GenAI4ELab/papercli-papers huggingface dataset:
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
Authors: Siyuan Huang; Bo Zhang; Botian Shi; Peng Gao; Tao Chen; Hongsheng Li; Yikang LI
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In recent years, research on zero-shot domain adaptation, namely Domain Generalization (DG), which aims to adapt a well-trained source domain model to unseen target domains without accessing any target sample, has been fast-growing in the 2D image tasks such as classification and object detection. However, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with irregular point data structures and uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we focus on the 3D DG problem, and propose a Single-dataset Unified Generalization (SUG) framework that only leverages the source domain data to alleviate the unforeseen domain differences faced by the well-pretrained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method that can constrain the learned representations to be domain-agnostic and discriminative, by performing a multi-grained feature alignment process between the splitted sub-domains from the single source dataset. Then, a Sample-level Domain-aware Attention (SDA) strategy is presented, which can selectively enhance easy-to-adapt samples from different sub-domains according to the sample-level inter-domain distance, to avoid the negative transfer. Extensive experiments are conducted on three common 3D point cloud benchmarks. The experimental results demonstrate that SUG framework is effective to boost the model generalization ability for unseen target domains, even outperforming the existing unsupervised domain adaptation methods that have to access extensive target domain data, where we significantly improve classification accuracy by 7.7% on ModelNet-to-ScanNet setting and 2.3% on ShapeNet-to-ScanNet setting. Our code will be available.
RLSBench: A Large-Scale Empirical Study of Domain Adaptation Under Relaxed Label Shift
Authors: Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite the emergence of principled methods for domain adaptation under label shift (where only the class balance changes), the sensitivity of these methods to natural-seeming covariate shifts remains precariously underexplored. Meanwhile, popular deep domain adaptation heuristics, despite showing promise on benchmark datasets, tend to falter when faced with shifts in the class balance. Moreover, it's difficult to assess the state of the field owing to inconsistencies among relevant papers in evaluation criteria, datasets, and baselines. In this paper, we introduce \textsc{RLSbench}, a large-scale benchmark for such \emph{relaxed label shift} settings, consisting of 11 vision datasets spanning $>$200 distribution shift pairs with different class proportions. We evaluate 12 popular domain adaptation methods, demonstrating a more widespread susceptibility to failure under extreme shifts in the class proportions than was previously known. We develop an effective meta-algorithm, compatible with most deep domain adaptation heuristics, that consists of the following two steps: (i) \emph{pseudo-balance} the data at each epoch; and (ii) adjust the final classifier with (an estimate of) target label distribution. Furthermore, we discover that batch-norm adaption of a model trained on source with aforementioned corrections offers a strong baseline, largely missing from prior comparisons. We hope that these findings and the availability of \textsc{RLSbench} will encourage researchers to include rigorously evaluate proposed methods in relaxed label shift settings. Code is publicly available at https://github.com/ICLR2023Anon.
Online black-box adaptation to label-shift in the presence of conditional-shift
Authors: Faruk Ahmed; Aaron Courville
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We consider an out-of-distribution setting where trained predictive models are deployed online in new locations (inducing conditional-shift), such that these locations are also associated with differently skewed target distributions (label-shift). While approaches for online adaptation to label-shift have recently been discussed by Wu et al. (2021), the potential presence of concurrent conditional-shift has not been considered in the literature, although one might anticipate such distributional shifts in realistic deployments. In this paper, we empirically explore the effectiveness of online adaptation methods in such situations on three synthetic and two realistic datasets, comprising both classification and regression problems. We show that it is possible to improve performance in these settings by learning additional hyper-parameters to account for the presence of conditional-shift by using appropriate validation sets.
