pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable Imitation Learning with Dynamic Causal Relations
Authors: Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen
Date: 2023-09-30 00:00:00
Tasks: Causal Discovery, Decision Making, Imitation Learning

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control policies learned by the agent. Difficulties mainly come from two aspects: 1) agents in imitation learning are usually implemented as deep neural networks, which are black-box models and lack interpretability; 2) the latent causal mechanism behind agents' decisions may vary along the trajectory, rather than staying static throughout time steps. To increase transparency and offer better interpretability of the neural agent, we propose to expose its captured knowledge in the form of a directed acyclic causal graph, with nodes being action and state variables and edges denoting the causal relations behind predictions. Furthermore, we design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. Concretely, we conduct causal discovery from the perspective of Granger causality and propose a self-explainable imitation learning framework, {\method}. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner. After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed {\method} in learning the dynamic causal graphs for understanding the decision-making of imitation learning meanwhile maintaining high prediction accuracy.
Nonparametric active learning for cost-sensitive classification
Authors: Boris Ndjia Njike, Xavier Siebert
Date: 2023-09-30 00:00:00
Tasks: Active Learning, Classification, Prediction

Cost-sensitive learning is a common type of machine learning problem where different errors of prediction incur different costs. In this paper, we design a generic nonparametric active learning algorithm for cost-sensitive classification. Based on the construction of confidence bounds for the expected prediction cost functions of each label, our algorithm sequentially selects the most informative vector points. Then it interacts with them by only querying the costs of prediction that could be the smallest. We prove that our algorithm attains optimal rate of convergence in terms of the number of interactions with the feature vector space. Furthermore, in terms of a general version of Tsybakov's noise assumption, the gain over the corresponding passive learning is explicitly characterized by the probability-mass of the boundary decision. Additionally, we prove the near-optimality of obtained upper bounds by providing matching (up to logarithmic factor) lower bounds.
Machine Learning-driven Analysis of Gastrointestinal Symptoms in Post-COVID-19 Patients
Authors: Maitham G. Yousif, Fadhil G. Al-Amran, Salman Rawaf, Mohammad Abdulla Grmt
Date: 2023-09-27 00:00:00

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed significant health challenges worldwide. While respiratory symptoms have been the primary focus, emerging evidence has highlighted the impact of COVID-19 on various organ systems, including the gastrointestinal (GI) tract. This study, based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and 2023, investigates the prevalence and patterns of GI symptoms in individuals recovering from COVID-19 and leverages machine learning algorithms to identify predictive factors for these symptoms. The research findings reveal that a notable percentage of post-COVID-19 patients experience GI symptoms during their recovery phase. Diarrhea emerged as the most frequently reported symptom, followed by abdominal pain and nausea. Machine learning analysis uncovered significant predictive factors for GI symptoms, including age, gender, disease severity, comorbidities, and the duration of COVID-19 illness. These findings underscore the importance of monitoring and addressing GI symptoms in post-COVID-19 care, with machine learning offering valuable tools for early identification and personalized intervention. This study contributes to the understanding of the long-term consequences of COVID-19 on GI health and emphasizes the potential benefits of utilizing machine learning-driven analysis in predicting and managing these symptoms. Further research is warranted to delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to develop targeted interventions for symptom management. Keywords: COVID-19, gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19 care, long COVID.
Understanding Robust Overfitting from the Feature Generalization Perspective
Authors: Chaojian Yu, Xiaolong Shi, Jun Yu, Bo Han, Tongliang Liu
Date: 2023-10-01 00:00:00
Tasks: Adversarial Robustness, Data Augmentation

Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. However, it is plagued by the issue of robust overfitting (RO), which severely damages the model's robustness. In this paper, we investigate RO from a novel feature generalization perspective. Specifically, we design factor ablation experiments to assess the respective impacts of natural data and adversarial perturbations on RO, identifying that the inducing factor of RO stems from natural data. Given that the only difference between adversarial and natural training lies in the inclusion of adversarial perturbations, we further hypothesize that adversarial perturbations degrade the generalization of features in natural data and verify this hypothesis through extensive experiments. Based on these findings, we provide a holistic view of RO from the feature generalization perspective and explain various empirical behaviors associated with RO. To examine our feature generalization perspective, we devise two representative methods, attack strength and data augmentation, to prevent the feature generalization degradation during AT. Extensive experiments conducted on benchmark datasets demonstrate that the proposed methods can effectively mitigate RO and enhance adversarial robustness.
GNRK: Graph Neural Runge-Kutta method for solving partial differential equations
Authors: Hoyun Choi, Sungyeop Lee, B. Kahng, Junghyo Jo
Date: 2023-10-01 00:00:00
Tasks: Graph Neural Network

Neural networks have proven to be efficient surrogate models for tackling partial differential equations (PDEs). However, their applicability is often confined to specific PDEs under certain constraints, in contrast to classical PDE solvers that rely on numerical differentiation. Striking a balance between efficiency and versatility, this study introduces a novel approach called Graph Neural Runge-Kutta (GNRK), which integrates graph neural network modules with a recurrent structure inspired by the classical solvers. The GNRK operates on graph structures, ensuring its resilience to changes in spatial and temporal resolutions during domain discretization. Moreover, it demonstrates the capability to address general PDEs, irrespective of initial conditions or PDE coefficients. To assess its performance, we benchmark the GNRK against existing neural network based PDE solvers using the 2-dimensional Burgers' equation, revealing the GNRK's superiority in terms of model size and accuracy. Additionally, this graph-based methodology offers a straightforward extension for solving coupled differential equations, typically necessitating more intricate models.
A primal-dual perspective for distributed TD-learning
Authors: Han-Dong Lim, Donghwan Lee
Date: 2023-10-01 00:00:00
Tasks: Distributed Optimization

The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process. The proposed approach is based on distributed optimization algorithms, which can be interpreted as primal-dual Ordinary differential equation (ODE) dynamics subject to null-space constraints. Based on the exponential convergence behavior of the primal-dual ODE dynamics subject to null-space constraints, we examine the behavior of the final iterate in various distributed TD-learning scenarios, considering both constant and diminishing step-sizes and incorporating both i.i.d. and Markovian observation models. Unlike existing methods, the proposed algorithm does not require the assumption that the underlying communication network structure is characterized by a doubly stochastic matrix.
PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting
Authors: Zeying Gong, Yujin Tang, Junwei Liang
Date: 2023-10-01 00:00:00
Tasks: Time Series, Time Series Forecasting

Although the Transformer has been the dominant architecture for time series forecasting tasks in recent years, a fundamental challenge remains: the permutation-invariant self-attention mechanism within Transformers leads to a loss of temporal information. To tackle these challenges, we propose PatchMixer, a novel CNN-based model. It introduces a permutation-variant convolutional structure to preserve temporal information. Diverging from conventional CNNs in this field, which often employ multiple scales or numerous branches, our method relies exclusively on depthwise separable convolutions. This allows us to extract both local features and global correlations using a single-scale architecture. Furthermore, we employ dual forecasting heads encompassing linear and nonlinear components to better model future curve trends and details. Our experimental results on seven time-series forecasting benchmarks indicate that compared with the state-of-the-art method and the best-performing CNN, PatchMixer yields $3.9\%$ and $21.2\%$ relative improvements, respectively, while being 2-3x faster than the most advanced method.
Twin Neural Network Improved k-Nearest Neighbor Regression
Authors: Sebastian J. Wetzel
Date: 2023-10-01 00:00:00
Tasks: regression

Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. Choosing the anchors to be the nearest neighbors of the unknown data point leads to a neural network-based improvement of k-nearest neighbor regression. This algorithm is shown to outperform both neural networks and k-nearest neighbor regression on small to medium-sized data sets.
Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification
Authors: Sepehr Bakhshi, Fazli Can
Date: 2023-10-01 00:00:00
Tasks: Ensemble Learning, Imputation, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot balance effectiveness and efficiency. In this work, we propose a neural network-based approach that tackles this issue and is suitable for high-dimensional multi-label classification. Our model uses a selective concept drift adaptation mechanism that makes it suitable for a non-stationary environment. Additionally, we adapt our model to an environment with missing labels using a simple yet effective imputation strategy and demonstrate that it outperforms a vast majority of the state-of-the-art supervised models. To achieve our purposes, we introduce a weighted binary relevance-based approach named ML-BELS using the Broad Ensemble Learning System (BELS) as its base classifier. Instead of a chain of stacked classifiers, our model employs independent weighted ensembles, with the weights generated by the predictions of a BELS classifier. We show that using the weighting strategy on datasets with low label cardinality negatively impacts the accuracy of the model; with this in mind, we use the label cardinality as a trigger for applying the weights. We present an extensive assessment of our model using 11 state-of-the-art baselines, five synthetics, and 13 real-world datasets, all with different characteristics. Our results demonstrate that the proposed approach ML-BELS is successful in balancing effectiveness and efficiency, and is robust to missing labels and concept drift.
Learning Type Inference for Enhanced Dataflow Analysis
Authors: Lukas Seidel, Sedick David Baker Effendi, Xavier Pinho, Konrad Rieck, Brink van der Merwe, Fabian Yamaguchi
Date: 2023-10-01 00:00:00
Tasks: Type prediction

Statically analyzing dynamically-typed code is a challenging endeavor, as even seemingly trivial tasks such as determining the targets of procedure calls are non-trivial without knowing the types of objects at compile time. Addressing this challenge, gradual typing is increasingly added to dynamically-typed languages, a prominent example being TypeScript that introduces static typing to JavaScript. Gradual typing improves the developer's ability to verify program behavior, contributing to robust, secure and debuggable programs. In practice, however, users only sparsely annotate types directly. At the same time, conventional type inference faces performance-related challenges as program size grows. Statistical techniques based on machine learning offer faster inference, but although recent approaches demonstrate overall improved accuracy, they still perform significantly worse on user-defined types than on the most common built-in types. Limiting their real-world usefulness even more, they rarely integrate with user-facing applications. We propose CodeTIDAL5, a Transformer-based model trained to reliably predict type annotations. For effective result retrieval and re-integration, we extract usage slices from a program's code property graph. Comparing our approach against recent neural type inference systems, our model outperforms the current state-of-the-art by 7.85% on the ManyTypes4TypeScript benchmark, achieving 71.27% accuracy overall. Furthermore, we present JoernTI, an integration of our approach into Joern, an open source static analysis tool, and demonstrate that the analysis benefits from the additional type information. As our model allows for fast inference times even on commodity CPUs, making our system available through Joern leads to high accessibility and facilitates security research.
Learning How to Propagate Messages in Graph Neural Networks
Authors: Teng Xiao, Zhengyu Chen, Donglin Wang, Suhang Wang
Date: 2023-10-01 00:00:00

This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the propagation strategy. For instance, the choices of propagation steps are often specialized to a single graph and are not personalized to different nodes. To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs. We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters in a variational Expectation-Maximization (VEM) framework. Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can significantly achieve better performance compared with the state-of-the-art methods, and can effectively learn personalized and interpretable propagate strategies of messages in GNNs.
Deterministic Langevin Unconstrained Optimization with Normalizing Flows
Authors: James M. Sullivan, Uros Seljak
Date: 2023-10-01 00:00:00
Tasks: Active Learning, Bayesian Optimization, Hyperparameter Optimization

