pwc-archive/papers-with-abstracts huggingface dataset:
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
Authors: Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych
Date: 2024-02-20 00:00:00
Tasks: Federated Learning, image-classification, Image Classification

The possibility of adversarial (a.k.a., {\em Byzantine}) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a \emph{robust averaging rule}. While a significant amount of work has been devoted to studying the convergence of federated {\em robust averaging} (which we denote by $\mathsf{FedRo}$), prior work has largely ignored the impact of {\em client subsampling} and {\em local steps}, two fundamental FL characteristics. While client subsampling increases the effective fraction of Byzantine clients, local steps increase the drift between the local updates computed by honest (i.e., non-Byzantine) clients. Consequently, a careless deployment of $\mathsf{FedRo}$ could yield poor performance. We validate this observation by presenting an in-depth analysis of $\mathsf{FedRo}$ tightly analyzing the impact of client subsampling and local steps. Specifically, we present a sufficient condition on client subsampling for nearly-optimal convergence of $\mathsf{FedRo}$ (for smooth non-convex loss). Also, we show that the rate of improvement in learning accuracy {\em diminishes} with respect to the number of clients subsampled, as soon as the sample size exceeds a threshold value. Interestingly, we also observe that under a careful choice of step-sizes, the learning error due to Byzantine clients decreases with the number of local steps. We validate our theory by experiments on the FEMNIST and CIFAR-$10$ image classification tasks.
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting
Authors: Zijian Li, Ruichu Cai, Zhenhui Yang, Haiqin Huang, Guangyi Chen, Yifan Shen, Zhengming Chen, Xiangchen Song, Kun Zhang
Date: 2024-02-20 00:00:00
Tasks: Time Series, Time Series Forecasting

Temporal distribution shifts are ubiquitous in time series data. One of the most popular methods assumes that the temporal distribution shift occurs uniformly to disentangle the stationary and nonstationary dependencies. But this assumption is difficult to meet, as we do not know when the distribution shifts occur. To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur. Beyond that, we further disentangle the stationary and nonstationary latent states via sufficient observation assumption to learn how the latent states change. Specifically, we formalize the causal process with environment-irrelated stationary and environment-related nonstationary variables. Under mild conditions, we show that latent environments and stationary/nonstationary variables are identifiable. Based on these theories, we devise the IDEA model, which incorporates an autoregressive hidden Markov model to estimate latent environments and modular prior networks to identify latent states. The IDEA model outperforms several latest nonstationary forecasting methods on various benchmark datasets, highlighting its advantages in real-world scenarios.
Learning under Singularity: An Information Criterion improving WBIC and sBIC
Authors: Lirui Liu, Joe Suzuki
Date: 2024-02-20 00:00:00
Tasks: Model Selection

We introduce a novel Information Criterion (IC), termed Learning under Singularity (LS), designed to enhance the functionality of the Widely Applicable Bayes Information Criterion (WBIC) and the Singular Bayesian Information Criterion (sBIC). LS is effective without regularity constraints and demonstrates stability. Watanabe defined a statistical model or a learning machine as regular if the mapping from a parameter to a probability distribution is one-to-one and its Fisher information matrix is positive definite. In contrast, models not meeting these conditions are termed singular. Over the past decade, several information criteria for singular cases have been proposed, including WBIC and sBIC. WBIC is applicable in non-regular scenarios but faces challenges with large sample sizes and redundant estimation of known learning coefficients. Conversely, sBIC is limited in its broader application due to its dependence on maximum likelihood estimates. LS addresses these limitations by enhancing the utility of both WBIC and sBIC. It incorporates the empirical loss from the Widely Applicable Information Criterion (WAIC) to represent the goodness of fit to the statistical model, along with a penalty term similar to that of sBIC. This approach offers a flexible and robust method for model selection, free from regularity constraints.
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Authors: Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-Kiong Ng
Date: 2024-02-20 00:00:00
Tasks: Anomaly Detection, Federated Learning, Graph Anomaly Detection, Knowledge Distillation

Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases, thereby impeding collaboration among organizations seeking to collectively develop robust GAD models. Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants. To tackle these challenges, we propose an effective federated graph anomaly detection framework (FGAD). We first introduce an anomaly generator to perturb the normal graphs to be anomalous, and train a powerful anomaly detector by distinguishing generated anomalous graphs from normal ones. Then, we leverage a student model to distill knowledge from the trained anomaly detector (teacher model), which aims to maintain the personality of local models and alleviate the adverse impact of non-IID problems. Moreover, we design an effective collaborative learning mechanism that facilitates the personalization preservation of local models and significantly reduces communication costs among clients. Empirical results of the GAD tasks on non-IID graphs compared with state-of-the-art baselines demonstrate the superiority and efficiency of the proposed FGAD method.
Static vs. Dynamic Databases for Indoor Localization based on Wi-Fi Fingerprinting: A Discussion from a Data Perspective
Authors: Zhe Tang, Ruocheng Gu, Sihao Li, Kyeong Soo Kim, Jeremy S. Smith
Date: 2024-02-20 00:00:00
Tasks: Indoor Localization

