GenAI4ELab/papercli-papers huggingface dataset:
Smoothed-SGDmax: A Stability-Inspired Algorithm to Improve Adversarial Generalization
Authors: Jiancong Xiao; Jiawei Zhang; Zhi-Quan Luo; Asuman E. Ozdaglar
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Unlike standard training, deep neural networks can suffer from serious overfitting problems in adversarial settings, which is studied extensively by empirical papers. Recent research (e.g., Xing et al. (2021); Xiao et al. (2022)) show that SGDmax-based adversarial training algorithms with $1/s(T)$ training loss incurs a stability-based generalization bound in $\Theta(c+s(T)/n)$. Here $T$ is the number of iterations, $n$ is the number of samples, $s(T)\rightarrow \infty$ as $T\rightarrow \infty$, and $c$ is a $n$-independent term. This reveals that adversarial training can have nonvanishing generalization errors even if the sample size $n$ goes to infinity. A natural question arises: can we eliminate the nonvanishing term $c$ by designing a more generalizable algorithm? We give an affirmative answer in this paper. First, by an adaptation of information-theoretical lower bound on the complexity of solving Lipschitz-convex problems using randomized algorithms, we show that a minimax lower bound for adversarial generalization gap is $\Omega(s(T)/n)$ given training loss $1/s(T)$. This implies that SGDmax does not achieve the lower bound. Next, by observing that the nonvanishing generalization error term for SGDmax comes from the non-smoothness of the adversarial loss function, we employ a smoothing technique to smooth the adversarial loss function. Based on the smoothed loss function, we design a smoothed SGDmax algorithm achieving generalization bound $\mathcal{O}(s(T)/n)$, which matches the minimax lower bound. Experimentally, we show that our algorithm improves adversarial generalization on common datasets.
Words are all you need? Language as an approximation for human similarity judgments
Authors: Raja Marjieh; Pol Van Rijn; Ilia Sucholutsky; Theodore Sumers; Harin Lee; Thomas L. Griffiths; Nori Jacoby
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model alignment, but classical methods for collecting human similarity judgments are too expensive to be used at scale. Recent methods propose using pre-trained deep neural networks (DNNs) to approximate human similarity, but pre-trained DNNs may not be available for certain domains (e.g., medical images, low-resource languages) and their performance in approximating human similarity has not been extensively tested. We conducted an evaluation of 611 pre-trained models across three domains -- images, audio, video -- and found that there is a large gap in performance between human similarity judgments and pre-trained DNNs. To address this gap, we propose a new class of similarity approximation methods based on language. To collect the language data required by these new methods, we also developed and validated a novel adaptive tag collection pipeline. We find that our proposed language-based methods are significantly cheaper, in the number of human judgments, than classical methods, but still improve performance over the DNN-based methods. Finally, we also develop `stacked' methods that combine language embeddings with DNN embeddings, and find that these consistently provide the best approximations for human similarity across all three of our modalities. Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data. To accompany this guide, we also release all of the similarity and language data, a total of 206,339 human judgments, that we collected in our experiments, along with a detailed breakdown of all modeling results.
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices
Authors: Minxue Tang; Jianyi Zhang; Mingyuan Ma; Louis DiValentin; Aolin Ding; Amin Hassanzadeh; Hai Li; Yiran Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints at the same time. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on resource-constrained edge devices. FADE decouples the entire model into small modules to fit into the resource budget of each edge device respectively, and each device only needs to perform AT on a single module in each communication round. We also propose an auxiliary weight decay to alleviate objective inconsistency and achieve better accuracy-robustness balance in FADE. FADE offers a theoretical guarantee for convergence and adversarial robustness, and our experimental results show that FADE can significantly reduce the consumption of memory and computing power while maintaining accuracy and robustness.
Graph Contrastive Learning Under Heterophily: Utilizing Graph Filters to Generate Graph Views
Authors: Wenhan Yang; Baharan Mirzasoleiman
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graph Neural Networks have achieved tremendous success in (semi-)supervised tasks for which task-specific node labels are available. However, obtaining labels is expensive in many domains, specially as the graphs grow larger in size. Hence, there has been a growing interest in the application of self-supervised techniques, in particular contrastive learning (CL), to graph data. In general, CL methods work by maximizing the agreement between encoded augmentations of the same example, and minimizing agreement between encoded augmentations of different examples. However, we show that existing graph CL methods perform very poorly on graphs with heterophily, in which connected nodes tend to belong to different classes. First, we show that this is attributed to the ineffectiveness of existing graph augmentation methods. Then, we leverage graph filters to directly generate augmented graph views for graph CL under heterophily. In particular, instead of explicitly augmenting the graph topology and encoding the augmentations, we use a high-pass filter in the encoder to generate node representations only based on high-frequency graph signals. Then, we contrast the high-pass filtered representations with their low-pass counterparts produced by the same encoder, to generate representations. Our experimental results confirm that our proposed method, HLCL, outperforms state-of-the-art CL methods on benchmark graphs with heterophily, by up to 10%.
