GenAI4ELab/papercli-papers huggingface dataset:
SYNG4ME: Model Evaluation using Synthetic Test Data
Authors: Boris van Breugel; Nabeel Seedat; Fergus Imrie; Mihaela van der Schaar
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Model evaluation is a crucial step in ensuring reliable machine learning systems. Currently, predictive models are evaluated on held-out test data, quantifying aggregate model performance. Limitations of available test data make it challenging to evaluate model performance on small subgroups or when the environment changes. Synthetic test data provides a unique opportunity to address this challenge; instead of evaluating predictive models on real data, we propose to use synthetic data. This brings two advantages. First, supplementing and increasing the amount of evaluation data can lower the variance of model performance estimates compared to evaluation on the original test data. This is especially true for local performance evaluation in low-density regions, e.g. minority or intersectional groups. Second, generative models can be conditioned as to induce a shift in the synthetic data distribution, allowing us to evaluate how supervised models could perform in different target settings. In this work, we propose SYNG4ME: an automated suite of synthetic data generators for model evaluation. By generating smart synthetic data sets, data practitioners have a new tool for exploring how supervised models may perform on subgroups of the data, and how robust methods are to distributional shifts. We show experimentally that SYNG4ME achieves more accurate performance estimates compared to using the test data alone.
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Authors: Shiwei Liu; Tianlong Chen; Zhenyu Zhang; Xuxi Chen; Tianjin Huang; AJAY KUMAR JAISWAL; Zhangyang Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e.g., low complexity, high scalability, and robustness), sometimes even better than the original dense networks. As research effort is focused on developing increasingly sophisticated sparse algorithms, it is startling that a comprehensive benchmark to evaluate the effectiveness of these algorithms has been highly overlooked. In absence of a carefully crafted evaluation benchmark, most if not all, sparse algorithms are evaluated against fairly simple and naive tasks (eg. CIFAR-10/100, ImageNet, GLUE, etc.), which can potentially camouflage many advantages as well unexpected predicaments of SNNs. In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce “Sparsity May Cry” Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge. Our systemic evaluation of the most representative sparse algorithms reveals an important obscured observation: the state-of-the-art magnitude- and/or gradient-based sparse algorithms seemingly fail to perform on SMC-Bench when applied out-of-the-box, sometimes at significantly trivial sparsity as low as 5%. The observations seek the immediate attention of the sparsity research community to reconsider the highly proclaimed benefits of SNNs. We further conduct a thorough investigation into the reasons for the failure of common SNNs. Our analysis points out that such failure is intimately related to the “lazy regime” of large model training, which hints us with stronger pruning recipes that alleviate the failure on SMC-Bench (though still more or less suffering). By incorporating these well-thought and diverse tasks, SMC-Bench is designed to favor and encourage the development of more scalable and generalizable sparse algorithms. We open-source SMC-Bench to assist researchers in building next-generation sparse algorithms that scale and generalize: https://github.com/VITA-Group/SMC-Bench.
Fooling SHAP with Stealthily Biased Sampling
Authors: gabriel laberge; Ulrich Aïvodji; Satoshi Hara; Mario Marchand; Foutse Khomh
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus 
a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired 
explanations. However, existing attacks focus solely on altering the black-box model itself. In this paper, we propose a complementary family 
of attacks that leave the model intact and manipulate SHAP explanations using stealthily biased sampling of the data points used to approximate expectations w.r.t the background distribution. In the context of fairness audit, we show that our attack can reduce the importance of a sensitive feature when explaining the difference in outcomes between groups while remaining undetected. More precisely, experiments performed on real-world datasets showed that our attack could yield up to a 90\% relative decrease in amplitude of the sensitive feature attribution. These results highlight the manipulability of SHAP explanations and encourage auditors to treat them with skepticism.
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Authors: Alessio Gravina; Davide Bacciu; Claudio Gallicchio
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes. However, DGNs are typically limited in their ability to propagate and preserve long-term dependencies between nodes, i.e., they suffer from the over-squashing phenomena. As a result, 
we can expect them to under-perform, since different problems require to capture interactions at different (and possibly large) radii in order to be effectively solved. In this work, we present Anti-Symmetric Deep Graph Networks (A-DGNs), a framework for stable and non-dissipative DGN design, conceived through the lens of ordinary differential equations. We give theoretical proof that our method is stable and non-dissipative, leading to two key results: long-range information between nodes is preserved, and no gradient vanishing or explosion occurs in training. We empirically validate the proposed approach on several graph benchmarks, showing that A-DGN yields to improved performance and enables to learn effectively even when dozens of layers are used.
Object Tracking by Hierarchical Part-Whole Attention
Authors: Jinkun Cao; Jiangmiao Pang; Xinshuo Weng; Rawal Khirodkar; Kris M. Kitani
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We present in this paper that hierarchical representations of objects can provide an informative and low-noisy proxy to associate objects of interest in multi-object tracking. This is aligned with our intuition that we usually only need to compare a little region of the body of target objects to distinguish them from other objects. We build the hierarchical representation in levels of (1) target body parts, (2) the whole target body, and  (3) the union area of the target and other objects of overlap.  Furthermore, with the spatio-temporal attention mechanism by transformer, we can solve the tracking in a global fashion and keeps the process online.  We design our method by combining the representation with the transformer and name it Hierarchical Part-Whole Attention, or HiPWA for short. The experiments on multiple datasets suggest its good effectiveness.  Moreover, previous methods mostly focus on leveraging transformers to exploit long temporal context during association which requires heavy computation resources. But HiPWA focuses on a more informative representation of objects on every single frame instead. So it is more robust with the length of temporal context and more computationally economic.
Toeplitz Neural Network for Sequence Modeling
Authors: Zhen Qin; Xiaodong Han; Weixuan Sun; Bowen He; Dong Li; Dongxu Li; Yuchao Dai; Lingpeng Kong; Yiran Zhong
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various sequence modeling tasks, which rely on attention to capture pairwise token relations, and position embedding to inject positional information. While showing good performance, the transformer models are inefficient to scale to long input sequences, mainly due to the quadratic space-time complexity of attention. To overcome this inefficiency, we propose to model sequences with a relative position encoded Toeplitz matrix and use a Toeplitz matrix-vector production trick to reduce the space-time complexity of the sequence modeling to log linear. A lightweight sub-network called relative position encoder is proposed to generate relative position coefficients with a fixed budget of parameters, enabling the proposed Toeplitz neural network to deal with varying sequence lengths. In addition, despite being trained on 512-token sequences, our model can extrapolate input sequence length up to 14K tokens in inference with consistent performance. Extensive experiments on autoregressive and bidirectional language modeling, image modeling, and the challenging Long-range Arena Benchmark show that our method achieves better performance than its competitors in most downstream tasks while being significantly faster.
DITTO: Offline Imitation Learning with World Models
Authors: Branton DeMoss; Paul Duckworth; Nick Hawes; Ingmar Posner
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose DITTO, a fully offline approach to imitation learning which addresses the problem of covariate shift without access to an oracle or any additional online interactions. By unrolling agent policies in the latent space of a learned world model and penalizing drift from expert demonstrations, we can use online reinforcement learning algorithms to learn policies which solve the imitation objective, without access to the underlying environment or reward function. Decoupling policy and world model learning lets us leverage datasets of any quality to learn latent representations which provide a natural reward signal for imitation learning, avoiding the need for complex adversarial or sparse imitation-inducing rewards. Compared to competitive baselines, our method achieves state-of-the-art performance in a variety of challenging environments from pixel observations alone.