We introduce a global, gradient-free surrogate optimization strategy for expensive black-box functions inspired by the Fokker-Planck and Langevin equations. These can be written as an optimization problem where the objective is the target function to maximize minus the logarithm of the current density of evaluated samples. This objective balances exploitation of the target objective with exploration of low-density regions. The method, Deterministic Langevin Optimization (DLO), relies on a Normalizing Flow density estimate to perform active learning and select proposal points for evaluation. This strategy differs qualitatively from the widely-used acquisition functions employed by Bayesian Optimization methods, and can accommodate a range of surrogate choices. We demonstrate superior or competitive progress toward objective optima on standard synthetic test functions, as well as on non-convex and multi-modal posteriors of moderate dimension. On real-world objectives, such as scientific and neural network hyperparameter optimization, DLO is competitive with state-of-the-art baselines.
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
Authors: Parikshit Pareek, Deepjyoti Deka, Sidhant Misra
Date: 2023-10-01 00:00:00
Tasks: Transfer Learning

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage-power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50% reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50-250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75% of N-2 contingency network structures, even without historical N-2 outage data. The proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection
Authors: Sarath Sivaprasad, Mario Fritz
Date: 2023-10-01 00:00:00
Tasks: Anomaly Detection

Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based on a familiarity hypothesis, where familiar features serve as the reference in a pre-trained embedding space. While this strategy has proven highly successful, it turns out that it causes consistent false negatives when anomalies consist of truly novel features that are not well captured by the pre-trained encoding. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. We achieve strong performance across a wide range of anomaly benchmarks by combining familiarity and novelty in a hybrid approach. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types while eliminating the need for expensive background models and dense matching. In particular, we show that by taking account of novel features, we reduce false negative anomalies by up to 40% on challenging benchmarks compared to the state-of-the-art. Our method gives visually inspectable explanations for pixel-level anomalies.
Bayesian Design Principles for Frequentist Sequential Learning
Authors: Yunbei Xu, Assaf Zeevi
Date: 2023-10-01 00:00:00
Tasks: Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning

We develop a general theory to optimize the frequentist regret for sequential learning problems, where efficient bandit and reinforcement learning algorithms can be derived from unified Bayesian principles. We propose a novel optimization approach to generate "algorithmic beliefs" at each round, and use Bayesian posteriors to make decisions. The optimization objective to create "algorithmic beliefs," which we term "Algorithmic Information Ratio," represents an intrinsic complexity measure that effectively characterizes the frequentist regret of any algorithm. To the best of our knowledge, this is the first systematical approach to make Bayesian-type algorithms prior-free and applicable to adversarial settings, in a generic and optimal manner. Moreover, the algorithms are simple and often efficient to implement. As a major application, we present a novel algorithm for multi-armed bandits that achieves the "best-of-all-worlds" empirical performance in the stochastic, adversarial, and non-stationary environments. And we illustrate how these principles can be used in linear bandits, bandit convex optimization, and reinforcement learning.
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
Authors: Han Yu, Huiyuan Yang, Akane Sano
Date: 2023-10-01 00:00:00
Tasks: Attribute, Self-Supervised Learning, Sleep apnea detection

Electrocardiogram (ECG) is an essential signal in monitoring human heart activities. Researchers have achieved promising results in leveraging ECGs in clinical applications with deep learning models. However, the mainstream deep learning approaches usually neglect the periodic and formative attribute of the ECG heartbeat waveform. In this work, we propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals. More specifically, ECG signals are first split into heartbeat segments, and then structural features are extracted from each of the segments. Based on the structural features, a temporal model is designed to learn the temporal information for various clinical tasks. Further, due to the fact that massive ECG signals are available but the labeled data are very limited, we also explore self-supervised learning strategy to pre-train the models, resulting significant improvement for downstream tasks. The proposed method outperforms the baseline model and shows competitive performances compared with task-specific methods in three clinical applications: cardiac condition diagnosis, sleep apnea detection, and arrhythmia classification. Further, we find that the ECG-SL tends to focus more on each heartbeat's peak and ST range than ResNet by visualizing the saliency maps.