Wi-Fi fingerprinting has emerged as the most popular approach to indoor localization. The use of ML algorithms has greatly improved the localization performance of Wi-Fi fingerprinting, but its success depends on the availability of fingerprint databases composed of a large number of RSSIs, the MAC addresses of access points, and the other measurement information. However, most fingerprint databases do not reflect well the time varying nature of electromagnetic interferences in complicated modern indoor environment. This could result in significant changes in statistical characteristics of training/validation and testing datasets, which are often constructed at different times, and even the characteristics of the testing datasets could be different from those of the data submitted by users during the operation of localization systems after their deployment. In this paper, we consider the implications of time-varying Wi-Fi fingerprints on indoor localization from a data-centric point of view and discuss the differences between static and dynamic databases. As a case study, we have constructed a dynamic database covering three floors of the IR building of XJTLU based on RSSI measurements, over 44 days, and investigated the differences between static and dynamic databases in terms of statistical characteristics and localization performance. The analyses based on variance calculations and Isolation Forest show the temporal shifts in RSSIs, which result in a noticeable trend of the increase in the localization error of a Gaussian process regression model with the maximum error of 6.65 m after 14 days of training without model adjustments. The results of the case study with the XJTLU dynamic database clearly demonstrate the limitations of static databases and the importance of the creation and adoption of dynamic databases for future indoor localization research and real-world deployment.
APT-MMF: An advanced persistent threat actor attribution method based on multimodal and multilevel feature fusion
Authors: Nan Xiao, Bo Lang, Ting Wang, Yikai Chen
Date: 2024-02-20 00:00:00
Tasks: Attribute, Graph Attention

Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learning models to classify CTI reports according to their threat actors. However, these methods usually extract only one kind of feature and ignore heterogeneous information, especially the attributes and relations of indicators of compromise (IOCs), which form the core of CTI. To address these problems, we propose an APT actor attribution method based on multimodal and multilevel feature fusion (APT-MMF). First, we leverage a heterogeneous attributed graph to characterize APT reports and their IOC information. Then, we extract and fuse multimodal features, including attribute type features, natural language text features and topological relationship features, to construct comprehensive node representations. Furthermore, we design multilevel heterogeneous graph attention networks to learn the deep hidden features of APT report nodes; these networks integrate IOC type-level, metapath-based neighbor node-level, and metapath semantic-level attention. Utilizing multisource threat intelligence, we construct a heterogeneous attributed graph dataset for verification purposes. The experimental results show that our method not only outperforms the existing methods but also demonstrates its good interpretability for attribution analysis tasks.
Guarantee Regions for Local Explanations
Authors: Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez
Date: 2024-02-20 00:00:00

Interpretability methods that utilise local surrogate models (e.g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not guaranteed to extrapolate to the local region surrounding the point. However, overfitting to the local curvature of the predictive model and malicious tampering can significantly limit extrapolation. We propose an anchor-based algorithm for identifying regions in which local explanations are guaranteed to be correct by explicitly describing those intervals along which the input features can be trusted. Our method produces an interpretable feature-aligned box where the prediction of the local surrogate model is guaranteed to match the predictive model. We demonstrate that our algorithm can be used to find explanations with larger guarantee regions that better cover the data manifold compared to existing baselines. We also show how our method can identify misleading local explanations with significantly poorer guarantee regions.
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Authors: Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
Date: 2024-02-20 00:00:00
Tasks: Continual Learning, Graph structure learning, Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting performance and reliable explanations. However, modeling variable dependencies remains underexplored when MTS is continuously accumulated under different regimes (stages). Due to the potential distribution and dependency disparities, the underlying model may encounter the catastrophic forgetting problem, i.e., it is challenging to memorize and infer different types of variable dependencies across different regimes while maintaining forecasting performance. To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay. Specifically, we develop a forecasting model based on graph structure learning, where a consistency regularization scheme is imposed between the learned variable dependencies and the structural knowledge while optimizing the forecasting objective over the MTS data. As such, MTS representations learned in each regime are associated with distinct structural knowledge, which helps the model memorize a variety of conceivable scenarios and results in accurate forecasts in the continual learning context. Meanwhile, we develop a representation-matching memory replay scheme that maximizes the temporal coverage of MTS data to efficiently preserve the underlying temporal dynamics and dependency structures of each regime. Thorough empirical studies on synthetic and real-world benchmarks validate SKI-CL's efficacy and advantages over the state-of-the-art for continual MTS forecasting tasks.
Spurious Correlations in Machine Learning: A Survey
Authors: Wenqian Ye, Guangtao Zheng, Xu Cao, Yunsheng Ma, Aidong Zhang
Date: 2024-02-20 00:00:00
Tasks: Survey

Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as "spurious" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's generalization and robustness. In this paper, we provide a review of this issue, along with a taxonomy of current state-of-the-art methods for addressing spurious correlations in machine learning models. Additionally, we summarize existing datasets, benchmarks, and metrics to aid future research. The paper concludes with a discussion of the recent advancements and future challenges in this field, aiming to provide valuable insights for researchers in the related domains.
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning
Authors: Rui Jiao, Xiangzhe Kong, Li Zhang, Ziyang Yu, Fangyuan Ren, Wenjuan Tan, Wenbing Huang, Yang Liu
Date: 2024-02-20 00:00:00
Tasks: Denoising, Drug Discovery, Molecular Property Prediction, molecular representation, Prediction, Property Prediction, Representation Learning

Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models in a specific domain, either proteins or small molecules, missing the opportunity to leverage cross-domain knowledge. To mitigate this gap, we introduce Equivariant Pretrained Transformer (EPT), an all-atom foundation model that can be pretrained from multiple domain 3D molecules. Built upon an E(3)-equivariant transformer, EPT is able to not only process atom-level information but also incorporate block-level features (e.g. residuals in proteins). Additionally, we employ a block-level denoising task, rather than the conventional atom-level denoising, as the pretraining objective. To pretrain EPT, we construct a large-scale dataset of 5.89M entries, comprising small molecules, proteins, protein-protein complexes, and protein-molecule complexes. Experimental evaluations on downstream tasks including ligand binding affinity prediction, protein property prediction, and molecular property prediction, show that EPT significantly outperforms previous state-of-the-art methods in the first task and achieves competitively superior performance for the remaining two tasks. Furthermore, we demonstrate the potential of EPT in identifying small molecule drug candidates targeting 3CL protease, a critical target in the replication of SARS-CoV-2. Among 1,978 FDA-approved drugs, EPT ranks 7 out of 8 known anti-COVID-19 drugs in the top 200, indicating the high recall of EPT. By using Molecular Dynamics (MD) simulations, EPT further discoveries 7 novel compounds whose binding affinities are higher than that of the top-ranked known anti-COVID-19 drug, showcasing its powerful capabilities in drug discovery.
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Authors: Junyan Liu, Yunfan Li, Ruosong Wang, Lin F. Yang
Date: 2024-02-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the agent to play an arbitrarily bad policy at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger metric, uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of RL algorithms. Specifically, ULI characterizes the instantaneous performance by ensuring that the per-round suboptimality of the played policy is bounded by a function, monotonically decreasing w.r.t. round t, preventing revisiting bad policies when sufficient samples are available. We demonstrate that a near-optimal ULI guarantee directly implies near-optimal cumulative performance across aforementioned metrics, but not the other way around. To examine the achievability of ULI, we first provide two positive results for bandit problems with finite arms, showing that elimination-based algorithms and high-probability adversarial algorithms with stronger analysis or additional designs, can attain near-optimal ULI guarantees. We also provide a negative result, indicating that optimistic algorithms cannot achieve near-optimal ULI guarantee. Furthermore, we propose an efficient algorithm for linear bandits with infinitely many arms, which achieves the ULI guarantee, given access to an optimization oracle. Finally, we propose an algorithm that achieves near-optimal ULI guarantee for the online reinforcement learning setting.
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
Authors: Hongtao Zhu, Sizhe Zhang, Yang Su, Zhenyu Zhao, Nan Chen
Date: 2024-02-20 00:00:00
Tasks: Active Learning, Causal Inference

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.
Quantum Embedding with Transformer for High-dimensional Data
Authors: Hao-Yuan Chen, Yen-Jui Chang, Shih-wei Liao, Ching-Ray Chang
Date: 2024-02-20 00:00:00
Tasks: Quantum Machine Learning

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems.
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Authors: Guoqi Yu, Jing Zou, Xiaowei Hu, Angelica I. Aviles-Rivero, Jing Qin, Shujun Wang
Date: 2024-02-20 00:00:00
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods, relying on basic moving average kernels, may struggle with the non-linear structure and complex trends in real-world data. Given that, we introduce a learnable decomposition strategy to capture dynamic trend information more reasonably. Additionally, we propose a dual attention module tailored to capture inter-series dependencies and intra-series variations simultaneously for better time series forecasting, which is implemented by channel-wise self-attention and autoregressive self-attention. To evaluate the effectiveness of our method, we conducted experiments across eight open-source datasets and compared it with the state-of-the-art methods. Through the comparison results, our Leddam (LEarnable Decomposition and Dual Attention Module) not only demonstrates significant advancements in predictive performance, but also the proposed decomposition strategy can be plugged into other methods with a large performance-boosting, from 11.87% to 48.56% MSE error degradation.