A Large Scale Sample Complexity Analysis of Neural Policies in the Low-Data Regime
Authors: Ezgi Korkmaz
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The progress in reinforcement learning algorithm development is at one of its highest points starting from the initial study that enabled sequential decision making from high-dimensional observations. Currently, deep reinforcement learning research has had quite recent breakthroughs from learning without the presence of rewards to learning functioning policies without even knowing the rules of the game. In our paper we focus on the trends currently used in deep reinforcement learning algorithm development in the low-data regime. We theoretically show that the performance profiles of the algorithms developed for the high-data regime do not transfer to the low-data regime in the same order. We conduct extensive experiments in the Arcade Learning Environment and our results demonstrate that the baseline algorithms perform significantly better in the low-data regime compared to the set of algorithms that were initially designed and compared in the large-data region.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Authors: Gabriele Corso; Hannes Stärk; Bowen Jing; Regina Barzilay; Tommi S. Jaakkola
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Predicting the binding structure of a small molecule ligand to a protein---a task known as molecular docking---is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DiffDock maintains significantly higher precision (21.7%). Finally, DiffDock has fast inference times and provides confidence estimates with high selective accuracy.
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Authors: Jiaqi Guan; Wesley Wei Qian; Xingang Peng; Yufeng Su; Jian Peng; Jianzhu Ma
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Rich data and powerful machine learning models allow us to design drugs for a specific protein target <em>in silico</em>. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitly modeled. However, current 3D target-aware models either rely on the voxelized atom densities or the autoregressive sampling process, which are not equivariant to rotation or easily violate geometric constraints resulting in unrealistic structures. In this work, we develop a 3D equivariant diffusion model to solve the above challenges. To achieve target-aware molecule design, our method learns a joint generative process of both continuous atom coordinates and categorical atom types with a SE(3)-equivariant network. Moreover, we show that our model can serve as an unsupervised feature extractor to estimate the binding affinity under proper parameterization, which provides an effective way for drug screening. To evaluate our model, we propose a comprehensive framework to evaluate the quality of sampled molecules from different dimensions. Empirical studies show our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining.
ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection
Authors: Chenyang ZHAO; Antoni B. Chan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We propose the Gradient-weighted Object Detector Activation Mapping (Grad-ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, Grad-ODAM produces heat maps that show the influence of regions on the detector's decision. Compared to previous classification activation mapping works, Grad-ODAM generates instance-specific explanations rather than class-specific ones. We show that Grad-ODAM is applicable to both one-stage detectors such as FCOS and two-stage detectors such as Faster R-CNN, and produces higher-quality visual explanations than the state-of-the-art both effectively and efficiently. We next propose a training scheme, ODAM-Train, to improve the explanation ability on object discrimination of the detector through encouraging consistency between explanations for detections on the same object, and distinct explanations for detections on different objects. Based on the heat maps produced by Grad-ODAM with ODAM-Train, we propose ODAM-NMS, which considers the information of the model's explanation for each prediction to distinguish the duplicate detected objects. We present a detailed analysis of the visualized explanations of detectors and carry out extensive experiments to validate the effectiveness of the proposed ODAM.
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Authors: Jie Ren; Jiaming Luo; Yao Zhao; Kundan Krishna; Mohammad Saleh; Balaji Lakshminarayanan; Peter J Liu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Machine learning algorithms typically assume independent and identically distributed samples in training and at test time (IID). Much work has shown that high-performing ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions,  particularly for out-of-distribution (OOD) inputs. Conditional language models (CLMs) are predominantly trained to classify the next token in an output sequence, and may suffer even worse degradation on OOD inputs as the prediction is done auto-regressively over many steps. Furthermore, the space of potential low-quality outputs is larger as arbitrary text can be generated and it is important to know when to trust the generated output. We present a highly accurate and lightweight OOD detection method for CLMs, and demonstrate its effectiveness on abstractive summarization and translation. We also show how our method can be used under the common and realistic setting of distribution shift for selective generation (analogous to selective prediction for classification) of high-quality outputs, while  automatically abstaining from low-quality ones, enabling safer deployment of generative language models.
