pwc-archive/papers-with-abstracts huggingface dataset:
Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses
Authors: Ramtin Zargari Marandi, Anne Svane Frahm, Jens Lundgren, Daniel Dawson Murray, Maja Milojevic
Date: 2025-01-08 00:00:00
Tasks: Binary Classification, Feature Importance

While machine learning offers diverse techniques suitable for exploring various medical research questions, a cohesive synergistic framework can facilitate the integration and understanding of new approaches within unified model development and interpretation. We therefore introduce the Medical Artificial Intelligence Toolbox (MAIT), an explainable, open-source Python pipeline for developing and evaluating binary classification, regression, and survival models on tabular datasets. MAIT addresses key challenges (e.g., high dimensionality, class imbalance, mixed variable types, and missingness) while promoting transparency in reporting (TRIPOD+AI compliant). Offering automated configurations for beginners and customizable source code for experts, MAIT streamlines two primary use cases: Discovery (feature importance via unified scoring, e.g., SHapley Additive exPlanations - SHAP) and Prediction (model development and deployment with optimized solutions). Moreover, MAIT proposes new techniques including fine-tuning of probability threshold in binary classification, translation of cumulative hazard curves to binary classification, enhanced visualizations for model interpretation for mixed data types, and handling censoring through semi-supervised learning, to adapt to a wide set of data constraints and study designs. We provide detailed tutorials on GitHub, using four open-access data sets, to demonstrate how MAIT can be used to improve implementation and interpretation of ML models in medical research.
HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs
Authors: Nicolò Botteghi, Stefania Fresca, Mengwu Guo, Andrea Manzoni
Date: 2025-01-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In this work, we devise a new, general-purpose reinforcement learning strategy for the optimal control of parametric partial differential equations (PDEs). Such problems frequently arise in applied sciences and engineering and entail a significant complexity when control and/or state variables are distributed in high-dimensional space or depend on varying parameters. Traditional numerical methods, relying on either iterative minimization algorithms or dynamic programming, while reliable, often become computationally infeasible. Indeed, in either way, the optimal control problem must be solved for each instance of the parameters, and this is out of reach when dealing with high-dimensional time-dependent and parametric PDEs. In this paper, we propose HypeRL, a deep reinforcement learning (DRL) framework to overcome the limitations shown by traditional methods. HypeRL aims at approximating the optimal control policy directly. Specifically, we employ an actor-critic DRL approach to learn an optimal feedback control strategy that can generalize across the range of variation of the parameters. To effectively learn such optimal control laws, encoding the parameter information into the DRL policy and value function neural networks (NNs) is essential. To do so, HypeRL uses two additional NNs, often called hypernetworks, to learn the weights and biases of the value function and the policy NNs. We validate the proposed approach on two PDE-constrained optimal control benchmarks, namely a 1D Kuramoto-Sivashinsky equation and a 2D Navier-Stokes equations, by showing that the knowledge of the PDE parameters and how this information is encoded, i.e., via a hypernetwork, is an essential ingredient for learning parameter-dependent control policies that can generalize effectively to unseen scenarios and for improving the sample efficiency of such policies.
A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes
Authors: Qingmei Wang, Yuxin Wu, Yujie Long, Jing Huang, Fengyuan Ran, Bing Su, Hongteng Xu
Date: 2025-01-08 00:00:00
Tasks: Point Processes

An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches. The code is available at \url{https://github.com/qingmeiwangdaily/BADMM_TPP}.
Histogram-Equalized Quantization for logic-gated Residual Neural Networks
Authors: Van Thien Nguyen, William Guicquero, Gilles Sicard
Date: 2025-01-08 00:00:00
Tasks: Quantization

