GenAI4ELab/papercli-papers huggingface dataset:
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Authors: Yuxiao Cheng; Runzhao Yang; Tingxiong Xiao; Zongren Li; Jinli Suo; Kunlun He; Qionghai Dai
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from irregular time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.
Improving group robustness under noisy labels using predictive uncertainty
Authors: Dongpin Oh; Dae Lee; Jeunghyun Byun; Bonggun Shin
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The standard empirical risk minimization (ERM) can underperform on certain minority groups (i.e., waterbirds in lands or landbirds in water) due to the spurious correlation between the input and its label. Several studies have improved the worst-group accuracy by focusing on the high-loss samples. The hypothesis behind this is that such high-loss samples are spurious-cue-free (SCF) samples. However, these approaches can be problematic since the high-loss samples may also be samples with noisy labels in the real-world scenarios. To resolve this issue, we utilize the predictive uncertainty of a model to improve the worst-group accuracy under noisy labels. To motivate this, we theoretically show that the high-uncertainty samples are the SCF samples in the binary classification problem. This theoretical result implies that the predictive uncertainty is an adequate indicator to identify SCF samples in a noisy label setting. Motivated from this, we propose a novel Entropy based Debiasing (END) framework that prevents models from learning the spurious cues while being robust to the noisy labels. In the END framework, we first train the \textit{identification model} to obtain the SCF samples from a training set using its predictive uncertainty. Then, another model is trained on the dataset augmented with an oversampled SCF set. The experimental results show that our END framework outperforms other strong baselines on several real-world benchmarks that consider both the noisy labels and the spurious-cues.
Monkeypox with Cross Infection Hypothesis via Epidemiological Mode
Authors: Tahir Khan; zhiqiang xu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A new re-emerging infectious disease of monkeypox 2022 is structurally related to smallpox that is induced by the monkeypox viruses and has caused 59,606 active cases with 18 deaths up to September 15, 2022. To end this ongoing epidemic, there is a need for population-wide control policies like reducing social interaction by keeping social distance, treatment of infected individuals, and restriction on animals, etc. We forecast the progression of the epidemic and come up with an efficient control mechanism by formulating a mathematical model. The biological feasibility and dynamical behavior of the proposed model are then investigated together with sensitivity analysis to obtain the effect of various epidemic parameters mitigating the spread of the disease. Subsequently, by taking non-pharmaceutical and pharmaceutical intervention strategies as control measures, an optimal control theory is applied to mitigate the fatality of the disease to minimize the infectious population and reduce the cost of controls, we construct an objective functional and solve it by using Pontryagin’s maximum principle. Finally, extensive numerical simulations are performed to show the impact of the application of intervention mechanisms in controlling the transmission of the monkeypox epidemic.
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Authors: Anh Tuan Bui; Trung Le; He Zhao; Quan Hung Tran; Paul Montague; Dinh Phung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model's robustness. The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals (e.g., finding adversarial examples that maximize the model losses for simultaneously attacking multiple models). Therefore, multi-objective optimization (MOO) is a natural tool for adversarial example generation, where we search adversarial examples simultaneously maximizing some objectives/goals. However, we observe that a naive application of MOO tends to maximize all objectives/goals equally, without caring if an objective/goal has been achieved yet. This leads to useless effort to further improve the goal-achieved tasks, while putting less focus on the goal-unachieved tasks. In this paper, we propose \emph{Task Oriented MOO} to address this issue, in the context where we can explicitly define the goal achievement for a task. Our principle is to only maintain the goal-achieved tasks, while letting the optimizer spend more effort on improving the goal-unachieved tasks. We conduct comprehensive experiments for our Task Oriented MOO on various adversarial example generation schemes. The experimental results firmly demonstrate the merit of our proposed approach.
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Authors: Kenton Lee; Mandar Joshi; Iulia Raluca Turc; Hexiang Hu; Fangyu Liu; Julian Martin Eisenschlos; Urvashi Khandelwal; Peter Shaw; Ming-Wei Chang; Kristina Toutanova
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Visually-situated language is ubiquitous---sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.
A Quasistatic Derivation of Optimization Algorithms' Exploration on Minima Manifolds
Authors: Chao Ma; Daniel Kunin; Lexing Ying
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: A quasistatic approach is proposed to derive the optimization algorithms' effective dynamics on the manifold of minima when the iterator oscillates around the manifold. Compared with existing strict analysis, our derivation method is simple and intuitive, has wide applicability, and produces easy-to-interpret results. As examples, we derive the manifold dynamics for SGD, SGD with momentum (SGDm) and Adam with different noise covariances, and justify the closeness of the derived manifold dynamics with the true dynamics through numerical experiments. We then use minima manifold dynamics to study and compare the properties of optimization algorithms. For SGDm, we show that scaling up learning rate and batch size simultaneously accelerates exploration without affecting generalization, which confirms a benefit of large batch training. For Adam, we show that the speed of its manifold dynamics changes with the direction of the manifold, because Adam is not rotationally invariant. This may cause slow exploration in high dimensional parameter spaces.
