that is, we sample 30% of the source domain for training.
To answer the first question, we use ORIENT to train a ResNet50 architecture using the stochastic gradient descent (SGD) algorithm with the momentum of 0.9 and the weight decay ratio of 5e4. We compare our approach against three baselines: Full-traine trainingin on the source data and the target data, random subset of the source dataset and the target set, i.e. R U D R
Le. D U D*; Random- RandomSubset( D*); and CRAIG a coreset of the source dataset, i.e coreset(D'), without any information of the target dataset. We standard categorical cross-entropy loss function (for multi- class classification) and five different instantiations of the submodular mutual information function: Facility Location Mutual Information (ORIENT (FLMT).. Graph Cut Mutual Information (ORIENT (GCM)) Log Determinant Mutual Information (ORIENT (LDMJ),, GLISTER (ORIENT (G)), and GradMatch (ORIENT (GM)).
A D
A +
W
D +
A
D -
W
W + A
W
W
CCSA
Full
0.78
0.72
0.55
0.93
0.55
0.97
Random
0.76
0.72
0.54
0.81
0.54
0.92
ORIENT
0.77
0.76
0.55
0.89
0.55
0.96
d-SNE
Full
0.77
0.69
0.53
0.93
0.54
0.98
Random
0.76
0.68
0.53
0.86
0.53
0.94
ORIENT
0.78
0.71
0.55
0.90
0.56
0.97
Table 2: Test accuracy for office-31 with SDA methods
presents the seatter plot of the speed up against the prediction accuracy of Office-31 dataset Figure 2
using cross-entropy loss.. In all our experiments, use 0.3 fraction of the source data the subset

---

Full
CRAIG ORIENT (G)
ORIENT (GM) ORIENT (FLM))
ORIENT (GCMI)
Random
ORIENT (LDMI)
0.8
0.7
0.6
Accuracy
0.65
Accuracy
0.55
X
0.65
0.6
0.5
0.6
0.55
0.45
0.55
0.5
0.4
2.0
0.5
1.0
2.0
2.5
Speed up
Speed up
Speed up
(a.1) A
(a.2)
A W
(b.1) D
0.95
0.58
Accuracy
2 0.55
Accuracy
0.9
x
0.52
0.85
0.48
0.8
0.75
0.45
0.75
0.7
0.43
1.0
1.5
2.0
2.5
3.0
1.0
2.0
3.0
4.0
5.0
0.5
1.0
1.5
2.0
2,5
Speed up
Speed
Speed up
(b.2) D
(c.1) +
(c.2)
W -
Figure 2: Speed up vs prediction accuracy on three domains of Office31 datasct: Amazon (A), DSLR (D), and
Webcam (W), X -axis represents the speed by the model, i.c. the ratio of the time taken to train on complete source dataset (Full) to the time taken by the model. -axis represents the prediction accuracy of the model on the target domain.
R-
R
C
P- R
P-+
C R
C-+ P
A
P
A
A A
R-+ A
P A
C
A
d-SNE CCSA
Full
0.74
0.55
0.62
0.46
0.56
0.67
0.70
0.64
0.48
0.57
0.49
0.41
Random
0.71
0.62
0.45
0.51
0.66
0.69
0.48
0.47
0.15
ORIENT
0.78
0.54
0.65
0.5
0.61
0.71
0.71
0.65
0.51
0.59
U.54
0.47
Full
0.77
0.53
0.62
0.50
0.60
0.71
0.72
0.63
0.49
0.52
0.44
0.40
Random
0.75
0.50
0.60
0.45
0.57
0.69
0.68
0.59
0.49
0.46
0.43
0.40
ORIENT
0.77
0.52
0.63
0.50
0.71
0.71
0.61
0.51
0.52
0.44
0.42
Table 3: Test accuracy for Office-Homet with SDA methods
and see >> 2.5x speed up. Figure presents the convergence curves of the Office-Home dataset using cross-entropy loss. evident from these charts that our proposed approach ORIENT can achieve better or comparable performance to the Full training in significantly less amount of time, indicating better efficiency. This analysis helps us answer our QI. ORIENT reduces the training time substantially, the speed up achieved is reciprocal to the fraction of the subset used, without trading performance. Rather, some combinations, we observe that ORIENT outperforms Full.
Figure presents the bar plots of the speed up when ORIENT(FLMI) is used in conjunction with d-SNE loss function on Office-31 and Office-Home datasets. Here, we see a consistent 3x speed up as compared to full model training with d-SNE loss. These bar plots demonstrate that our proposed approach ORIENT can augment existing domain adaptation approaches and substantially reduce the training times. Additionally, Tables 2 and 3 present the test accuracy while using d-SNE and CCSA loss conjunction with (FLM)) on Office-31 and Office- Home datasets, respectively. It's evident that augmenting existing domain adaptation approaches with ORIENT achieves better or comparable performance to the Full training. These two observations help us answer our Q2 in the affirmative. Precise values of prediction accuracy and training time for all the experiments are provided in Appendix (A.5). Further, in Appendix Appendix (A.7) and Appendix (A.8), we analyze the effect of different subset sizes and subset sampling frequencies.
7

