GenAI4ELab/papercli-papers huggingface dataset:
Pooling Image Datasets with Multiple Covariate Shift and Imbalance
Authors: Sotirios Panagiotis Chytas; Vishnu Suresh Lokhande; Vikas Singh
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Small sample sizes are common in many disciplines, 
which necessitates pooling roughly similar datasets across 
multiple sites/institutions to study weak but relevant 
associations between images and disease incidence. Such 
data often manifest shifts and imbalances in covariates 
(secondary non-imaging data). 
These issues are well-studied for classical models, but 
the ideas simply do not apply to overparameterized DNN models. 
Consequently, recent work has shown how strategies from 
fairness and invariant representation learning provides 
a meaningful starting point, but the current repertoire 
of methods remains limited to accounting for shifts/imbalances in just a couple of covariates at a time. In this paper, we show how 
viewing this problem from the perspective of Category theory 
provides a simple and effective solution that completely avoids 
elaborate multi-stage training pipelines that would otherwise be 
needed. We show the effectiveness of this approach via 
extensive experiments on real datasets. Further, we 
discuss how our style of formulation offers a unified 
perspective on at least 5+ distinct 
problem settings in vision, from self-supervised learning
to matching problems in 3D reconstruction.
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
Authors: Chongyu Fan; Jiancheng Liu; Yihua Zhang; Eric Wong; Dennis Wei; Sijia Liu
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: With evolving data regulations, machine unlearning (MU) has become an important tool for fostering trust and safety in today's AI models. However, existing MU methods focusing on data and/or weight perspectives often suffer limitations in unlearning accuracy, stability, and cross-domain applicability. To address these challenges, we introduce the concept of 'weight saliency' for MU, drawing parallels with input saliency in model explanation. This innovation directs MU's attention toward specific model weights rather than the entire model, improving effectiveness and efficiency. The resultant method that we call saliency unlearning (SalUn) narrows the performance gap with 'exact' unlearning (model retraining from scratch after removing the forgetting data points). To the best of our knowledge, SalUn is the first principled MU approach that can effectively erase the influence of forgetting data, classes, or concepts in both image classification and generation tasks. As highlighted below, For example, SalUn yields a stability advantage in high-variance random data forgetting, e.g., with a 0.2% gap compared to exact unlearning on the CIFAR-10 dataset. Moreover, in preventing conditional diffusion models from generating harmful images, SalUn achieves nearly 100% unlearning accuracy, outperforming current state-of-the-art baselines like Erased Stable Diffusion and Forget-Me-Not. Codes are available at https://github.com/OPTML-Group/Unlearn-Saliency.

**WARNING**: This paper contains model outputs that may be offensive in nature.
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
Authors: Rishabh Agarwal; Nino Vieillard; Yongchao Zhou; Piotr Stanczyk; Sabela Ramos Garea; Matthieu Geist; Olivier Bachem
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, current KD methods for auto-regressive sequence models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference. To address this issue, we introduce Generalized Knowledge Distillation (GKD). Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences. Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when the student lacks the expressivity to mimic the teacher's distribution. Furthermore, GKD facilitates the seamless integration of distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for distilling auto-regressive T5 language models on summarization, translation, and arithmetic reasoning tasks.
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
Authors: Qiang He; Tianyi Zhou; Meng Fang; Setareh Maghsudi
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performance. Hence, fine-tuning representation rank presents a challenging and crucial optimization problem. To address this issue, we find a guiding principle for adaptive control of the representation rank. We employ the Bellman equation as a theoretical foundation and derive an upper bound on the cosine similarity of consecutive state-action pairs representations of value networks. We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER). This regularizer adaptively regularizes the representation rank, thus improving the DRL agent's performance. We first validate the effectiveness of automatic control of rank on illustrative experiments. Then, we scale up BEER to complex continuous control tasks by combining it with the deterministic policy gradient method. Among 12 challenging DeepMind control tasks, BEER outperforms the baselines by a large margin. Besides, BEER demonstrates significant advantages in Q-value approximation. Our code is available at https://github.com/sweetice/BEER-ICLR2024.
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
Authors: Puja Trivedi; Mark Heimann; Rushil Anirudh; Danai Koutra; Jayaraman J. Thiagarajan
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: While graph neural networks (GNNs) are widely used for node and graph representation learning tasks, the reliability of GNN uncertainty estimates under distribution shifts remains relatively under-explored. Indeed, while post-hoc calibration strategies can be used to improve in-distribution calibration, they need not also improve calibration under distribution shift. However, techniques which produce GNNs with better intrinsic uncertainty estimates are particularly valuable, as they can always be combined with post-hoc strategies later. Therefore, in this work, we propose G-$\Delta$UQ, a novel training framework designed to improve intrinsic GNN uncertainty estimates. Our framework adapts the principle of stochastic data centering to graph data through novel graph anchoring strategies, and is able to support partially stochastic GNNs. While, the prevalent wisdom is that fully stochastic networks are necessary to obtain reliable estimates, we find that the functional diversity induced by our anchoring strategies when sampling hypotheses renders this unnecessary and allows us to support G-$\Delta$UQ on pretrained models. Indeed, through extensive evaluation under covariate, concept and graph size shifts, we show that G-$\Delta$UQ leads to better calibrated GNNs for node and graph classification. Further, it also improves performance on the uncertainty-based tasks of out-of-distribution detection and generalization gap estimation. Overall, our work provides insights into uncertainty estimation for GNNs, and demonstrates the utility of G-$\Delta$UQ in obtaining reliable estimates.