Efficient Hyperparameter Optimization Through Tensor Completion
Authors: Aaman Rebello; Kriton Konstantinidis; Yao Lei Xu; Danilo Mandic
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Hyperparameter optimization is a prerequisite for state-of-the-art performance in machine learning, with current strategies including Bayesian optimisation, Hyperband, and evolutionary methods. Whereas such methods have been shown to improve performance, none of these is designed to explicitly take advantage of the underlying data structure.  To this end, we introduce a completely different approach for hyperaparameter optimization, based on low-rank tensor completion. This is achieved by first forming a multi-dimensional tensor which comprises performance scores for different combinations of hyperparameters. Based on the realistic underlying assumption that the so-formed tensor has a low rank structure, this then allows for reliable estimates of the unobserved validation scores of combinations of hyperparameters to be obtained through tensor completion, and from only a fraction of known elements. Through extensive experimentation on various datasets and learning models, the proposed method is shown to exhibit competitive or superior performance to the state-of-the-art hyperparameter optimization strategies. Distinctive advantages of the proposed method include its ability to simultaneously handle any hyperparameter type (e.g., kind of optimizer, number of neurons,  number of layer, etc.), its relative simplicity compared to the competing methods, as well as the ability to suggest multiple optimal combinations of hyperparameters.
A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet
Authors: Ido Galil; Mohammed Dabbah; Ran El-Yaniv
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: When deployed for risk-sensitive tasks, deep neural networks must be able to detect instances with labels from outside the distribution for which they were trained.
In this paper we present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution instances
(i.e., instances whose true labels do not appear in the training distribution) at various levels of detection difficulty.
We apply this technique to ImageNet, and benchmark 525 pretrained, publicly available, ImageNet-1k classifiers. 
The code for generating a benchmark for any ImageNet-1k classifier, along with the benchmarks prepared for the above-mentioned 525 models is available at https://github.com/mdabbah/COOD_benchmarking.

The usefulness of the proposed framework and its advantage over alternative existing benchmarks is demonstrated by analyzing the results obtained for these models, which reveals numerous novel observations including:
(1) knowledge distillation consistently improves class-out-of-distribution (C-OOD) detection performance; (2) a subset of ViTs performs better C-OOD detection than any other model; (3) the language–-vision CLIP model achieves good zero-shot detection performance, with its best instance outperforming 96% of all other models evaluated; (4) accuracy and in-distribution ranking are positively correlated to C-OOD detection; and 
(5) we compare various confidence functions for C-OOD detection.
Our companion paper, also published in ICLR 2023 (What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers), examines the uncertainty estimation performance (ranking, calibration, and selective prediction performance) of these classifiers in an in-distribution setting.
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
Authors: Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Om Thakkar; Abhradeep Guha Thakurta
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: All state-of-the-art (SOTA) differentially private machine learning (DP ML) methods are iterative in nature, and their privacy analyses allow publicly releasing the intermediate training checkpoints. However, DP ML benchmarks, and even practical deployments, typically use only the final training checkpoint to make predictions. In this work, for the first time, we comprehensively explore various methods that aggregate intermediate checkpoints to improve the utility of DP training. Empirically, we demonstrate that checkpoint aggregations provide significant gains in the prediction accuracy over the existing SOTA for CIFAR10 and StackOverflow datasets, and that these gains get magnified in settings with periodically varying training data distributions.  For instance, we improve  SOTA StackOverflow accuracies to 22.7\% (+0.43\% absolute) for $\epsilon=8.2$, and 23.84\%  (+0.43\%) for $\epsilon=18.9$. Theoretically, we show that uniform tail averaging of checkpoints improves the empirical risk minimization bound compared to the last checkpoint of DP-SGD. Lastly, we initiate an exploration into estimating the uncertainty that DP noise adds in the predictions of DP ML models. We prove that, under standard assumptions on the loss function, the sample variance from last few checkpoints provides a good approximation of the variance of the final model of a DP run. Empirically, we show that the last few checkpoints can provide a reasonable lower bound for the variance of a converged DP model.
Benchmarking Algorithms for Domain Generalization in Federated Learning
Authors: Ruqi Bai; Saurabh Bagchi; David I. Inouye
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we present a unified platform to study domain generalization in the federated learning (FL) context and conduct extensive empirical evaluations of the current state-of-the-art domain generalization algorithms adapted to FL. In particular, we perform a fair comparison of nine existing algorithms in solving domain generalization {either centralized domain generalization algorithms adapted to the FL context or existing FL domain generalization algorithms } to comprehensively explore the challenges introduced by FL. These challenges include statistical heterogeneity among clients, the number of clients, the number of communication rounds, etc. The evaluations are conducted on three diverse datasets including PACS (image dataset covering photo, sketch, cartoon, and painting domains), iWildCam (image dataset with 323 domains), and Py150 (natural language processing dataset with 8421 domains). The experiments show that the challenges brought by federated learning stay unsolved in the realistic experiment setting. Furthermore, the code base supports fair and reproducible new algorithm evaluation with easy implementation.
AGRO: Adversarial discovery of error-prone Groups for Robust Optimization
Authors: Bhargavi Paranjape; Pradeep Dasigi; Vivek Srikumar; Luke Zettlemoyer; Hannaneh Hajishirzi
Venue: ICLR 2023 (ICLR 2023 poster)

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.
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding 
Authors: Abdullah Hamdi; Silvio Giancola; Bernard Ghanem
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the point level. To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points. This novel 3D Voint cloud representation combines the compactness of 3D point cloud representation with the natural view-awareness of multi-view representation. Naturally, we can equip this new representation with convolutional and pooling operations. We deploy a Voint neural network (VointNet) to learn representations in the Voint space. Our novel representation achieves state-of-the-art performance on 3D classification, shape retrieval, and robust 3D part segmentation on standard benchmarks ( ScanObjectNN, ShapeNet Core55, and ShapeNet Parts). Further analysis shows that VointNet improves the robustness to occlusion compared to other methods.
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
Authors: Chenhongyi Yang; Jiarui Xu; Shalini De Mello; Elliot J. Crowley; Xiaolong Wang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We present the Group Propagation Vision Transformer (GPViT): a novel non- hierarchical (i.e. non-pyramidal) transformer model designed for general visual recognition with high-resolution features. High-resolution features (or tokens) are a natural fit for tasks that involve perceiving fine-grained details such as detection and segmentation, but exchanging global information between these features is expensive in memory and computation because of the way self-attention scales. We provide a highly efficient alternative Group Propagation Block (GP Block) to exchange global information. In each GP Block, features are first grouped to- gether by a fixed number of learnable group tokens; we then perform Group Propagation where global information is exchanged between the grouped fea- tures; finally, global information in the updated grouped features is returned back to the image features through a transformer decoder. We evaluate GPViT on a variety of visual recognition tasks including image classification, semantic seg- mentation, object detection, and instance segmentation. Our method achieves significant performance gains over previous works across all tasks, especially on tasks that require high-resolution outputs, for example, our GPViT-L3 out- performs Swin Transformer-B by 2.0 mIoU on ADE20K semantic segmentation with only half as many parameters. Code and pre-trained models are available at https://github.com/ChenhongyiYang/GPViT.
Contextual Subspace Approximation with Neural Householder Transforms
Authors: Kerrick Johnstonbaugh; Michael Przystupa; Jacob Keller; Martin Jagersand
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Choosing an appropriate action representation is an integral part of solving robotic manipulation problems. Published approaches include latent action models which compress the control space into a low dimensional manifold. These involve training a conditional autoencoder, where the current observation and a low-dimensional action are passed through a neural network decoder to compute high dimensional actuation commands. Such models can have a large number of parameters, and can be difficult to interpret from a user perspective. In this work, we propose that similar performance gains in robotics tasks can be achieved by restructuring the neural network to map observations to a basis for a context-dependent linear actuation subspace. This results in an action interface wherein a user’s actions determine a linear combination of a state-conditioned actuation basis. We introduce the Neural Householder Transform (NHT) as a method for computing this basis. Our results show that reinforcement learning agents trained with NHT in kinematic manipulation and locomotion environments tend to be more robust to hyperparameter choice and achieve higher final success rates compared to agents trained with alternative action representations. NHT agents outperformed agents trained with joint velocity/torque actions, agents trained with an SVD actuation basis, and agents trained with a LASER action interface in the WAMWipe, WAMGrasp, and HalfCheetah environments.