Adjusting the quantization according to the data or to the model loss seems mandatory to enable a high accuracy in the context of quantized neural networks. This work presents Histogram-Equalized Quantization (HEQ), an adaptive framework for linear symmetric quantization. HEQ automatically adapts the quantization thresholds using a unique step size optimization. We empirically show that HEQ achieves state-of-the-art performances on CIFAR-10. Experiments on the STL-10 dataset even show that HEQ enables a proper training of our proposed logic-gated (OR, MUX) residual networks with a higher accuracy at a lower hardware complexity than previous work.
Safe Reinforcement Learning with Minimal Supervision
Authors: Alexander Quessy, Thomas Richardson, Sebastian East
Date: 2025-01-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement learning (RL) in the real world necessitates the development of procedures that enable agents to explore without causing harm to themselves or others. The most successful solutions to the problem of safe RL leverage offline data to learn a safe-set, enabling safe online exploration. However, this approach to safe-learning is often constrained by the demonstrations that are available for learning. In this paper we investigate the influence of the quantity and quality of data used to train the initial safe learning problem offline on the ability to learn safe-RL policies online. Specifically, we focus on tasks with spatially extended goal states where we have few or no demonstrations available. Classically this problem is addressed either by using hand-designed controllers to generate data or by collecting user-generated demonstrations. However, these methods are often expensive and do not scale to more complex tasks and environments. To address this limitation we propose an unsupervised RL-based offline data collection procedure, to learn complex and scalable policies without the need for hand-designed controllers or user demonstrations. Our research demonstrates the significance of providing sufficient demonstrations for agents to learn optimal safe-RL policies online, and as a result, we propose optimistic forgetting, a novel online safe-RL approach that is practical for scenarios with limited data. Further, our unsupervised data collection approach highlights the need to balance diversity and optimality for safe online exploration.
Regularising NARX models with multi-task learning
Authors: Sarah Bee, Lawrence Bull, Nikolaos Dervilis, Keith Worden
Date: 2025-01-08 00:00:00
Tasks: Multi-Task Learning

A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previous external input variables. One limitation of NARX models is their propensity to overfit and result in poor generalisation for future predictions. The proposed method to help to overcome the issue of overfitting is a NARX model which predicts outputs at both the current time and several lead times into the future. This is a form of multi-task learner (MTL); whereby the lead time outputs will regularise the current time output. This work shows that for high noise level, MTL can be used to regularise NARX with a lower Normalised Mean Square Error (NMSE) compared to the NMSE of the independent learner counterpart.
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning
Authors: Bin Li, Xiaoye Miao, Yongheng Shang, Xinkui Zhao, Shuiguang Deng, Jianwei Yin
Date: 2025-01-08 00:00:00
Tasks: Federated Learning

Decentralized federated learning (DFL) is inherently vulnerable to poisoning attacks, as malicious clients can transmit manipulated model gradients to neighboring clients. Existing defense methods either reject suspicious gradients per iteration or restart DFL aggregation after detecting all malicious clients. They overlook the potential accuracy benefit from the discarded malicious gradients. In this paper, we propose a novel gradient purification defense, named GPD, that integrates seamlessly with existing DFL aggregation to defend against poisoning attacks. It aims to mitigate the harm in model gradients while retaining the benefit in model weights for enhancing accuracy. For each benign client in GPD, a recording variable is designed to track the historically aggregated gradients from one of its neighbors. It allows benign clients to precisely detect malicious neighbors and swiftly mitigate aggregated malicious gradients via historical consistency checks. Upon mitigation, GPD optimizes model weights via aggregating gradients solely from benign clients. This retains the previously beneficial portions from malicious clients and exploits the contributions from benign clients, thereby significantly enhancing the model accuracy. We analyze the convergence of GPD, as well as its ability to harvest high accuracy. Extensive experiments over three datasets demonstrate that, GPD is capable of mitigating poisoning attacks under both iid and non-iid data distributions. It significantly outperforms state-of-the-art defenses in terms of accuracy against various poisoning attacks.
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Authors: Ruichen Luo, Sebastian U Stich, Samuel Horváth, Martin Takáč
Date: 2025-01-08 00:00:00
Tasks: Federated Learning, Stochastic Optimization

LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated learning. However, rigorously establishing their theoretical advantages over simpler methods, such as minibatch SGD (MbSGD), has proven challenging, as existing analyses often rely on strong assumptions, unrealistic premises, or overly restrictive scenarios. In this work, we revisit the convergence properties of LocalSGD and SCAFFOLD under a variety of existing or weaker conditions, including gradient similarity, Hessian similarity, weak convexity, and Lipschitz continuity of the Hessian. Our analysis shows that (i) LocalSGD achieves faster convergence compared to MbSGD for weakly convex functions without requiring stronger gradient similarity assumptions; (ii) LocalSGD benefits significantly from higher-order similarity and smoothness; and (iii) SCAFFOLD demonstrates faster convergence than MbSGD for a broader class of non-quadratic functions. These theoretical insights provide a clearer understanding of the conditions under which LocalSGD and SCAFFOLD outperform MbSGD.
Motif Discovery Framework for Psychiatric EEG Data Classification
Authors: Melanija Kraljevska, Katerina Hlavackova-Schindler, Lukas Miklautz, Claudia Plant
Date: 2025-01-08 00:00:00
Tasks: Classification, EEG

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general.
Risk-averse policies for natural gas futures trading using distributional reinforcement learning
Authors: Félicien Hêche, Biagio Nigro, Oussama Barakat, Stephan Robert-Nicoud
Date: 2025-01-08 00:00:00
Tasks: Distributional Reinforcement Learning, energy trading, quantile regression, Reinforcement Learning (RL)

Financial markets have experienced significant instabilities in recent years, creating unique challenges for trading and increasing interest in risk-averse strategies. Distributional Reinforcement Learning (RL) algorithms, which model the full distribution of returns rather than just expected values, offer a promising approach to managing market uncertainty. This paper investigates this potential by studying the effectiveness of three distributional RL algorithms for natural gas futures trading and exploring their capacity to develop risk-averse policies. Specifically, we analyze the performance and behavior of Categorical Deep Q-Network (C51), Quantile Regression Deep Q-Network (QR-DQN), and Implicit Quantile Network (IQN). To the best of our knowledge, these algorithms have never been applied in a trading context. These policies are compared against five Machine Learning (ML) baselines, using a detailed dataset provided by Predictive Layer SA, a company supplying ML-based strategies for energy trading. The main contributions of this study are as follows. (1) We demonstrate that distributional RL algorithms significantly outperform classical RL methods, with C51 achieving performance improvement of more than 32\%. (2) We show that training C51 and IQN to maximize CVaR produces risk-sensitive policies with adjustable risk aversion. Specifically, our ablation studies reveal that lower CVaR confidence levels increase risk aversion, while higher levels decrease it, offering flexible risk management options. In contrast, QR-DQN shows less predictable behavior. These findings emphasize the potential of distributional RL for developing adaptable, risk-averse trading strategies in volatile markets.
Machine Learning and statistical classification of CRISPR-Cas12a diagnostic assays
Authors: Nathan Khosla, Jake M. Lesinski, Marcus Haywood-Alexander, Andrew J. deMello, Daniel A. Richards
Date: 2025-01-08 00:00:00
Tasks: Diagnostic, Specificity