FIGARO: Controllable Music Generation using Learned and Expert Features
Authors: Dimitri von Rütte; Luca Biggio; Yannic Kilcher; Thomas Hofmann
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Recent symbolic music generative models have achieved significant improvements in the quality of the generated samples. Nevertheless, it remains hard for users to control the output in such a way that it matches their expectation. To address this limitation, high-level, human-interpretable conditioning is essential. In this work, we release FIGARO, a Transformer-based conditional model trained to generate symbolic music based on a sequence of high-level control codes. To this end, we propose description-to-sequence learning, which consists of automatically extracting fine-grained, human-interpretable features (the description) and training a sequence-to-sequence model to reconstruct the original sequence given only the description as input. FIGARO achieves state-of-the-art performance in multi-track symbolic music generation both in terms of style transfer and sample quality. We show that performance can be further improved by combining human-interpretable with learned features. Our extensive experimental evaluation shows that FIGARO is able to generate samples that closely adhere to the content of the input descriptions, even when they deviate significantly from the training distribution.
A Neural PDE Solver with Temporal Stencil Modeling
Authors: Zhiqing Sun; Yiming Yang; Shinjae Yoo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Numerical simulation of non-linear partial differential equations plays a crucial role in modeling physical science and engineering phenomena, such as weather, climate, and aerodynamics. Recent Machine Learning (ML) models trained on low-resolution spatio-temporal signals have shown new promises in capturing important dynamics in high-resolution signals, under the condition that the models can effectively recover the missing details. However, this study shows that significant information is often lost in the low-resolution down-sampled features. To address such issues, we propose a new approach, namely Temporal Stencil Modeling (TSM), which combines the strengths of advanced time-series sequence modeling (with the HiPPO features) and state-of-the-art neural PDE solvers (with learnable stencil modeling). TSM aims to recover the lost information from the PDE trajectories and can be regarded as a temporal generalization of classic finite volume methods such as WENO. Our experimental results show that TSM achieves the new state-of-the-art simulation accuracy for 2-D incompressible Navier-Stokes turbulent flows: it significantly outperforms the previously reported best results by 19.9% in terms of the highly-correlated duration time, and reduces the inference latency into 80%. We also show a strong generalization ability of the proposed method to various out-of-distribution turbulent flow settings.
Visual Transformation Telling
Authors: Xin Hong; Yanyan Lan; Liang Pang; Jiafeng Guo; Xueqi Cheng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we propose a new visual reasoning task, called Visual Transformation Telling (VTT). Given a series of states (i.e.~images), a machine is required to describe what happened (i.e.~transformation) between every two adjacent states. Different from most existing visual reasoning tasks, which focus on state reasoning, VTT concentrates on transformation reasoning. Moreover, describing the transformation in the form of language is more natural and closer to the real application than the property change way in the previous TVR task. We collect 13,547 samples from two instructional video datasets, i.e.~CrossTask and COIN, and extract desired states and transformation descriptions to form a suitable VTT benchmark dataset. After that, we introduce an end-to-end learning model for VTT, named TTNet. TTNet consists of three components to mimic human's cognition process of reasoning transformation. First, an image encoder, e.g. CLIP, reads content from each image, then a context encoder links the image content together, and at last, a transformation decoder autoregressively generates transformation descriptions between every two adjacent images. This basic version of TTNet is difficult to meet the cognitive challenge of VTT, that is to identify abstract transformations from images with small visual differences, and the descriptive challenge, which asks to describe the transformation consistently. In response to these difficulties, we propose three strategies to improve TTNet. Specifically, TTNet leverages difference features to emphasize small visual gaps, masked transformation model to stress context by forcing attention to neighbor transformations, and auxiliary category and topic classification tasks to make transformations consistent by sharing underlying semantics among representations. We adapt some typical methods from visual storytelling and dense video captioning tasks, considering their similarity with VTT. Our experimental results show that TTNet achieves better performance on transformation reasoning. In addition, our empirical analysis demonstrates the soundness of each module in TTNet, and provides some insight into transformation reasoning.
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small
Authors: Kevin Ro Wang; Alexandre Variengien; Arthur Conmy; Buck Shlegeris; Jacob Steinhardt
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Research in mechanistic interpretability seeks to explain behaviors of ML models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models, or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task that requires logical reasoning: indirect object identification (IOI). Our explanation encompasses 28 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches including causal interventions and projections.
To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model.  We evaluate the reliability of our explanation using three quantitative criteria - faithfulness, completeness and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. 
Our work provides evidence that a mechanistic understanding of large ML models is feasible, opening opportunities to scale our understanding to both larger models and more complex tasks.
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
Authors: Mingyuan Fan; Yang Liu; Cen Chen; Ximeng Liu; Wenzhong Guo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recent studies reveal the vulnerability of neural networks to backdoor attacks. By embedding backdoors into the hidden neurons with poisoned training data, the backdoor attacker can override normal predictions of the victim model to the attacker-chosen ones whenever the backdoor pattern is present in a testing input. In this paper, to mitigate public concerns about the attack, we propose a novel backdoor defense via identifying and purifying the backdoored neurons of the victim neural network. Specifically, we first define a new metric, called benign salience. By combining the first-order gradient to retain the connections between neurons, benign salience can identify the backdoored neurons with high accuracy. Then, a new Adaptive Regularization (AR) mechanism is proposed to assist in purifying these identified bad neurons via fine-tuning. Due to the ability to adapt to different magnitudes of parameters, AR can provide faster and more stable convergence than the common regularization mechanisms in neuron purifying. Finally, we test the defense effect of our method on ten different backdoor attacks with three benchmark datasets. Experimental results show that our method can decrease the attack success rate by more than 95% on average, which is the best among six state-of-the-art defense methods.
GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation
Authors: Ming Zhang; Shenghan Zhang; Zhenjie Yang; Lekai Chen; Jinliang Zheng; Chao Yang; Chuming Li; Hang Zhou; Yazhe Niu; Yu Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The emergence of various multi-agent environments has motivated powerful algorithms to explore agents' cooperation or competition. Even though this has greatly promoted the development of multi-agent reinforcement learning  (MARL), it is still not enough to support further exploration on the behavior of swarm intelligence between multiple teams, and cooperation between multiple agents due to their limited scalability. To alleviate this, we introduce GoBigger, a scalable platform for cooperative-competition multi-agent interactive simulation. GoBigger is an enhanced environment for the Agar-like game, enabling the simulation of multiple scales of agent intra-team cooperation and inter-team competition. Compared with existing multi-agent simulation environments, our platform supports multi-team games with more than two teams simultaneously, which dramatically expands the diversity of agent cooperation and competition, and can more effectively simulate the swarm intelligent agent behavior. Besides, in GoBigger, the cooperation between the agents in a team can lead to much higher performance. We offer a diverse set of challenging scenarios, built-in bots, and visualization tools for best practices in benchmarking. We evaluate several state-of-the-art algorithms on GoBigger and demonstrate the potential of the environment. We believe this platform can inspire various emerging research directions in MARL, swarm intelligence, and large-scale agent interactive learning. Both GoBigger and its related benchmark are open-sourced. More information could be found at https://github.com/opendilab/GoBigger.
Masked Siamese ConvNets: Towards an Effective Masking Strategy for General-purpose Siamese Networks 
Authors: Li Jing; Jiachen Zhu; Yann LeCun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Siamese Networks are a popular self-supervised learning framework that learns useful representation without human supervision by encouraging representations to be invariant to distortions. Existing methods heavily rely on hand-crafted augmentations, which are not easily adapted to new domains. To explore a general-purpose or domain-agnostic siamese network, we investigate using masking as augmentations in siamese networks. Recently, masking for siamese networks has only been shown useful with transformer architectures, e.g. MSN and data2vec. In this work, we identify the underlying problems of masking for siamese networks with arbitrary backbones, including ConvNets. We propose an effective and general-purpose masking strategy and demonstrate its effectiveness on various siamese network frameworks. Our method generally improves siamese networks' performances in the few-shot image classification, and object detection tasks.
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations
Authors: Grégoire Mialon; Randall Balestriero; Yann LeCun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Self-Supervised Learning (SSL) methods such as VICReg, Barlow Twins or W-MSE avoid collapse of their joint embedding architectures by constraining or regularizing the covariance matrix of their projector’s output. This study highlights important properties of such strategy, which we coin Variance-Covariance regularization (VCReg). More precisely, we show that VCReg enforces pairwise independence between the features of the learned representation. This result emerges by bridging VCReg applied on the projector’s output to kernel independence criteria applied on the projector’s input. This provides the first theoretical motivations and explanations of VCReg. We empirically validate our findings where (i) we put in evidence which projector’s characteristics favor pairwise independence, (ii) we use these findings to obtain nontrivial performance gains for VICReg, (iii) we demonstrate that the scope of VCReg goes beyond SSL by using it to solve Independent Component Analysis. We hope that our findings will support the adoption of VCReg in SSL and beyond.
Decision Transformer under Random Frame Dropping
Authors: Kaizhe Hu; Ray Chen Zheng; Yang Gao; Huazhe Xu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world. Furthermore, when applying to the frame dropping scenarios, they perform unsatisfactorily even with moderate drop rates. To address these issues, we propose  Decision Transformer under Random Frame Dropping~(DeFog), an offline RL algorithm that enables agents to act robustly in frame dropping scenarios without online interaction. DeFog first randomly masks out data in the offline datasets and explicitly adds the time span of frame dropping as inputs. After that, a finetuning stage on the same offline dataset with a higher mask rate would further boost the performance. Empirical results show that DeFog outperforms strong baselines under severe frame drop rates like 90\%, while maintaining similar returns under non-frame-dropping conditions in the regular MuJoCo control benchmarks and the Atari environments. Our approach offers a robust and deployable solution for controlling agents in real-world environments with limited or unreliable data.
Protein structure generation via folding diffusion
Authors: Kevin Eric Wu; Kevin K Yang; Rianne van den Berg; James Zou; Alex Xijie Lu; Ava P Amini
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientation of the constituent amino acid residues, and generate new structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins biologically twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release the first open-source codebase and trained models for protein structure diffusion.