Coupled Multiwavelet Operator Learning for Coupled Differential Equations
Authors: Xiongye Xiao; Defu Cao; Ruochen Yang; Gaurav Gupta; Gengshuo Liu; Chenzhong Yin; Radu Balan; Paul Bogdan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve PDEs by learning the integral kernel directly in Fourier/Wavelet space, so the difficulty of solving the coupled PDEs depends on dealing with the coupled mappings between the functions. Towards this end, we propose a \textit{coupled multiwavelets neural operator} (CMWNO) learning scheme by decoupling the coupled integral kernels during the multiwavelet decomposition and reconstruction procedures in the Wavelet space. The proposed model achieves significantly higher accuracy compared to previous learning-based solvers in solving the coupled PDEs including Gray-Scott (GS) equations and the non-local mean field game (MFG) problem. According to our experimental results, the proposed model exhibits a $2X-4X$ improvement relative $L$2 error compared to the best results from the state-of-the-art models.
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
Authors: Qing Li; Siyuan Huang; Yining Hong; Yixin Zhu; Ying Nian Wu; Song-Chun Zhu
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, HINT, to examine machines' capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t the three levels. Further, we design a few-shot learning split to determine whether or not models can rapidly learn new concepts and generalize them to more complex scenarios. To comprehend existing models' limitations, we undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results indicate that current models struggle to extrapolate to long-range syntactic dependency and semantics. Models exhibit a considerable gap toward human-level generalization when evaluated with new concepts in a few-shot setting. Moreover, we discover that it is infeasible to solve HINT by merely scaling up the dataset and the model size; this strategy contributes little to the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting exhibits impressive results and significantly boosts the test accuracy. We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization.%
Self-Supervised Set Representation Learning for Unsupervised Meta-Learning
Authors: Dong Bok Lee; Seanie Lee; Kenji Kawaguchi; Yunji Kim; Jihwan Bang; Jung-Woo Ha; Sung Ju Hwang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Unsupervised meta-learning (UML) essentially shares the spirit of self-supervised learning (SSL) in that their goal aims at learning models without any human supervision so that the models can be adapted to downstream tasks. Further, the learning objective of self-supervised learning, which pulls positive pairs closer and repels negative pairs, also resembles metric-based meta-learning. Metric-based meta-learning is one of the most successful meta-learning methods, which learns to minimize the distance between representations from the same class. 
One notable aspect of metric-based meta-learning, however, is that it is widely interpreted as a set-level problem since the inference of discriminative class prototypes (or set representations) from few examples is crucial for the performance of downstream tasks. Motivated by this, we propose Set-SimCLR, a novel self-supervised set representation learning framework for targeting UML problem. Specifically, our Set-SimCLR learns a set encoder on top of instance representations to maximize the agreement between two sets of augmented samples, which are generated by applying stochastic augmentations to a given image. We theoretically analyze how our proposed set representation learning can potentially improve the generalization performance at the meta-test. We also empirically validate its effectiveness on various benchmark datasets, showing that Set-SimCLR largely outperforms both UML and instance-level self-supervised learning baselines.
Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
Authors: Yinan Huang; Xingang Peng; Jianzhu Ma; Muhan Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful GNN architectures. However, knowing one model is more powerful than another gives little insight about what functions they can or cannot express. It is still unclear whether these models are able to approximate specific functions such as counting certain graph substructures, which is essential for applications in biology, chemistry and social network analysis. Motivated by this, we propose to study the counting power of Subgraph MPNNs, a recent and popular class of powerful GNN models that extract rooted subgraphs for each node, assign the root node a unique identifier and encode the root node's representation within its rooted subgraph. Specifically, we prove that Subgraph MPNNs fail to count more-than-4-cycles at node level, implying that node representations cannot correctly encode the surrounding substructures like ring systems with more than four atoms. To overcome this limitation, we propose I$^2$-GNNs to extend Subgraph MPNNs by assigning different identifiers for the root node and its neighbors in each subgraph. I$^2$-GNNs' discriminative power is shown to be strictly stronger than Subgraph MPNNs and partially stronger than the 3-WL test. More importantly, I$^2$-GNNs are proven capable of counting all 3, 4, 5 and 6-cycles, covering common substructures like benzene rings in organic chemistry, while still keeping linear complexity. To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees. We validate its counting power in cycle counting tasks and demonstrate its competitive performance in molecular prediction benchmarks.