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
Authors: Ameya Daigavane; Song Eun Kim; Mario Geiger; Tess Smidt
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We present Symphony, an $E(3)$ equivariant autoregressive generative model for 3D molecular geometries
that iteratively builds a molecule from molecular fragments.
Existing autoregressive models such as G-SchNet and G-SphereNet  for molecules utilize rotationally invariant features to respect the 3D symmetries of molecules.
In contrast, Symphony uses message-passing with higher-degree $E(3)$-equivariant features.
This allows a novel representation of probability distributions via spherical harmonic signals to efficiently model the 3D geometry of
molecules. We show that Symphony is able to accurately generate small molecules from the QM9 dataset, outperforming existing
autoregressive models and approaching the performance of diffusion models. Our code is available at https://github.com/atomicarchitects/symphony.
Set Learning for Accurate and Calibrated Models
Authors: Lukas Muttenthaler; Robert A. Vandermeulen; Qiuyi Zhang; Thomas Unterthiner; Klaus Robert Muller
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a novel method to alleviate these problems that we call odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets rather than for single examples. This naturally allows the model to capture correlations across data examples and achieves both better accuracy and calibration, especially in limited training data and class-imbalanced regimes. Perhaps surprisingly, OKO often yields better calibration even when training with hard labels and dropping any additional calibration parameter tuning, such as temperature scaling. We demonstrate this in extensive experimental analyses and provide a mathematical theory to interpret our findings. We emphasize that OKO is a general framework that can be easily adapted to many settings and a trained model can be applied to single examples at inference time, without significant run-time overhead or architecture changes.
INViTE: INterpret and Control Vision-Language Models with Text Explanations
Authors: Haozhe Chen; Junfeng Yang; Carl Vondrick; Chengzhi Mao
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the underlying rules behind these models’ predictions and controlling model behaviors have remained open challenges. We present INViTE: a framework for INterpreting Vision Transformer’s latent tokens with Text Explanations. Given a latent token, INViTE retains its semantic information to the final layer using transformer’s local operations and retrieves the closest text for explanation. INViTE enables understanding of model visual reasoning procedure without needing additional model training or data collection. Based on the obtained interpretations, INViTE allows for model editing that controls model reasoning behaviors and improves model robustness against biases and spurious correlations. Our code is available at https://github.com/tonychenxyz/vit-interpret.
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
Authors: Weiran Yao; Shelby Heinecke; Juan Carlos Niebles; Zhiwei Liu; Yihao Feng; Le Xue; Rithesh R N; Zeyuan Chen; Jianguo Zhang; Devansh Arpit; Ran Xu; Phil L Mui; Huan Wang; Caiming Xiong; Silvio Savarese
Venue: ICLR 2024 (ICLR 2024 spotlight)

Abstract: Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however, are not optimized using environment-specific rewards. Although some agents enable iterative refinement through verbal feedback, they do not reason and plan in ways that are compatible with gradient-based learning from rewards. This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model, which automatically tunes the language agent prompts from environment feedback through policy gradient. Specifically, our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model which refines the language agent prompt by summarizing the root cause of prior failed attempts and proposing action plans. Experimental results on various tasks demonstrate that the language agents improve over time and that our approach considerably outperforms baselines that do not properly leverage gradients from the environment.
Trajeglish: Traffic Modeling as Next-Token Prediction
Authors: Jonah Philion; Xue Bin Peng; Sanja Fidler
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Using a simple data-driven tokenization scheme, we discretize trajectories to centimeter-level resolution using a small vocabulary. We then model the multi-agent sequence of discrete motion tokens with a GPT-like encoder-decoder that is autoregressive in time and takes into account intra-timestep interaction between agents. Scenarios sampled from our model exhibit state-of-the-art realism; our model tops the Waymo Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%. We ablate our modeling choices in full autonomy and partial autonomy settings, and show that the representations learned by our model can quickly be adapted to improve performance on nuScenes. We additionally evaluate the scalability of our model with respect to parameter count and dataset size, and use density estimates from our model to quantify the saliency of context length and intra-timestep interaction for the traffic modeling task.