---

Full
CRAIG ORIENT (G)
ORIENT (GM)
ORIENT (FLM))
Random
0.75
0.5
0.7
0.65
Accuracy
0.4
0.65
Accuracy
0.35
0.55
0.55
0.5
0.5
0.25
0.45
0.2
0.4
0.45
6
10
10
2
Time taken (in hrs)
Time taken (in hrs)
Time taken (in hrs)
(a.1)
A
(a.2)
A ->
(a.3)
A. R
0.45
0.7
0.6
Accuracy
Accuracy
Accuracy
0.55
0.35
0.5
0.5
0.45
0.25
0.4
0.4
0.3
0.35
15
60
10
Time taken (in hrs)
Time taken (in hrs)
Time taken (in hrs)
(b.1) C-A
(b.2) C
(b.3) C- R
0.5
0.5
0.65
Accuracy
Accuracy
0.45
0.4
Accuracy
0.6
0.4
0.35
0.55
0.35
0.3
0.25
W/
0.5
0.25
0.2
15
20
25
10
30 40
60
15 20 30
Time taken (in hrs)
Time taken (in hrs)
Time taken (in hrs)
(c.1) P
(c.2) P
(c.3) P R
0.55
0.5
0.75
Accuracy
0.445
Accuracy
0.7
0.4
0.45
0.65
0.6
0.4
0.3
0.55
0.25
10 15
20
25
10
20
30
40 50 60
10 20 30 40 50 60
Time taken hrs)
Time taken (in hrs)
Time taken (in hrs)
(d.1) R-A
(d.2) R-C
(d.3) R-P
Figure 3: Convergence four domains of Office-Home dataset: (A), Clipart (C), Product (P), and Real World (R). -axis presents the training time in hours and Yaxxis presents the prediction accuracy on the target domain.
Figure presents the GradCam 455 class-activation maps trained models on the Office-Home dataset (P- + R setting) using d-SNE loss for both Full and ORIENT (FLMI). These activation maps show that the model trained with the ORIENT framework learned better class discrimination features than the model trained with Full. This might explain why the ORIENT framework performs better sometimes than Full.
Comparison of different SMI functions: Domain adaptation results Office31 (Figure 2) and OfficeHome (Figure (] datasets show that ORIENT(GCMI) performs suboptimally compared to ORIENT using other SMI functions in terms of target domain accuracy, Although ORIENT(LDMI)