On the duality between contrastive and non-contrastive self-supervised learning
Authors: Quentin Garrido; Yubei Chen; Adrien Bardes; Laurent Najman; Yann LeCun
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differences between the two families have been thoroughly discussed to motivate new approaches, we focus more on the theoretical similarities between them. By designing contrastive and covariance based non-contrastive criteria that can be related algebraically and shown to be equivalent under limited assumptions, we show how close those families can be. We further study popular methods and introduce variations of them, allowing us to relate this theoretical result to current practices and show the influence (or lack thereof) of design choices on downstream performance. Motivated by our equivalence result, we investigate the low performance of SimCLR and show how it can match VICReg's with careful hyperparameter tuning, improving significantly over known baselines. We also challenge the popular assumption that non-contrastive methods need large output dimensions. Our theoretical and quantitative results suggest that the numerical gaps between contrastive and non-contrastive methods in certain regimes can be closed given better network design choices and hyperparameter tuning. The evidence shows that unifying different SOTA methods is an important direction to build a better understanding of self-supervised learning.
Learning large-scale Kernel Networks
Authors: Amirhesam Abedsoltan; Parthe Pandit; Misha Belkin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract:     This paper concerns large-scale training of *Kernel Networks*, a generalization of kernel machines that allows the model to have arbitrary centers. We propose a scalable training algorithm -- EigenPro 3.0 -- based on alternating projections with preconditioned SGD for the alternating steps. In contrast to classical kernel machines, but similar to neural networks, our algorithm enables decoupling the learned model from the training set. This empowers kernel models to take advantage of modern methodologies in deep learning, such as data augmentation. We demonstrate the promise of EigenPro 3.0 on several experiments over large datasets. We also show data augmentation can improve performance of kernel models.
Instance-Specific Augmentation: Capturing Local Invariances
Authors: Ning Miao; Tom Rainforth; Emile Mathieu; Yann Dubois; Yee Whye Teh; Adam Foster; Hyunjik Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous data augmentation methods have generally assumed independence between the original input and the transformation applied to that input. This can be highly restrictive, as the invariances that the augmentations are based on are themselves often highly input dependent; e.g., we can change a leaf from green to yellow while maintaining its label, but not a lime. InstaAug instead allows for input dependency by introducing an invariance module that maps inputs to tailored transformation distributions. It can be simultaneously trained alongside the downstream model in a fully end-to-end manner, or separately learned for a pre-trained model. We empirically demonstrate that InstaAug learns meaningful input-dependent augmentations for a wide range of transformation classes, which in turn provides better performance on both supervised and self-supervised tasks.
A general differentially private learning framework for decentralized data
Authors: Mei Li; Bo Pan; Yafei Wang; Lingchen Kong; Linglong Kong; Bei Jiang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Decentralized consensus learning has been hugely successful, which minimizes a finite sum of expected objective functions over a network of nodes. However, the local communication across neighboring nodes in the network may lead to the leakage of private information. To address this challenge, we propose a general differentially private (DP) learning framework for decentralized data that applies to many non-smooth learning problems. We show that the proposed algorithm retains the performance guarantee in terms of stability, generalization, and finite sample performance. We investigate the impact of local privacy-preserving computation on the global DP guarantee. Further, we extend the discussion by adopting a new class of noise-adding DP mechanisms based on generalized Gaussian distributions to improve the utility-privacy trade-offs. Our numerical results demonstrate the effectiveness of our algorithm and its better performance over the state-of-the-art baseline methods in various decentralized settings.