pwc-archive/papers-with-abstracts huggingface dataset:
Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks
Authors: Peng Yang, Ting Wang, Haibin Cai, Yuanming Shi, Chunxiao Jiang, Linling Kuang
Date: 2025-01-27 00:00:00

Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.
Classification Error Bound for Low Bayes Error Conditions in Machine Learning
Authors: Zijian Yang, Vahe Eminyan, Ralf Schlüter, Hermann Ney
Date: 2025-01-27 00:00:00
Tasks: Automatic Speech Recognition, Classification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replaced with a model distribution estimated from the training data in the Bayes decision rule. This substitution introduces a mismatch between the Bayes error and the model-based classification error. In this work, we apply classification error bounds to study the relationship between the error mismatch and the Kullback-Leibler divergence in machine learning. Motivated by recent observations of low model-based classification errors in many machine learning tasks, bounding the Bayes error to be lower, we propose a linear approximation of the classification error bound for low Bayes error conditions. Then, the bound for class priors are discussed. Moreover, we extend the classification error bound for sequences. Using automatic speech recognition as a representative example of machine learning applications, this work analytically discusses the correlations among different performance measures with extended bounds, including cross-entropy loss, language model perplexity, and word error rate.
Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data
Authors: Sheresh Zahoor, Pietro Liò, Gaël Dias, Mohammed Hasanuzzaman
Date: 2025-01-27 00:00:00
Tasks: Decision Making, Sensitivity

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional Machine Learning (ML) models excel at predicting outcomes, such as identifying high risk patients, they are limited in addressing what-if questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions. PCF leverages causal relationships from CBNs to structure PTrees, enabling both the quantification of factor impacts and simulation of hypothetical interventions. PCF was validated on three real-world healthcare datasets i.e. MIMIC-IV, Framingham Heart Study, and Diabetes, chosen for their clinically diverse variables. It demonstrated predictive performance comparable to traditional ML models while providing additional causal reasoning capabilities. To enhance interpretability, PCF incorporates sensitivity analysis and SHapley Additive exPlanations (SHAP). Sensitivity analysis quantifies the influence of causal parameters on outcomes such as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while SHAP highlights the importance of individual features in predictive modeling. By combining causal reasoning with predictive modeling, PCF bridges the gap between clinical intuition and data-driven insights. Its ability to uncover relationships between modifiable factors and simulate hypothetical scenarios provides clinicians with a clearer understanding of causal pathways. This approach supports more informed, evidence-based decision-making, offering a robust framework for addressing complex questions in diverse healthcare settings.
REINFORCE-ING Chemical Language Models in Drug Design
Authors: Morgan Thomas, Albert Bou, Gianni de Fabritiis
Date: 2025-01-27 00:00:00
Tasks: Drug Design

Chemical language models, combined with reinforcement learning, have shown significant promise to efficiently traverse large chemical spaces in drug design. However, the performance of various RL algorithms and their best practices for practical drug design are still unclear. Here, starting from the principles of the REINFORCE algorithm, we investigate the effect of different components from RL theory including experience replay, hill-climbing, baselines to reduce variance, and alternative reward shaping. Additionally we demonstrate how RL hyperparameters can be fine-tuned for effectiveness, efficiency, or chemical regularization as demonstrated using the MolOpt benchmark.
Inverse Reinforcement Learning via Convex Optimization
Authors: Hao Zhu, Yuan Zhang, Joschka Boedecker
Date: 2025-01-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We consider the inverse reinforcement learning (IRL) problem, where an unknown reward function of some Markov decision process is estimated based on observed expert demonstrations. In most existing approaches, IRL is formulated and solved as a nonconvex optimization problem, posing challenges in scenarios where robustness and reproducibility are critical. We discuss a convex formulation of the IRL problem (CIRL) initially proposed by Ng and Russel, and reformulate the problem such that the domain-specific language CVXPY can be applied directly to specify and solve the convex problem. We also extend the CIRL problem to scenarios where the expert policy is not given analytically but by trajectory as state-action pairs, which can be strongly inconsistent with optimality, by augmenting some of the constraints. Theoretical analysis and practical implementation for hyperparameter auto-selection are introduced. This note helps the users to easily apply CIRL for their problems, without background knowledge on convex optimization.
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data
Authors: Judith Sáinz-Pardo Díaz, Álvaro López García
Date: 2025-01-27 00:00:00
Tasks: Federated Learning, image-classification, Image Classification