---

Full
Random
ORIENT (FLMI)
UP
3.0
3.0
2.0
2.0
1.0
1.0
0.0
C
R
W
source
target
source
target
Office-31 datasct
(b) Office-Home dataset
Figure 4: Speed up achieved by combining d-SNE with ORIENT
d-SNE Full
(b) d-SNE+ (FLMI)
d-SNE Full
(d) d-SNE (FLMI)
Figure 5: GradCam 44 activation maps of the models learned using d-SNE + Full and d-SNE + ORIENT (FLMI) on the Office-Home dataset with "Product" as the source domain and "Real World" as the target domain. As evidenced by class activation maps, the ORIENT framework enabled the model to learn more effective class discriminative fcatures than Full data training.
achieves reasonable target domain accuracy, it is computationally expensive and does not achieve the best performance-speedup trade-off. In comparison to the ORIENT using remaining SMI functions, i.e. OGI)NT(GM), ORIENT(G), and ORIENT(FLMI), ORIENT(FLMM)) consistently achieves the best performance versus speed-up trade-off. We further present synthetic experiments to provide intuitions on how different SMI functions selects data subsets in Appendix A.6
To summarize, our experiments on two adaptation datasets suggest that our proposed approach ORI- ENT can substantially reduce the training time while maintaining comparable performance training on complete source data. Additionally, our experiments using d-SNE and CCSA loss functions suggest that ORIENT can be used in conjunction with existing SDA methods to achieve significant speed ups in training time, while maintaining or improving the prediction accuracy. In particular, two instantiations of our proposed approach, (FL and ORIENT(GM) consistently achieve comparable or better performance compared to Full training while being ~ 3x faster to train.
5 Conclusion, Limitations, and Broader Impact
We introduce ORIENT, a subset selection framework based on SMI functions for supervised domain adaptation. The submodularity SMI functions allows us use scalable greedy algorithms select the data subsets efficiently. In addition, we demonstrate how ORIENT is a unified framework that integrates previous approaches based on subset selection for robust learning. Empirically, we show that ORIENT is very effective for SDA. Specifically, it achieves ~ 3x speed up over existing SDA approaches like d-SNE and CCSA while achieving comparable or better performance. Our findings confirm that ORIENT has significant social impact by making existing SDA algorithms significantly faster and more energy-efficient, reducing CO2 emissions and energy consumption incurred during training, thus contributing to Green Al [4). The main limitation of our work is that although ORIENT significantly reduces the training time, it requires more memory to store the similarity kernel required for subset selection. This makes running ORIENT harder on devices with low memory.

---

Acknowledgments and Disclosure of Funding
AK acknowledges the support by the NIH grant 201HD101244 HK gratefully acknowledges the support of the ARO award W91 INF2010222 RI and KK would like to acknowledge support from NSF Grant Number IIS-2 2106937, a gift from Google Research, and the Adobe Data Science Research award. Authors would like to acknowledge Dr. Sriraam Natarajan for helpful discussions and support. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ARO, NIH, NSF, Google Research, Adobe Data Science or the U.S. government.
References
[1] D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, S. Kanwal, T. Maharaj, A. Fischer, A. C. Courville, Y. Bengio, and S. Lacoste-Julien. A closer look at memorization in deep networks. In ICML., volume 70, pages 233-242. PMLR, 2017.
[2] J. T. Ash, C. Zhang, A. Krishnamurthy, J. Langford, and A. Agarwal. Deep batch active learning by diverse, uncertain gradient lower bounds. In ICLR, 2020.
[3] A. Axelrod, X. He, and J. Gao. Domain adaptation via pseudo in-domain data selection. In EMNLP. pages 355-362. ACL, 2011.
[4] S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In NIPS, pages 137-144. MIT Press, 2006.
[5] S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Vaughan. A theory of learning from different domains. Machine Learning, 79:151-1755 2010.
[6] A. Bilmes. Submodularity in machine learning and artificial intelligence. CoRR, abs/2202.00132, 2022.
[7] Z. Borsos. M. Mutny, M. Tagliasacchi, and A. Krause. Data summarization via bilevel opti- mization. CoRR, abs/2109. 12534, 2021.
[8] B. Chu, V. Madhavan, O. Beijbom, J. Hoffman, and T. Darrell. Best practices for fine-tuning visual classifiers to new domains. In ECCV Workshops (3), volume 9915 of Lecture Notes in Computer Science, pages 435-442 2016.
[9] D. C. Ciresan, Meier, and Schmidhuber. Multi-column deep neural networks for image classification. In CVPR, pages
IEEE Computer Society, 2012.
[10] D. Feldman. Core-sets: Updated survey. In Sampling Techniques Supervised or Unsupervised Tasks, pages 23 44. Springer, 2020.
[11] S. Fujishige. Submodular functions and optimization. Elsevier, 2005.
[12] Y. Ganin and V. Lempitsky. Unsupervised domain adaptation by backpropagation. In ICML, volume 37 of JMLR Workshop and Conference Proceedings, pages 1 1180-1189. JMLR.org, 2015.
[13] R. Geirhos, C. R. M. Temme, Rauber, H. H. Schüit,, M. Bethge, and F. A. Wichmann. Generalisation in humans and deep neural networks. In NeurIPS, pages 7549-7561, 2018.
[14] R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, pages 580-587. IEEE Computer Society, 2014.
[15] B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic flow kernel for unsupervised domain adaptation. In CVPR, pages 2066-2073. IEEE Computer Society, 2012.
[16] Y. Guo and M. Xiao. Cross language text classification via subspace co-regularized multi-view learning. In JCML. icml.ce/ Omnipress, 2012.
[17] A. Gupta and R. Levin. The online submodular cover problem. In SODA, pages 1552--137. SIAM. 2020.
10