Meaning Representations from Trajectories in Autoregressive Models
Authors: Tian Yu Liu; Matthew Trager; Alessandro Achille; Pramuditha Perera; Luca Zancato; Stefano Soatto
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text. This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model. Moreover, unlike vector-based representations,  distribution-based representations can also model asymmetric relations (e.g., direction of logical entailment, hypernym/hyponym relations) by using algebraic operations between likelihood functions. These ideas are grounded in distributional perspectives on semantics and are connected to standard constructions in automata theory, but to our knowledge they have not been applied to modern language models. We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle. Finally, we extend our method  to represent data from different modalities (e.g., image and text) using multimodal autoregressive models. Our code is available at: https://github.com/tianyu139/meaning-as-trajectories
SWAP: Sparse Entropic Wasserstein Regression for Robust Network Pruning
Authors: Lei You; Hei Victor Cheng
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: This study addresses the challenge of inaccurate gradients in computing the empirical Fisher Information Matrix during network pruning. We introduce SWAP, a formulation of Entropic Wasserstein regression (EWR) for network pruning, capitalizing on the geometric properties of the optimal transport problem. The “swap” of the commonly used linear regression with the EWR in optimization is analytically demonstrated to offer noise mitigation effects by incorporating neighborhood interpolation across data points with only marginal additional computational cost. The unique strength of SWAP is its intrinsic ability to balance noise reduction and covariance information preservation effectively. Extensive experiments performed on various networks and datasets show comparable performance of SWAP with state-of-the-art (SoTA) network pruning algorithms. Our proposed method outperforms the SoTA when the network size or the target sparsity is large, the gain is even larger with the existence of noisy gradients, possibly from noisy data, analog memory, or adversarial attacks. Notably, our proposed method achieves a gain of 6% improvement in accuracy and 8% improvement in testing loss for MobileNetV1 with less than one-fourth of the network parameters remaining.
Circumventing Concept Erasure Methods For Text-To-Image Generative Models
Authors: Minh Pham; Kelly O. Marshall; Niv Cohen; Govind Mittal; Chinmay Hegde
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine seven recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety.
Pose Modulated Avatars from Video
Authors: Chunjin Song; Bastian Wandt; Helge Rhodin
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: It is now possible to reconstruct dynamic human motion and shape from a sparse set of cameras using Neural Radiance Fields (NeRF) driven by an underlying skeleton. However, a challenge remains to model the deformation of cloth and skin in relation to skeleton pose. Unlike existing avatar models that are learned implicitly or rely on a proxy surface, our approach is motivated by the observation that different poses necessitate unique frequency assignments. Neglecting this distinction yields noisy artifacts in smooth areas or blurs fine-grained texture and shape details in sharp regions. We develop a two-branch neural network that is adaptive and explicit in the frequency domain. The first branch is a graph neural network that models correlations among body parts locally, taking skeleton pose as input. The second branch combines these correlation features to a set of global frequencies and then modulates the feature encoding. Our experiments demonstrate that our network outperforms state-of-the-art methods in terms of preserving details and generalization capabilities. Our code is available at https://github.com/ChunjinSong/PM-Avatars.
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images
Authors: Nicholas Konz; Maciej A Mazurowski
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: This paper investigates discrepancies in how neural networks learn from different imaging domains, which are commonly overlooked when adopting computer vision techniques from the domain of natural images to other specialized domains such as medical images. Recent works have found that the generalization error of a trained network typically increases with the intrinsic dimension ($d_{data}$) of its training set. Yet, the steepness of this relationship varies significantly between medical (radiological) and natural imaging domains, with no existing theoretical explanation. We address this gap in knowledge by establishing and empirically validating a generalization scaling law with respect to $d_{data}$, and propose that the substantial scaling discrepancy between the two considered domains may be at least partially attributed to the higher intrinsic ``label sharpness'' ($K_\mathcal{F}$) of medical imaging datasets, a metric which we propose. Next, we demonstrate an additional benefit of measuring the label sharpness of a training set: it is negatively correlated with the trained model's adversarial robustness, which notably leads to models for medical images having a substantially higher vulnerability to adversarial attack. Finally, we extend our $d_{data}$ formalism to the related metric of learned representation intrinsic dimension ($d_{repr}$), derive a generalization scaling law with respect to $d_{repr}$, and show that $d_{data}$ serves as an upper bound for $d_{repr}$. Our theoretical results are supported by thorough experiments with six models and eleven natural and medical imaging datasets over a range of training set sizes. Our findings offer insights into the influence of intrinsic dataset properties on generalization, representation learning, and robustness in deep neural networks. *Code link: https://github.com/mazurowski-lab/intrinsic-properties*
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Authors: Keqiang Yan; Cong Fu; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji
Venue: ICLR 2024 (ICLR 2024 poster)

Abstract: Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture  the complete  geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations.  Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).