GenAI4ELab/papercli-papers huggingface dataset:
Boosting Adversarial Transferability using Dynamic Cues
Authors: Muzammal Naseer; Ahmad Mahmood; Salman Khan; Fahad Khan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The transferability of adversarial perturbations between image models has been extensively studied. In this case, an attack is generated from a known surrogate \eg, the ImageNet trained model, and transferred to change the decision of an unknown (black-box) model trained on an image dataset. However, attacks generated from image models do not capture the dynamic nature of a moving object or a changing scene due to a lack of temporal cues within image models. This leads to reduced transferability of adversarial attacks from representation-enriched \emph{image} models such as Supervised Vision Transformers (ViTs), Self-supervised ViTs (\eg, DINO), and Vision-language models (\eg, CLIP) to black-box \emph{video} models. In this work, we induce dynamic cues within the image models without sacrificing their original performance on images. To this end, we optimize \emph{temporal prompts} through frozen image models to capture motion dynamics. Our temporal prompts are the result of a learnable transformation that allows optimizing for temporal gradients during an adversarial attack to fool the motion dynamics. Specifically, we introduce spatial (image) and temporal (video) cues within the same source model through task-specific prompts. Attacking such prompts maximizes the adversarial transferability from image-to-video and image-to-image models using the attacks designed for image models. As an example, an iterative attack launched from image model Deit-B with temporal prompts reduces generalization (top1 \% accuracy) of a video model by 35\% on Kinetics-400. Our approach also improves adversarial transferability to image models by 9\% on ImageNet w.r.t the current state-of-the-art approach. Our attack results indicate that the attacker does not need specialized architectures, \eg, divided space-time attention, 3D convolutions, or multi-view convolution networks for different data modalities. Image models are effective surrogates to optimize an adversarial attack to fool black-box models in a changing environment over time. Code is available at \url{https://bit.ly/3Xd9gRQ}
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Authors: Zeyu Tang; Yatong Chen; Yang Liu; Kun Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly.
A Probabilistic Framework For Modular Continual Learning
Authors: Lazar Valkov; Akash Srivastava; Dipak Chaudhari; Swarat Chaudhuri; Charles Sutton
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Continual learning (CL) algorithms seek to accumulate and transfer knowledge across a sequence of tasks and achieve better performance on each successive task. Modular approaches, which use a different composition of modules for each task and avoid forgetting by design, have been shown to be a promising direction to CL. However, searching through the large space of possible module compositions remains a challenge. In this work, we develop a scalable probabilistic search framework as a solution to this challenge. Our framework has two distinct components. The first is designed to transfer knowledge across similar input domains. To this end, it models each module’s training input distribution and uses a Bayesian model to find the most promising module compositions for a new task. The second component targets transfer across tasks with disparate input distributions or different input spaces and uses Bayesian optimisation to explore the space of module compositions. We show that these two methods can be easily combined and evaluate the resulting approach on two benchmark suites designed to capture different desiderata of CL techniques. The experiments show that our framework offers superior performance compared to state-of-the-art CL baselines.
Personalized Federated Learning with Feature Alignment and Classifier Collaboration
Authors: Jian Xu; Xinyi Tong; Shao-Lun Huang
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for each client. However, previous works do not utilize the global knowledge during local representation learning and also neglect the fine-grained collaboration between local classifier heads, which limits the model generalization ability. In this work, we conduct explicit local-global feature alignment by leveraging global semantic knowledge for learning a better representation. Moreover, we quantify the benefit of classifier combination for each client as a function of the combining weights and derive an optimization problem for estimating optimal weights. Finally, extensive evaluation results on benchmark datasets with various heterogeneous data scenarios demonstrate the effectiveness of our proposed method.