---

[18] K. He, X. Zhang. S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016.
[19] L. Hedegaard, O. A. Sheikh-Omar, and A. Iosifidis. Supervised domain adaptation: A graph embedding perspective and a rectified experimental protocol. IEEE Trans. Image Process., 30: 8619-8631, 1631, 2021.
[20] J. R. Hershey, S. Rennie, P. A. Olsen, and T. T. Kristjansson. Super-human multi-talker speech recognition: A graphical modeling approach. Comput. Speech Lang., 24(1):45-66, 2010.
[21] R. K. lyer, N. Khargonkar, J. A. Bilmes, and H. Asnani. Generalized submodular information measures:
Theoretical properties, examples, optimization algorithms, and applications. lEEE Trans. Inf. Theory, 68(2):752-788 2022.
[22] V. Kaushal, R. K. Iyer, S. Kothawade, R. Mahadev, K. Doctor, and G. Ramakrishnan. Learning from less data: A unified data subset selection and actíve learning framework for computer vision. WACV, pages 1289-1299. IEEE, 2019.
[23] K. Killamsetty, D. Bhat, G. Ramakrishnan, and R. Iyer. CORDS: COResets and Data Subset selection for Efficient Learning, March 2021. URL http:::/tgttiiieii com/écille cords
[24] K. Killamsetty, D. Sivasubramanian, G. Ramakrishnan, A. De, and R. K. Iyer. GRAD-MATCH: gradient matching based data subset selection for efficient deep model training. In ICML, volume 139, pages 5464-5474. PMLR, 2021.
[25] K. Killamsetty, D. Sivasubramanian, G. Ramakrishnan, and R. K. Iyer. GLISTER: generalization based data subset selection for efficient and robust learning. In AAAI, pages 8110-8118. AAAT Press, 2021.
[26] K. Killamsetty, X. Zhao. F. Chen, and R. K. Iyer. RETRIEVE: coreset selection for efficient and robust semi- supervisd learning. In NeurlPS, pages 145811 2021.
[27] K. Killamsetty, G. S. Abhishek, Aakriti, A. V. Evfimievski, L. Popa. G. Ramakrishnan, and R. Iyer. AUTOMATA: Gradient based data subset selection for compute-efficiet hyper- parameter tuning, 2022. /Rtps::::///rxxi org hrgg/abs//0 2/////233.001
[28] K. Kirchhoff and J. Bilmes. Submodularity for data selection in machine translation. In EMNLP, pages 131-141, ACL, 2014.
[29] Koniusz, Y. Tas, and F. Porikli. Domain adaptation by mixture of alignments of second-or higher-order scatter tensors. In CVPR, pages 7139-7148. IEEE Computer Society, 2017.
[30] S. Kothawade, N. Beck, K. Killamsetty, and R. K. Iyer. SIMILAR: submodular information measures based active learning in realistic scenarios. In NeurIPS, pages 18685-18697, 2021.
[31] S. Kothawade, V. Kaushal, G. Ramakrishnan, J. A. Bilmes, and R. K. Iyer. PRISM: rich class of parameterized submodular information measures for guided subset selection. AAAI, 36, 2022.
[32] M. Liu and O. Tuzel. Coupled generative adversarial networks. In NeurIPS, pages 469-477, 2016.
[33] L. Lovász. Submodular functions and convexity. ISMP, pages 235-257. Springer, 1982.
[34] M. Minoux. Accelerated greedy algorithms for maximizing submodular set functions. In Optimization techniques, pages 234-243. Springer, 1978.
[35] B. Mirzasoleiman, A. Bilmes, and J. Leskovec. Coresets for data-efficient training of machine learning models. In /CML, volume 119, pages 6950-6960. PMLR, 2020.
[36] B. Mirzasoleiman, K. Cao, and J. Lekovee. Coresets for robust training of neural networks against noisy labels. Advances in Neural Information Processing Systems, 33, 2020.