The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has privacy and legal restrictions that in many cases prevent it from being processed from central servers. However, in this area collaboration between different research centers, in order to create models as robust as possible, trained with the largest quantity and diversity of data available, is a critical point to be taken into account. In this sense, the application of privacy aware distributed architectures, such as federated learning arises. When applying this type of architecture, the server aggregates the different local models trained with the data of each data owner to build a global model. This point is critical and therefore it is fundamental to analyze different ways of aggregation according to the use case, taking into account the distribution of the clients, the characteristics of the model, etc. In this paper we propose a novel aggregation strategy and we apply it to a use case of cerebral magnetic resonance image classification. In this use case the aggregation function proposed manages to improve the convergence obtained over the rounds of the federated learning process in relation to different aggregation strategies classically implemented and applied.
TimeHF: Billion-Scale Time Series Models Guided by Human Feedback
Authors: Yongzhi Qi, Hao Hu, Dazhou Lei, Jianshen Zhang, Zhengxin Shi, Yulin Huang, Zhengyu Chen, Xiaoming Lin, Zuo-Jun Max Shen
Date: 2025-01-27 00:00:00
Tasks: Time Series

Time series neural networks perform exceptionally well in real-world applications but encounter challenges such as limited scalability, poor generalization, and suboptimal zero-shot performance. Inspired by large language models, there is interest in developing large time series models (LTM) to address these issues. However, current methods struggle with training complexity, adapting human feedback, and achieving high predictive accuracy. We introduce TimeHF, a novel pipeline for creating LTMs with 6 billion parameters, incorporating human feedback. We use patch convolutional embedding to capture long time series information and design a human feedback mechanism called time-series policy optimization. Deployed in JD.com's supply chain, TimeHF handles automated replenishment for over 20,000 products, improving prediction accuracy by 33.21% over existing methods. This work advances LTM technology and shows significant industrial benefits.
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
Authors: Jingruo Sun, Zachary Frangella, Madeleine Udell
Date: 2025-01-27 00:00:00

Regularized empirical risk minimization (rERM) has become important in data-intensive fields such as genomics and advertising, with stochastic gradient methods typically used to solve the largest problems. However, ill-conditioned objectives and non-smooth regularizers undermine the performance of traditional stochastic gradient methods, leading to slow convergence and significant computational costs. To address these challenges, we propose the $\texttt{SAPPHIRE}$ ($\textbf{S}$ketching-based $\textbf{A}$pproximations for $\textbf{P}$roximal $\textbf{P}$reconditioning and $\textbf{H}$essian $\textbf{I}$nexactness with Variance-$\textbf{RE}$educed Gradients) algorithm, which integrates sketch-based preconditioning to tackle ill-conditioning and uses a scaled proximal mapping to minimize the non-smooth regularizer. This stochastic variance-reduced algorithm achieves condition-number-free linear convergence to the optimum, delivering an efficient and scalable solution for ill-conditioned composite large-scale convex machine learning problems. Extensive experiments on lasso and logistic regression demonstrate that $\texttt{SAPPHIRE}$ often converges $20$ times faster than other common choices such as $\texttt{Catalyst}$, $\texttt{SAGA}$, and $\texttt{SVRG}$. This advantage persists even when the objective is non-convex or the preconditioner is infrequently updated, highlighting its robust and practical effectiveness.
Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
Authors: Chengting Yu, Xiaochen Zhao, Lei Liu, Shu Yang, Gaoang Wang, Erping Li, Aili Wang
Date: 2025-01-27 00:00:00
Tasks: Knowledge Distillation

Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neuromorphic hardware. Despite this, SNNs often suffer from accuracy degradation compared to ANNs and face deployment challenges due to fixed inference timesteps, which require retraining for adjustments, limiting operational flexibility. To address these issues, our work considers the spatio-temporal property inherent in SNNs, and proposes a novel distillation framework for deep SNNs that optimizes performance across full-range timesteps without specific retraining, enhancing both efficacy and deployment adaptability. We provide both theoretical analysis and empirical validations to illustrate that training guarantees the convergence of all implicit models across full-range timesteps. Experimental results on CIFAR-10, CIFAR-100, CIFAR10-DVS, and ImageNet demonstrate state-of-the-art performance among distillation-based SNNs training methods. Our code is available at https://github.com/Intelli-Chip-Lab/snn\_temporal\_decoupling\_distillation.
SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub
Authors: Benjamin C. Carter, Jonathan Rivas Contreras, Carlos A. Llanes Villegas, Pawan Acharya, Jack Utzerath, Adonijah O. Farner, Hunter Jenkins, Dylan Johnson, Jacob Penney, Igor Steinmacher, Marco A. Gerosa, Fabio Santos
Date: 2025-01-27 00:00:00

New contributors often struggle to find tasks that they can tackle when onboarding onto a new Open Source Software (OSS) project. One reason for this difficulty is that issue trackers lack explanations about the knowledge or skills needed to complete a given task successfully. These explanations can be complex and time-consuming to produce. Past research has partially addressed this problem by labeling issues with issue types, issue difficulty level, and issue skills. However, current approaches are limited to a small set of labels and lack in-depth details about their semantics, which may not sufficiently help contributors identify suitable issues. To surmount this limitation, this paper explores large language models (LLMs) and Random Forest (RF) to predict the multilevel skills required to solve the open issues. We introduce a novel tool, SkillScope, which retrieves current issues from Java projects hosted on GitHub and predicts the multilevel programming skills required to resolve these issues. In a case study, we demonstrate that SkillScope could predict 217 multilevel skills for tasks with 91% precision, 88% recall, and 89% F-measure on average. Practitioners can use this tool to better delegate or choose tasks to solve in OSS projects.
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
Authors: Michael Muehlebach, Zhiyu He, Michael I. Jordan
Date: 2025-01-27 00:00:00

We study the sample complexity of online reinforcement learning in the general setting of nonlinear dynamical systems with continuous state and action spaces. Our analysis accommodates a large class of dynamical systems ranging from a finite set of nonlinear candidate models to models with bounded and Lipschitz continuous dynamics, to systems that are parametrized by a compact and real-valued set of parameters. In the most general setting, our algorithm achieves a policy regret of $\mathcal{O}(N \epsilon^2 + \mathrm{ln}(m(\epsilon))/\epsilon^2)$, where $N$ is the time horizon, $\epsilon$ is a user-specified discretization width, and $m(\epsilon)$ measures the complexity of the function class under consideration via its packing number. In the special case where the dynamics are parametrized by a compact and real-valued set of parameters (such as neural networks, transformers, etc.), we prove a policy regret of $\mathcal{O}(\sqrt{N p})$, where $p$ denotes the number of parameters, recovering earlier sample-complexity results that were derived for linear time-invariant dynamical systems. While this article focuses on characterizing sample complexity, the proposed algorithms are likely to be useful in practice, due to their simplicity, their ability to incorporate prior knowledge, and their benign transient behavior.
Investigating the Sensitivity of Pre-trained Audio Embeddings to Common Effects
Authors: Victor Deng, Changhong Wang, Gael Richard, Brian McFee
Date: 2025-01-27 00:00:00
Tasks: Sensitivity

In recent years, foundation models have significantly advanced data-driven systems across various domains. Yet, their underlying properties, especially when functioning as feature extractors, remain under-explored. In this paper, we investigate the sensitivity to audio effects of audio embeddings extracted from widely-used foundation models, including OpenL3, PANNs, and CLAP. We focus on audio effects as the source of sensitivity due to their prevalent presence in large audio datasets. By applying parameterized audio effects (gain, low-pass filtering, reverberation, and bitcrushing), we analyze the correlation between the deformation trajectories and the effect strength in the embedding space. We propose to quantify the dimensionality and linearizability of the deformation trajectories induced by audio effects using canonical correlation analysis. We find that there exists a direction along which the embeddings move monotonically as the audio effect strength increases, but that the subspace containing the displacements is generally high-dimensional. This shows that pre-trained audio embeddings do not globally linearize the effects. Our empirical results on instrument classification downstream tasks confirm that projecting out the estimated deformation directions cannot generally improve the robustness of pre-trained embeddings to audio effects.
Benchmarking Quantum Reinforcement Learning
Authors: Nico Meyer, Christian Ufrecht, George Yammine, Georgios Kontes, Christopher Mutschler, Daniel D. Scherer
Date: 2025-01-27 00:00:00
Tasks: Benchmarking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.
Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data
Authors: Luis Bote-Curiel, Sergio Ruiz-Llorente, Sergio Muñoz-Romero, Mónica Yagüe-Fernández, Arantzazu Barquín, Jesús García-Donas, José Luis Rojo-Álvarez
Date: 2025-01-27 00:00:00
Tasks: feature selection