CRISPR-based diagnostics have gained increasing attention as biosensing tools able to address limitations in contemporary molecular diagnostic tests. To maximise the performance of CRISPR-based assays, much effort has focused on optimizing the chemistry and biology of the biosensing reaction. However, less attention has been paid to improving the techniques used to analyse CRISPR-based diagnostic data. To date, diagnostic decisions typically involve various forms of slope-based classification. Such methods are superior to traditional methods based on assessing absolute signals, but still have limitations. Herein, we establish performance benchmarks (total accuracy, sensitivity, and specificity) using common slope-based methods. We compare the performance of these benchmark methods with three different quadratic empirical distribution function statistical tests, finding significant improvements in diagnostic speed and accuracy when applied to a clinical data set. Two of the three statistical techniques, the Kolmogorov-Smirnov and Anderson-Darling tests, report the lowest time-to-result and highest total test accuracy. Furthermore, we developed a long short-term memory recurrent neural network to classify CRISPR-biosensing data, achieving 100% specificity on our model data set. Finally, we provide guidelines on choosing the classification method and classification method parameters that best suit a diagnostic assays needs.
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
Authors: Xiaoye Miao, Bin Li, Yangyang Wu, Meng Xi, Xinkui Zhao, Jianwei Yin
Date: 2025-01-08 00:00:00
Tasks: Federated Learning, Privacy Preserving

Privacy concerns arise as sensitive data proliferate. Despite decentralized federated learning (DFL) aggregating gradients from neighbors to avoid direct data transmission, it still poses indirect data leaks from the transmitted gradients. Existing privacy-preserving methods for DFL add noise to gradients. They either diminish the model predictive accuracy or suffer from ineffective gradient protection. In this paper, we propose a novel lossless privacy-preserving aggregation rule named LPPA to enhance gradient protection as much as possible but without loss of DFL model predictive accuracy. LPPA subtly injects the noise difference between the sent and received noise into transmitted gradients for gradient protection. The noise difference incorporates neighbors' randomness for each client, effectively safeguarding against data leaks. LPPA employs the noise flow conservation theory to ensure that the noise impact can be globally eliminated. The global sum of all noise differences remains zero, ensuring that accurate gradient aggregation is unaffected and the model accuracy remains intact. We theoretically prove that the privacy-preserving capacity of LPPA is \sqrt{2} times greater than that of noise addition, while maintaining comparable model accuracy to the standard DFL aggregation without noise injection. Experimental results verify the theoretical findings and show that LPPA achieves a 13% mean improvement in accuracy over noise addition. We also demonstrate the effectiveness of LPPA in protecting raw data and guaranteeing lossless model accuracy.
Rising Rested MAB with Linear Drift
Authors: Omer Amichay, Yishay Mansour
Date: 2025-01-08 00:00:00

We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result is a tight regret bound of $\tilde{\Theta}(T^{4/5}K^{3/5})$, by providing both upper and lower bounds. We extend our results to derive instance dependent regret bounds, which depend on the unknown parametrization of the linear drift of the rewards.
Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
Authors: Thibaud Ardoin, Niklas Pauli, Benedikt Groß, Mahsa Kholghi, Khan Reaz, Gerhard Wunder
Date: 2025-01-08 00:00:00

Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer security, but might also allow undesired tracking of the devices. The scope of this paper is to explore the feasibility of applying RFF to UWB and investigates how well this technique generalizes across different environments. We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning. Moreover, we developed an improved deep learning pipeline to extract the hardware signature from the signal data. In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations.
The unbearable lightness of Restricted Boltzmann Machines: Theoretical Insights and Biological Applications
Authors: Giovanni di Sarra, Barbara Bravi, Yasser Roudi
Date: 2025-01-08 00:00:00

Restricted Boltzmann Machines are simple yet powerful neural networks. They can be used for learning structure in data, and are used as a building block of more complex neural architectures. At the same time, their simplicity makes them easy to use, amenable to theoretical analysis, yielding interpretable models in applications. Here, we focus on reviewing the role that the activation functions, describing the input-output relationship of single neurons in RBM, play in the functionality of these models. We discuss recent theoretical results on the benefits and limitations of different activation functions. We also review applications to biological data analysis, namely neural data analysis, where RBM units are mostly taken to have sigmoid activation functions and binary units, to protein data analysis and immunology where non-binary units and non-sigmoid activation functions have recently been shown to yield important insights into the data. Finally, we discuss open problems addressing which can shed light on broader issues in neural network research.