---

[37] L. H. Morsing. O. A. Sheikh-Omar, and A. Iosifidis. Supervised domain adaptation using graph embedding. In ICPR, pages 7841-7847. IEEE, 2020.
[38] Motiaan, Q. Jones, M. Iranmanesh, and G. Doretto. Few-shot adversarial domain adaptation. In NIPS, pages 6670-6680, 2017.
[39] S. Motiian, M. Piccirilli, D. A. Adjeroh, and G. Doretto. Unified deep supervised domain adaptation and generalization. In ICCV, pages 5716-5722 5726. IEEE Computer Society, 2017.
[40] K. Muandet, D. Balduzzi, and B. Schölkopf. Domain generalization via invariant feature representation. In ICML. volume 28 of JMLR Workshop and Conference Proceedings, pages 10-18. JorR.org, 2013.
[41] V. M. Patel. R. Gopalan, R. Li, and R. Chellappa. Visual domain adaptation: A survey of recent advances. IEEE Signal Process. Mag., 32(3):53-69, 2015.
[42] K. Saenko, Kulis, M. Fritz, and T. Darrell. Adapting visual category models new domains. In ECCV (4), volume 6314 of Lecture Notes in Computer Science, pages 213-226. Springer, 2010.
[43] K. Saito, D. Kim, S. Sclaroff, T. Darrell, and K. Saenko. Semi-supervised domain adaptation via minimax entropy. ICCV, 2019.
[44] R. Schwartz, J. Dodge, N. Smith, and O. Etzioni. Green ai. Communications of the ACM, 63:54 63, 2020.
[45] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV, pages 618-626. IEEE Computer Society, 2017.
[46] O. Sener and S. Savarese. Active learning for convolutional neural networks: A core-set approach. ICLR. OpenReview.net, 2018.
[47] H. Shimodaira. Improving predictive inference under covariate shift by weighting the log- likelihood function. Journal of statistical planning and inference, 90(2):227-244, 2000.
[48] A. Singh. CLDA: contrastive learning for semi-supervised domain adaptation. In NeurIPS, pages 8089-5101, 2021.
[49] R. Tiwari, K. Killamsetty, R. lyer, and P. Shenoy. GCR: Gradient coreset hased replay buffer selection for continual learning, 2021.
[50] A. Torralba and A. A. Efros. Unbiased look dataset bias. In CVPR, pages 1521-1528. IEEE Computer Society, 2011.
[51] E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko. Simultaneous deep transfer across domains and tasks. ICCV, pages 4068 4076. IEEE Computer Society, 2015.
[52] E. Tzeng, Hoffman, Saenko, and Darrell. Adversarial discriminative domain adaptation. In CVPR, pages 2962-2971. IEEE Computer Society, 2017.
[53] H. Venkateswara, J. Eusebio. S. Chakraborty, and S. Panchanathan. Deep hashing network for unsupervised domain adaptation. In CVPR, pages 5385-5394, IEEE Computer Society, 2017.
[54] M. Wang and W. Deng. Deep visual domain adaptation: A survey. Neurocomputing, 312: 135-153, 2018.
[55] K. Wei, Y. Liu, K. Kirchhoff, C. D. Bartels, and A. Bilmes. Submodular subset selection for large-scale speech training data. In ICASSP, pages 3311-3315. IEEE, 2014.
[56] K. Wei, Y. Liu, K. Kirchhoff, and J. A. Bilmes. Unsupervised submodular subset selection for speech data. ICASSP, pages 4107-4111 IEEE, 2014.
[57] K. Wei, R. K. Tyer, and J. A. Bilmes. Submodularity data subset selection and active learning. In ICML, volume 37, pages 1954-1963. JMLR.org, 2015.
12