Robustness Exploration of Semantic Information in Adversarial Training
Authors: Huafeng Kuang; Hong Liu; Mingliang Xu; YONGJIAN WU; Rongrong Ji
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we look into the problem of adversarial robustness from the semantic information perspective. We demonstrate a novel insight that adversarial attacks destroy the correlation between visual representations and semantic word vectors, and adversarial training fixed it. We further find that the correlation between robust features of different categories is consistent with the correlation between corresponding semantic word vectors. Based on that, we introduce the semantic information to assist model training and propose Semantic Constraint Adversarial Robust Learning (SCARL). First, we follow an information-theoretical lens to formulate the mutual information between the visual representation and the corresponding semantic word vector in the embedding space to bridge the information gap. We further provide a differentiable lower bound to optimize such mutual information efficiently. Second, we propose a novel semantic structural constraint, encouraging the trained model to keep the structure of visual representations consistent with that of semantic word vectors. Finally, we combine these two techniques with adversarial training to learn robust visual representation. Experimentally, we conduct extensive experiments on several benchmarks, demonstrating that semantic information is indeed beneficial to model robustness.
CLEEGN: A Convolutional Neural Network for Plug-and-Play Automatic EEG Reconstruction
Authors: Pin-Hua Lai; Wei-Chun Yang; Hsiang-Chieh Tsou; Chun-Shu Wei
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Human electroencephalography (EEG) is a brain monitoring modality that senses cortical neuroelectrophysiological activity in high-temporal resolution. One of the greatest challenges posed in applications of EEG is the unstable signal quality susceptible to inevitable artifacts during recordings. To date, most existing techniques for EEG artifact removal and reconstruction are applicable to offline analysis solely, or require individualized training data to facilitate online reconstruction. We have proposed CLEEGN, a novel convolutional neural network for plug-and-play automatic EEG reconstruction. CLEEGN is based on a subject-independent pre-trained model using existing data and can operate on a new user without any further calibration. The performance of CLEEGN was validated using multiple evaluations including waveform observation, reconstruction error assessment, and decoding accuracy on well-studied labeled datasets. The results of simulated online validation suggest that, even without any calibration, CLEEGN can largely preserve inherent brain activity and outperforms leading online/offline artifact removal methods in the decoding accuracy of reconstructed EEG data. In addition, visualization of model parameters and latent features exhibit the model behavior and reveal explainable insights related to existing knowledge of neuroscience. We foresee pervasive applications of CLEEGN in prospective works of online plug-and-play EEG decoding and analysis.
CASR: Generating Complex Sequences with Autoregressive Self-Boost Refinement
Authors: Hongwei Han; Mengyu Zhou; Shi Han; Xiu Li; Dongmei Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: There are sequence generation tasks where the best order to generate the target sequence is not left-to-right. For example, an answer to the Sudoku game, a structured code like s-expression, and even a logical natural language answer where the analysis may be generated after the decision. We define the target sequences of those tasks as complex sequences. Obviously, a complex sequence should be constructed with multiple logical steps, and has dependencies among each part of itself (e.g. decisions depend on analyses). It's a great challenge for the classic left-to-right autoregressive generation system to generate complex sequences. Current approaches improve one-pass left-to-right generation on NLG tasks by generating different heuristic intermediate sequences in multiple stages. However, for complex sequences, the heuristic rules to break down them may hurt performance, and increase additional exposure bias. To tackle these challenges, we propose a PLM-friendly autoregressive self-boost refinement framework, CASR. When training, CASR inputs the predictions generated by the model itself at the previous refinement step (instead of those produced by heuristic rules). To find an optimal design, we also discuss model architecture, parameter efficiency and initialization strategy. By evaluating CASR on Sudoku, WebQSP, MTOP and KVRET through controlled experiments and empirical studies, we find that CASR produces high-quality outputs. CASR also improves Accuracy on Sudoku (70.93% --> 97.28%) and achieves state-of-the-art performance on KVRET with Micro F1 score (67.88% --> 70.00%).
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Authors: Saarthak Kapse; Srijan Das; Jingwei Zhang; Rajarsi R. Gupta; Joel Saltz; Dimitris Samaras; Prateek Prasanna
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this work, we develop Di-SSL, a Diversity-inducing Self-Supervised Learning technique for histopathology image analysis. SSL techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective rep- resentations without any human supervision. Lately, computational pathology has also benefited from the resounding success of SSL. In this work, we develop a novel domain-aware pretext task to enhance representation learning in digital pathology. Our analysis of vanilla SSL-pretrained models’ attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although atten- tion sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological com- ponents. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we first leverage cell segmenta- tion to densely extract multiple histopathology-specific representations. We then propose a dense pretext task for SSL, designed to match the multiple correspond- ing representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversi- fication in attention for capturing context rich representations. Through quantita- tive and qualitative analysis on multiple slide-level tasks across cancer types, and patch-level classification tasks, we demonstrate the efficacy of our method and observe that the attention is more globally distributed. Specifically, we obtain a relative improvement in accuracy of up to 6.9% in slide-level and 2% in patch level classification tasks (corresponding AUC improvement up to 7.9% and 0.7%, respectively) over a baseline SSL model.
Analogy-Forming Transformers for Few-Shot 3D Parsing
Authors: Nikolaos Gkanatsios; Mayank Singh; Zhaoyuan Fang; Shubham Tulsiani; Katerina Fragkiadaki
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present Analogical Networks, a model that segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corresponding part structures, and then predicts analogous part structures in the input object 3D point cloud, via an end-to-end learnable modulation mechanism. By conditioning on more than one retrieved memories, compositions of structures are predicted, that mix and match parts across the retrieved memories. One-shot, few-shot or many-shot learning are treated uniformly in Analogical Networks, by conditioning on the appropriate set of memories, whether taken from a single, few or many memory exemplars, and inferring analogous parses. We show Analogical Networks are competitive with state-of-the-art 3D segmentation transformer in many-shot settings and outperform them and existing paradigms of meta-learning and few-shot learning in few-shot scenarios. Our model successfully parses instances of novel object categories simply by expanding its memory, without any weight updates.
Learn to Know Unknowns: A Bionic Memory Network for Unsupervised Anomaly Detection
Authors: Jiahao Li; Yiqiang Chen; Yunbing Xing
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Is generalization always beneficial? Over-strong generalization induces the model insensitive to anomalies. Unsupervised anomaly detection requires only unlabeled non-anomalous data to learn and generalize normal patterns, which results in a modest reconstruction error when reconstructing normal instances and a significant reconstruction error when reconstructing anomalies. However, over-strong generalization leads to the indistinguishable reconstruction error of normal instances and anomalies, which means that the model well reconstructs the unknown anomalies, resulting in unnoticeable reconstruction error. Inspired by the cascade structure of the hippocampus and cortex in human brain memory, we proposed a re-representation memory network called Random Forgetting Twin Memory (RFTM) to decompose the latent space and introduce a configurable reintegration mechanism to suppress overgeneralization. RFTM shows striking brain-like memory characteristics, which enables the model to know what it does not know. RFTM has the convenience of a single line of code boosting at the model level without adding any additional extra loss terms at the loss function level. RFTM-based models have achieved state-of-the-art experimental results on different public benchmarks.
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
Authors: Dan Qiao; Yu-Xiang Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We study the problem of deployment efficient reinforcement learning (RL) with linear function approximation under the \emph{reward-free} exploration setting. This is a well-motivated problem because deploying new policies is costly in real-life RL applications. Under the linear MDP setting with feature dimension $d$ and planning horizon $H$, we propose a new algorithm that collects at most $\widetilde{O}(\frac{d^2H^5}{\epsilon^2})$ trajectories within $H$ deployments to identify $\epsilon$-optimal policy for any (possibly data-dependent) choice of reward functions. To the best of our knowledge, our approach is the first to achieve optimal deployment complexity and optimal $d$ dependence in sample complexity at the same time, even if the reward is known ahead of time. Our novel techniques include an exploration-preserving policy discretization and a generalized G-optimal experiment design, which could be of independent interest. Lastly, we analyze the related problem of regret minimization in low-adaptive RL and provide information-theoretic lower bounds for switching cost and batch complexity.
Revisiting the Entropy Semiring for Neural Speech Recognition
Authors: Oscar Chang; Dongseong Hwang; Olivier Siohan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information between speech and text is rarely available during training, models need to learn it in a completely self-supervised way. In practice, the exponential number of possible alignments makes this extremely challenging, with models often learning peaky or sub-optimal alignments. Prima facie, the exponential nature of the alignment space makes it difficult to even quantify the uncertainty of a model's alignment distribution. Fortunately, it has been known for decades that the entropy of a probabilistic finite state transducer can be computed in time linear to the size of the transducer via a dynamic programming reduction based on semirings. In this work, we revisit the entropy semiring for neural speech recognition models, and show how alignment entropy can be used to supervise models through regularization or distillation. We also contribute an open-source implementation of CTC and RNN-T in the semiring framework that includes numerically stable and highly parallel variants of the entropy semiring. Empirically, we observe that the addition of alignment distillation improves the accuracy and latency of an already well-optimized teacher-student distillation model, achieving state-of-the-art performance on the Librispeech dataset in the streaming scenario.
Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks
Authors: Jurijs Nazarovs; Jack W Stokes; Melissa Turcotte; Justin Carroll; Itai Grady
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Ransomware attacks are increasing at an alarming rate, leading to large financial losses, unrecov- erable encrypted data, data leakage, and privacy concerns. The prompt detection of ransomware attacks is required to minimize further damage, particularly during the encryption stage. However, the frequency and structure of the observed ransomware attack data makes this task difficult to accomplish in practice. The data corresponding to ransomware attacks represents temporal, high- dimensional sparse signals, with limited records and very imbalanced classes. While traditional deep learning models have been able to achieve state-of-the-art results in a wide variety of domains, Bayesian Neural Networks, which are a class of probabilistic models, are better suited to the issues of the ransomware data. These models combine ideas from Bayesian statistics with the rich expres- sive power of neural networks. In this paper, we propose the Radial Spike and Slab Bayesian Neural Network, which is a new type of Bayesian Neural network that includes a new form of the approx- imate posterior distribution. The model scales well to large architectures and recovers the sparse structure of target functions. We provide a theoretical justification for using this type of distribution, as well as a computationally efficient method to perform variational inference. We demonstrate the performance of our model on a real dataset of ransomware attacks and show improvement over a large number of baselines, including state-of-the-art models such as Neural ODEs (ordinary dif- ferential equations). In addition, we propose to represent low-level events as MITRE ATT&CK tactics, techniques, and procedures (TTPs) which allows the model to better generalize to unseen ransomware attacks.
Convergence Analysis of Split Learning on Non-IID Data
Authors: Yipeng Li; Xinchen Lyu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Split Learning (SL) is one promising variant of Federated Learning (FL), where the AI model is split and trained at the clients and the server collaboratively. By offloading the computation-intensive portions to the server, SL enables efficient model training on resource-constrained clients. Despite its booming applications, SL still lacks rigorous convergence analysis on non-IID data, which is critical for hyperparameter selection. In this paper, we first prove that SL exhibits an $\mathcal{O}(1/\sqrt{T})$ convergence rate for non-convex objectives on non-IID data, where $T$ is the number of total steps. By comparing the convergence analysis and experimental results, SL can outperform FL in terms of convergence rate (w.r.t. per-client training/communication rounds, and hence, the computation efficiency) and exhibit comparable accuracy to FL on mildly non-IID data. In contrast, FL prevails on highly non-IID data.
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
Authors: Albert Qiaochu Jiang; Sean Welleck; Jin Peng Zhou; Timothee Lacroix; Jiacheng Liu; Wenda Li; Mateja Jamnik; Guillaume Lample; Yuhuai Wu
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no attempts were made to take advantage of available informal proofs. In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems. We investigate two relevant setups where informal proofs are either written by humans or generated by a language model. Our experiments and ablation studies show that large language models are able to produce well-structured formal sketches that follow the same reasoning steps as the informal proofs. Guiding an automated prover with these sketches enhances its performance from $20.9\%$ to $39.3\%$ on a collection of mathematical competition problems.