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
Authors: Ming Xu; Sourav Garg; Michael Milford; Stephen Gould
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead propose a DTW layer based around bi-level optimisation and deep declarative networks, which we name DecDTW. By formulating DTW as a continuous, inequality constrained optimisation problem, we can compute gradients for the solution of the optimal alignment (with respect to the underlying time series) using implicit differentiation. An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself. This naturally occurs, for instance, when learning to improve the accuracy of predicted alignments against ground truth alignments. We evaluate DecDTW on two such applications, namely the audio-to-score alignment task in music information retrieval and the visual place recognition task in robotics, demonstrating state-of-the-art results in both.
RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding
Authors: Xiaojuan Tang; Song-Chun Zhu; Yitao Liang; Muhan Zhang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Knowledge graph (KG) reasoning is an important problem for knowledge graphs. It predicts missing links by reasoning on existing facts. Knowledge graph embedding (KGE) is one of the most popular methods to address this problem. It embeds entities and relations into low-dimensional vectors and uses the learned entity/relation embeddings to predict missing facts. However, KGE only uses zeroth-order (propositional) logic to encode existing triplets (e.g., ``Alice is Bob's wife."); it is unable to leverage first-order (predicate) logic to represent generally applicable logical \textbf{rules} (e.g., ``$\forall x,y \colon x ~\text{is}~ y\text{'s wife} \rightarrow y ~\text{is}~ x\text{'s husband}$''). On the other hand, traditional rule-based KG reasoning methods usually rely on hard logical rule inference, making it brittle and hardly competitive with KGE. In this paper, we propose RulE, a novel and principled framework to represent and model logical rules and triplets. RulE jointly represents entities, relations and logical rules in a unified embedding space. By learning an embedding for each logical rule, RulE can perform logical rule inference in a soft way and give a confidence score to each grounded rule, similar to how KGE gives each triplet a confidence score. Compared to KGE alone, RulE allows injecting prior logical rule information into the embedding space, which improves the generalization of knowledge graph embedding. Besides, the learned confidence scores of rules improve the logical rule inference process by softly controlling the contribution of each rule, which alleviates the brittleness of logic. We evaluate our method with link prediction tasks. Experimental results on multiple benchmark KGs demonstrate the effectiveness of RulE.
Contrastive Vision Transformer for Self-supervised Out-of-distribution Detection
Authors: Hengding Wang; Yuchen Lu; Xi Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Out-of-distribution (OOD) detection is a type of technique that aims to detect abnormal samples that don't belong to the distribution of training data (or in-distribution (ID) data). The technique has been applied to various image classification tasks to identify abnormal image samples for which the abnormality is caused by semantic shift (from different classes) or covariate shift (from different domains). However, disentangling OOD samples caused by different shifts remains a challenge in image OOD detection. This paper proposes Contrastive Vision Transformer (CVT), an attention-based contrastive learning model, for self-supervised OOD detection in image classification tasks. Specifically, vision transformer architecture is integrated as a feature extracting module under a contrastive learning framework. An empirical ensemble module is developed to extract representative ensemble features, from which a balance can be achieved between semantic and covariate OOD samples. The proposed CVT model is tested in various self-supervised OOD detection tasks, and our approach outperforms state-of-the-art methods by 5.5% AUROC on CIFAR-10 (ID) vs. CIFAR-100 (OOD), and by 10.7% AUROC on CIFAR-100 (ID) vs. CIFAR-10 (OOD).
Neural Graphical Models
Authors: Harsh Shrivastava; Urszula Chajewska
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Graphs are ubiquitous and are often used to understand the dynamics of a system. Probabilistic Graphical Models comprising Bayesian and Markov networks, and Conditional Independence graphs are some of the popular graph representation techniques. They can model relationships between features (nodes) together with the underlying distribution.  Although theoretically these models can represent very complex dependency functions, in practice often simplifying assumptions are made due to computational limitations associated with graph operations. This work introduces Neural Graphical Models (NGMs) which attempt to represent complex feature dependencies with reasonable computational costs. Specifically, given a graph of feature relationships and corresponding samples, we capture the dependency structure between the features along with their complex function representations by using neural networks as a multi-task learning framework. We provide efficient learning, inference and sampling algorithms for NGMs. Moreover, NGMs can fit generic graph structures including directed, undirected and mixed-edge graphs as well as support mixed input data types. We present empirical studies that show NGMs' capability to represent Gaussian graphical models, inference analysis of a lung cancer data and extract insights from a real world infant mortality data provided by CDC.
FedFA:  Federated Feature Augmentation
Authors: Tianfei Zhou; Ender Konukoglu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients’ samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FedFA to tackle federated learning from a dis- tinct perspective of federated feature augmentation. FedFA is based on a major insight that each client’s data distribution can be characterized by statistics (i.e., mean and standard deviation) of latent features; and it is likely to manipulate these local statistics globally, i.e., based on information in the entire federation, to let clients have a better sense of the underlying distribution and therefore alleviate local data bias. Based on this insight, we propose to augment each local feature statistic probabilistically based on a normal distribution, whose mean is the original statistic and variance quantifies the augmentation scope. Key to our approach is the determination of a meaningful Gaussian variance, which is accomplished by taking into account not only biased data of each individual client, but also underlying feature statistics characterized by all participating clients. We offer both theoretical and empirical justifications to verify the effectiveness of FedFA. Our code is available at https://github.com/tfzhou/FedFA.
Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs
Authors: Pihe Hu; Yu Chen; Longbo Huang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We study reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, an agent first interacts with the environment without accessing the reward function in the exploration phase. In the subsequent planning phase, it is given a reward function and asked to output an $\epsilon$-optimal policy. We propose a novel algorithm LSVI-RFE under the linear MDP setting, where the transition probability and reward functions are linear in a feature mapping. We prove an $\widetilde{O}(H^{4} d^{2}/\epsilon^2)$ sample complexity upper bound for LSVI-RFE, where $H$ is the episode length and $d$ is the feature dimension. We also establish a sample complexity lower bound of $\Omega(H^{3} d^{2}/\epsilon^2)$. To the best of our knowledge, LSVI-RFE is the first computationally efficient algorithm that achieves the minimax optimal sample complexity in linear MDP settings up to an $H$ and logarithmic factors. Our LSVI-RFE algorithm is based on a novel variance-aware exploration mechanism to avoid overly-conservative exploration in prior works. Our sharp bound relies on the decoupling of UCB bonuses during two phases, and a Bernstein-type self-normalized bound, which remove the extra dependency of sample complexity on $H$ and $d$, respectively.
PA-LoFTR: Local Feature Matching with 3D Position-Aware Transformer
Authors: Chenhao Li; Qisheng Tang; Shuangjiu Xiao; Ying Mao; Deli Dong; Zhifeng Shi; Guoliang Chen; Jiawen Cheng
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose a novel image feature matching method that utilizes 3D position information to augment feature representation with a deep neural network. The proposed method introduces 3D position embedding to a state-of-the-art feature matcher, LoFTR, and achieves more promising performance. Following the coarse-to-fine matching pipeline of LoFTR, we construct a Transformer-based neural network that generates dense pixel-wise matches. Instead of using 2D position embeddings for transformer, the proposed method generates 3D position embeddings that can precisely capture position correspondence of matches between images. Importantly, in order to guide neural network to learn 3D space information, we augment features with depth information generated by a depth predictor. In this way, our method, PA-LoFTR, can generate 3D position-aware local feature descriptors with Transformer. We experiment on indoor datasets, and results show that PA-LoFTR improves the performance of feature matching compared to state-of-the-art methods.
ESP: Exponential Smoothing on Perturbations for Increasing Robustness to Data Corruptions
Authors: Weebum Yoo; Hwanjo Yu; Sung Whan Yoon
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Despite the great advances in the machine learning field over the past decade, deep learning algorithms are often vulnerable to data corruption in real-world environments. We propose a simple yet efficient data augmentation method named Exponential Smoothing on Perturbations (ESP) that imposes perturbations on training data to enhance a model’s robustness to unforeseen data corruptions. With the perturbation on the input side, the target label of a sample is smoothed with an exponentially decaying confidence level with respect to the size of the perturbation. ESP enforces a contour-like decision boundary that smoothly encompasses the region around inter-class samples. We theoretically show that perturbations in input space can encourage a model to find a flat minimum on the parameter space, which makes a model robust to domain shifts. In the extensive evaluation on common corruption benchmarks including MNIST-C, CIFAR-10/100-C, and Tiny-ImageNet-C, our method improves the robustness of a model both as a standalone method and in conjunction with the previous state-of-the-art augmentation-based methods. ESP is a model-agnostic algorithm in the sense that it is neither model-specific nor data-specific.
Solving Continuous Control via Q-learning
Authors: Tim Seyde; Peter Werner; Wilko Schwarting; Igor Gilitschenski; Martin Riedmiller; Daniela Rus; Markus Wulfmeier
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces. However, most actor-critic methods come at the cost of added complexity: heuristics for stabilisation, compute requirements and wider hyperparameter search spaces. We show that a simple modification of deep Q-learning largely alleviates these issues. By combining bang-bang action discretization with value decomposition, framing single-agent control as cooperative multi-agent reinforcement learning (MARL), this simple critic-only approach matches performance of state-of-the-art continuous actor-critic methods when learning from features or pixels. We extend classical bandit examples from cooperative MARL to provide intuition for how decoupled critics leverage state information to coordinate joint optimization, and demonstrate surprisingly strong performance across a variety of continuous control tasks.