---

[58] X. Xu, X. Zhou, R. Venkatesan, G. Swaminathan, and O. Majumder. d-sne: Domain adaptation using stochastic neighborhood embedding. In CVPR, pages 2497-2506. Computer Vision Foundation / IEEE, 2019.
[59] T. Yao, Y. Pan, C. Ngo, H. Li, and T. Mei. Semi-supervised domain adaptation with subspace learning for visual recognition. In CVPR, pages 2142-2150. IEEE Computer Society, 2015.
[60] Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In NeurlPS, pages 3320-3322 2014.
13


Reviews:
[{"review_id": "ewd_Rry8xa_", "reviewer": "Reviewer_HJ5G", "summary": "For SDA (Supervised Domain Adaptation), authors propose a framework to select a subset from source domain data so that the training process can be speeded up and better performance can be achieved.", "questions": "Can this framework be extended to UDA?\n", "limitations": "Stated in weaknesses.\n", "rating": 7, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n- The problem setting is novel, interesting and realistic. It is great to see ideas focusing on utilizing data instead of building fancy models which provides marginal improvement of performance. \n\n- Comprehensive experiments are conducted to demonstrate the effectiveness of this framework. \n\n- The paper is well-written and presented. \n\nWeaknesses:\n\n- The calculation of the similarity matrix is quite straightforward. Can it be learned or calculated more properly?", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "NrCjoq3oK73", "reviewer": "Reviewer_SGGh", "summary": "It is proposed to speed up the existing SDA methods by selecting a subset of the source dataset that is highly \"similar\" to the target dataset and training only using the source subset. Since model training is performed on smaller set (given budget), the computational effort is lessened. Also, since the subset is more aligned to the target SDA is explicitly performed.\n\nSubset similarity is measured through a SMI, with pairwise data similarity defined as cosine similarity of the gradient of loss at the points wrt. the model parameter. \n\nThe overall algorithm alternates between loss optimization (training/learning) and optimal subset selection under SMI. \n\nUsing simulations on benchmarks it is argued that computational efficiency is achieved without much compromise on accuracy.", "questions": "1. How does the initialized subset effect the final accuracy? Can this be experimentally studied via simulations?\n\n2. Since the initialization of subset is random, reporting mean and std div over multiple initializations seems a better way of presenting the results.\n", "limitations": "Memory limitation is mentioned. However some of the above written limitations can be discussed in more detail.", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The basic strategy of restricting training to merely a subset inherently seems to have some limitations:\n\n1. The valuable information in the complement of the selected subset is completely ignored.\n2. The extent of complement's usefulness is not only determined by its similarity to the target set. Perhaps, the complement can be used in other subtle ways: for example training domain-invariant features etc.\nHowever, such aspects and corresponding trade-off have not been discussed.\n\nSince the proposed methodology explicitly performs SDA, it seems very critical to empirically compare against few state-of-the-art SDA approaches. Especially, those which are scalable. However, the simulations do not seem to compare even wrt one SDA method. the baselines are simply other alternative subset methods like random/core, and training with full dataset (which is also NOT an SDA method). Without these critical comparisons the practical usefulness of the method cannot be established.\n\n----------\nrebuttal resolved some concerns. thanks.", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "G755t0M9kQA", "reviewer": "Reviewer_mSi3", "summary": "Authors propose to employ submodular mutual information measures for dataset selection in domain adaptation. Starting with a random subset of data, the proposed algorithm iterates between training on the currently selected dataset and selecting a new dataset based on submodular measure and gradient similarity. Authors claim that the proposed algorithm is much more efficient than full-data training. When combined with d-SNE, ORIENT outperforms full-data training baseline on three adaptation directions and underperforms on three directions, out of 12 directions.", "questions": "How do authors compute speedup in Figure 4? I am suspecting authors are fixing the number of passes on the dataset, because speed-up numbers in Figure 4 are very uniform. If so, then I am not sure it is fair to say ORIENT achieves 3x speed-up. FULL could've achieved performance similar with ORIENT when the number of gradient descent steps are the same. Therefore, speed-up should be computed based on how much time it takes to achieve a target performance level.\n\nCan authors provide some discussion on what are main ideas behind proposed SMIs, why they were chosen, what are their conceptual pros & cons, and how do experimental results validate/invalidate these intuitions & assumptions?\n\nHow should the size of selected dataset chosen? Can we benefit from more or less frequent dataset selection than authors currently do?