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a nonlinear examination of an OC dataset using autoencoders, which compress data into a 3-dimensional latent space to detect potential intrinsic separability between platinum-sensitive and platinum-resistant groups; and (2) an adaptation of the informative variable identifier (IVI) to determine which features (clinical or genetic) are most relevant to disease progression. In the autoencoder analysis, a clearer pattern emerged when using clinical features and the combination of clinical and genetic data, indicating that disease progression groups can be distinguished more effectively after supervised fine tuning. For genetic data alone, this separability was less apparent but became more pronounced with a supervised approach. Using the IVI-based feature selection, key clinical variables (such as type of surgery and neoadjuvant chemotherapy) and certain gene mutations showed strong relevance, along with low-risk genetic factors. These findings highlight the strength of combining machine learning tools (autoencoders) with feature selection methods (IVI) to gain insights into ovarian cancer progression. They also underscore the potential for identifying new biomarkers that integrate clinical and genomic indicators, ultimately contributing to improved patient stratification and personalized treatment strategies.
LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models
Authors: Yuewen Mei, Tong Nie, Jian Sun, Ye Tian
Date: 2025-01-27 00:00:00
Tasks: Autonomous Driving

Ensuring and improving the safety of autonomous driving systems (ADS) is crucial for the deployment of highly automated vehicles, especially in safety-critical events. To address the rarity issue, adversarial scenario generation methods are developed, in which behaviors of traffic participants are manipulated to induce safety-critical events. However, existing methods still face two limitations. First, identification of the adversarial participant directly impacts the effectiveness of the generation. However, the complexity of real-world scenarios, with numerous participants and diverse behaviors, makes identification challenging. Second, the potential of generated safety-critical scenarios to continuously improve ADS performance remains underexplored. To address these issues, we propose LLM-attacker: a closed-loop adversarial scenario generation framework leveraging large language models (LLMs). Specifically, multiple LLM agents are designed and coordinated to identify optimal attackers. Then, the trajectories of the attackers are optimized to generate adversarial scenarios. These scenarios are iteratively refined based on the performance of ADS, forming a feedback loop to improve ADS. Experimental results show that LLM-attacker can create more dangerous scenarios than other methods, and the ADS trained with it achieves a collision rate half that of training with normal scenarios. This indicates the ability of LLM-attacker to test and enhance the safety and robustness of ADS. Video demonstrations are provided at: https://drive.google.com/file/d/1Zv4V3iG7825oyiKbUwS2Y-rR0DQIE1ZA/view.
Gaussian Process-Based Prediction and Control of Hammerstein-Wiener Systems
Authors: Mingzhou Yin, Matthias A. Müller
Date: 2025-01-27 00:00:00
Tasks: LEMMA, Prediction

This work investigates data-driven prediction and control of Hammerstein-Wiener systems using physics-informed Gaussian process models. Data-driven prediction algorithms have been developed for structured nonlinear systems based on Willems' fundamental lemma. However, existing frameworks cannot treat output nonlinearities and require a dictionary of basis functions for Hammerstein systems. In this work, an implicit predictor structure is considered, leveraging the multi-step-ahead ARX structure for the linear part of the model. This implicit function is learned by Gaussian process regression with kernel functions designed from Gaussian process priors for the nonlinearities. The linear model parameters are estimated as hyperparameters by assuming a stable spline hyperprior. The implicit Gaussian process model provides explicit output prediction by optimizing selected optimality criteria. The model is also applied to receding horizon control with the expected control cost and chance constraint satisfaction guarantee. Numerical results demonstrate that the proposed prediction and control algorithms are superior to black-box Gaussian process models.