\n\nShouldn't authors compare against dataset selection baselines? Authors' main claim is that their proposed SDA algorithms provide 3x speedup, but maybe we can achieve 3x speedup alternatively by just employing dataset selection on the full data without considering alignment with target domain data.", "limitations": "Domain Adaptation via Pseudo In-Domain Data Selection (Axelrod et al, https://aclanthology.org/D11-1033.pdf ) should've been cited, as this paper proposes data selection for domain adaptation. They don't use submodular functions for selection, and they rely on unsupervised model (language model) for selection, so it'd be difficult for authors to compare against Axelrod et al. However, such an experiment would certainly make the paper stronger, by including additional task (machine translation) and comparing against non-submodular baseline (authors don't compare against any non-submodular baseline).\n\nIn line 118, authors do not assume monotonicity of submodular function when discussing (1-1/e) approximation guarantee of simple greedy algorithm.", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Quality, Significance: The paper builds upon well-established techniques such as submodular mutual information and submodular optimization, which makes the proposed method principled. For example, this allows the selection part of the algorithm to borrow well-known algorithms in submodular optimization literature, and provide approximation guarantee. The proposed framework is also quite general, which will allow future researchers to implement novel algorithms based on it, for example by proposing new similarity measures or mutual information metrics.\n\nClarity: Authors employ the abstract framework of submodular mutual information, which allows authors to describe their proposed method in high-level. This makes the narrative of the paper quite clear, but this also makes the paper a bit superficial in some of the technical details. For example, it is unclear how four SMIs proposed in this paper have advantages and disadvantages against others. Even intuitive ideas behind these SMIs are not discussed in the main body of the paper. Neither does experiment section discuss how SMIs compare against each other.\n\nQuality: The main contribution of this paper is mostly empirical, as SMIs have been established in prior papers. However, I am not sure  empirical results are very convincing. In Figure 3, Full data selection seems to be quite competitive in most of the adaptation directions even from the first evaluation. Also, there seems to be quite a bit of random errors in this plot, which makes me not sure we can conclude anything other than ORIENT (G), Random, and CRAIG being much worse than others. Results in Table 2 & 3 are mixed. In Table 2, Full outperforms ORIENT 3/6 directions, and ties with ORIENT on 2/6. In table 3, FULL outperforms on 3/12, and ORIENT outperforms on 3/12. Hence we can't really say ORIENT performs better. The only benefit is the reduction of training time, but it is unclear how authors computed the speedup. Therefore, I am not sure ORIENT would be actually computationally more efficient than FULL. ", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "7DRhIN19NLf", "reviewer": "Reviewer_dAxe", "summary": "This paper introduces ORIENT, a novel subset selection framework to speed up learning in supervised domain adaptation (SDA). Based on submodular mutual information (SMI) functions, ORIENT selects a small diverse subset of training data in the source domain that is similar to data in the target domain. The authors show that ORIENT generalizes the known SDA methods such as GLISTER, GRADMATCH, and a variant of CRAIG. Numerical experiments demonstrated that ORIENT successfully reduces the learning time while achieving the accuracy same as baseline methods.", "questions": "- In Algorithm 1, line 4, $\\theta$ is updated as $\\theta \\gets \\arg\\min \\mathcal{L}(A)$. Isn't it a single or a few (stochastic) gradient steps? Otherwise, you get the same $\\theta$ for $L$ times until $A$ is changed.", "limitations": "N/A", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "- *Originality*: Novel. This paper unifies the several known methods with SMI functions. To the best of my knowledge, this seems to be the first study in SDA from the viewpoint of SMI functions.\n- *Quality*: Good. The presented results seem sound.\n- *Clarity*: While I spotted a few typos in the manuscript, the writing is mostly clear.\n- *Significance*: This paper has several interesting contributions. I particularly like that the several known methods in SDA are special cases of ORIENT with different submodular functions. The proposed framework can be applied to a wide variety of machine learning models including neural nets, as long as the (stochastic) gradient descent is possible. So adapting ORIENT in the existing ML framework (like PyTorch or TensorFlow) seems quite easy. Furthermore, numerical experiments demonstrate that the proposed method indeed reduces the learning time: sometimes 2-3x speed-ups. I believe that ORIENT is useful and valuable for practitioners.", "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept