pwc-archive/papers-with-abstracts huggingface dataset:
Behavior of prediction performance metrics with rare events
Authors: Emily Minus, R. Yates Coley, Susan M. Shortreed, Brian D. Williamson
Date: 2025-04-22 00:00:00
Tasks: Prediction, Sensitivity, Specificity

Area under the receiving operator characteristic curve (AUC) is commonly reported alongside binary prediction models. However, there are concerns that AUC might be a misleading measure of prediction performance in the rare event setting. This setting is common since many events of clinical importance are rare events. We conducted a simulation study to determine when or whether AUC is unstable in the rare event setting. Specifically, we aimed to determine whether the bias and variance of AUC are driven by the number of events or the event rate. We also investigated the behavior of other commonly used measures of prediction performance, including positive predictive value, accuracy, sensitivity, and specificity. Our results indicate that poor AUC behavior -- as measured by empirical bias, variability of cross-validated AUC estimates, and empirical coverage of confidence intervals -- is driven by the minimum class size, not event rate. Performance of sensitivity is driven by the number of events, while that of specificity is driven by the number of non-events. Other measures, including positive predictive value and accuracy, depend on the event rate even in large samples. AUC is reliable in the rare event setting provided that the total number of events is moderately large.
Periodic Proprioceptive Stimuli Learning and Internal Model Development for Avian-inspired Flapping-wing Flight State Estimation
Authors: Chen Qian, Jiaxi Xing, Jifu Yan, Mingyu Luo, Shiyu Song, Xuyi Lian, Yongchun Fang, Fei Gao, Tiefeng Li
Date: 2025-04-29 00:00:00
Tasks: State Estimation

This paper presents a novel learning-based approach for online state estimation in flapping wing aerial vehicles (FWAVs). Leveraging low-cost Magnetic, Angular Rate, and Gravity (MARG) sensors, the proposed method effectively mitigates the adverse effects of flapping-induced oscillations that challenge conventional estimation techniques. By employing a divide-and-conquer strategy grounded in cycle-averaged aerodynamics, the framework decouples the slow-varying components from the high-frequency oscillatory components, thereby preserving critical transient behaviors while delivering an smooth internal state representation. The complete oscillatory state of FWAV can be reconstructed based on above two components, leading to substantial improvements in accurate state prediction. Experimental validations on an avian-inspired FWAV demonstrate that the estimator enhances accuracy and smoothness, even under complex aerodynamic disturbances. These encouraging results highlight the potential of learning algorithms to overcome issues of flapping-wing induced oscillation dynamics.
Universal Approximation with Softmax Attention
Authors: Jerry Yao-Chieh Hu, Hude Liu, Hong-Yu Chen, Weimin Wu, Han Liu
Date: 2025-04-22 00:00:00

We prove that with linear transformations, both (i) two-layer self-attention and (ii) one-layer self-attention followed by a softmax function are universal approximators for continuous sequence-to-sequence functions on compact domains. Our main technique is a new interpolation-based method for analyzing attention's internal mechanism. This leads to our key insight: self-attention is able to approximate a generalized version of ReLU to arbitrary precision, and hence subsumes many known universal approximators. Building on these, we show that two-layer multi-head attention alone suffices as a sequence-to-sequence universal approximator. In contrast, prior works rely on feed-forward networks to establish universal approximation in Transformers. Furthermore, we extend our techniques to show that, (softmax-)attention-only layers are capable of approximating various statistical models in-context. We believe these techniques hold independent interest.
Achieving Distributive Justice in Federated Learning via Uncertainty Quantification
Authors: Alycia Carey, Xintao Wu
Date: 2025-04-22 00:00:00
Tasks: Ethics, Fairness, Federated Learning, Generalization Bounds, Uncertainty Quantification

Client-level fairness metrics for federated learning are used to ensure that all clients in a federation either: a) have similar final performance on their local data distributions (i.e., client parity), or b) obtain final performance on their local data distributions relative to their contribution to the federated learning process (i.e., contribution fairness). While a handful of works that propose either client-parity or contribution-based fairness metrics ground their definitions and decisions in social theories of equality -- such as distributive justice -- most works arbitrarily choose what notion of fairness to align with which makes it difficult for practitioners to choose which fairness metric aligns best with their fairness ethics. In this work, we propose UDJ-FL (Uncertainty-based Distributive Justice for Federated Learning), a flexible federated learning framework that can achieve multiple distributive justice-based client-level fairness metrics. Namely, by utilizing techniques inspired by fair resource allocation, in conjunction with performing aleatoric uncertainty-based client weighing, our UDJ-FL framework is able to achieve egalitarian, utilitarian, Rawls' difference principle, or desert-based client-level fairness. We empirically show the ability of UDJ-FL to achieve all four defined distributive justice-based client-level fairness metrics in addition to providing fairness equivalent to (or surpassing) other popular fair federated learning works. Further, we provide justification for why aleatoric uncertainty weighing is necessary to the construction of our UDJ-FL framework as well as derive theoretical guarantees for the generalization bounds of UDJ-FL. Our code is publicly available at https://github.com/alycia-noel/UDJ-FL.
Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels
Authors: Georgios Mavroudeas, Malik Magdon-Ismail, Kristin P. Bennett, Jason Kuruzovich
Date: 2025-04-22 00:00:00
Tasks: Causal Inference

A treatment may be appropriate for some group (the ``sick" group) on whom it has a positive effect, but it can also have a detrimental effect on subjects from another group (the ``healthy" group). In a non-targeted trial both sick and healthy subjects may be treated, producing heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then difficult, because the effects on the different groups get tangled. We propose an efficient nonparametric approach to estimating the group effects, called {\bf PCM} (pre-cluster and merge). We prove its asymptotic consistency in a general setting and show, on synthetic data, more than a 10x improvement in accuracy over existing state-of-the-art. Our approach applies more generally to consistent estimation of functions with a finite range.
An Online Cross-layered Defense Strategy with Bandwidth Allocation for Multi-channel Systems under DoS Attacks
Authors: Liheng Wan, Panshuo Li, James Lam
Date: 2025-04-29 00:00:00

This paper proposes an online cross-layered defense strategy for multi-channel systems with switched dynamics under DoS attacks. The enabling condition of a channel under attacks is formulated with respect to attack flow and channel bandwidth, then a new networked control system model bridging the gap between system dynamics and network deployment is built. Based on this, the cross-layered defense strategy is proposed. It jointly optimizes the controller gain and bandwidth allocation of channels according to the real-time attack flow and system dynamics, by solving a mixed-integer semidefinite programming online. A smart enumeration algorithm for non-convex bi-level optimization is proposed to analyze the stability under the strategy. Numerical examples are given to illustrate the high resilience from the cross-layered feature.
Riemannian Neural Geodesic Interpolant
Authors: Jiawen Wu, Bingguang Chen, Yuyi Zhou, Qi Meng, Rongchan Zhu, Zhi-Ming Ma
Date: 2025-04-22 00:00:00

Stochastic interpolants are efficient generative models that bridge two arbitrary probability density functions in finite time, enabling flexible generation from the source to the target distribution or vice versa. These models are primarily developed in Euclidean space, and are therefore limited in their application to many distribution learning problems defined on Riemannian manifolds in real-world scenarios. In this work, we introduce the Riemannian Neural Geodesic Interpolant (RNGI) model, which interpolates between two probability densities on a Riemannian manifold along the stochastic geodesics, and then samples from one endpoint as the final state using the continuous flow originating from the other endpoint. We prove that the temporal marginal density of RNGI solves a transport equation on the Riemannian manifold. After training the model's the neural velocity and score fields, we propose the Embedding Stochastic Differential Equation (E-SDE) algorithm for stochastic sampling of RNGI. E-SDE significantly improves the sampling quality by reducing the accumulated error caused by the excessive intrinsic discretization of Riemannian Brownian motion in the classical Geodesic Random Walk (GRW) algorithm. We also provide theoretical bounds on the generative bias measured in terms of KL-divergence. Finally, we demonstrate the effectiveness of the proposed RNGI and E-SDE through experiments conducted on both collected and synthetic distributions on S2 and SO(3).
A Decentralized Local Flexibility Market for Local Energy Communities to Mitigate Grid Congestion: A Case Study in Sweden
Authors: Maryam Mohiti, Mohammadreza Mazidi, David Steen, Le Anh Tuan
Date: 2025-04-29 00:00:00
Tasks: Management

This paper proposes a preventive congestion management framework with joint Local Flexibility Capacity Market (LFCM) and Local Energy Markets (LEMs). The framework enables Local Energy Communities (LECs) to optimize their flexibility potential across the LEM, LFCM, and heat markets. The LECs utilize their heat and electricity resources to offer flexibility services to Distribution System Operators (DSOs) for congestion relief. In this framework, energy and flexibility are treated as separate variables, each subject to different pricing scheme. Flexibility prices are market-driven, dynamically reflecting the location and severity of congestion. A case study conducted at Chalmers University of Technology, Sweden, shows that the proposed framework can effectively mitigate congestion by trading the LECs flexibility in the LFCM. The study also highlights up to 40% financial benefits for LECs, promoting the LFCM as a viable solution for congestion management in future decentralized energy systems.
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning
Authors: Zhe Huang, Simone Rossi, Rui Yuan, Thomas Hannagan
Date: 2025-04-22 00:00:00
Tasks: Conformal Prediction, In-Context Learning, Prediction, Prediction Intervals, regression, Uncertainty Quantification

Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference time. However, uncertainty quantification for ICL remains an open challenge, particularly in noisy regression tasks. This paper investigates whether ICL can be leveraged for distribution-free uncertainty estimation, proposing a method based on conformal prediction to construct prediction intervals with guaranteed coverage. While traditional conformal methods are computationally expensive due to repeated model fitting, we exploit ICL to efficiently generate confidence intervals in a single forward pass. Our empirical analysis compares this approach against ridge regression-based conformal methods, showing that conformal prediction with in-context learning (CP with ICL) achieves robust and scalable uncertainty estimates. Additionally, we evaluate its performance under distribution shifts and establish scaling laws to guide model training. These findings bridge ICL and conformal prediction, providing a theoretically grounded and new framework for uncertainty quantification in transformer-based models.
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources
Authors: Costas Mylonas, Emmanouel Varvarigos, Georgios Tsaousoglou
Date: 2025-04-29 00:00:00
Tasks: Decision Making, Management, Multi-agent Reinforcement Learning

Modern renewables-based power systems need to tap on the flexibility of Distributed Energy Resources (DERs) connected to distribution networks. It is important, however, that DER owners/users remain in control of their assets, decisions, and objectives. At the same time, the dynamic landscape of DER-penetrated distribution networks calls for agile, data-driven flexibility management frameworks. In the face of these developments, the Multi-Agent Reinforcement Learning (MARL) paradigm is gaining significant attention, as a distributed and data-driven decision-making policy. This paper addresses the need for bottom-up DER management decisions to account for the distribution network's safety-related constraints. While the related literature on safe MARL typically assumes that network characteristics are available and incorporated into the policy's safety layer, which implies active DSO engagement, this paper ensures that self-organized DER communities are enabled to provide distribution-network-safe flexibility services without relying on the aspirational and problematic requirement of bringing the DSO in the decision-making loop.
Estimation of Tissue Deformation and Interactive Force in Robotic Surgery through Vision-based Learning
Authors: Srikar Annamraju, Yuxi Chen, Jooyoung Lim, Inki Kim
Date: 2025-04-29 00:00:00

Goal: A limitation in robotic surgery is the lack of force feedback, due to challenges in suitable sensing techniques. To enhance the perception of the surgeons and precise force rendering, estimation of these forces along with tissue deformation level is presented here. Methods: An experimental test bed is built for studying the interaction, and the forces are estimated from the raw data. Since tissue deformation and stiffness are non-linearly related, they are independently computed for enhanced reliability. A Convolutional Neural Network (CNN) based vision model is deployed, and both classification and regression models are developed. Results: The forces applied on the tissue are estimated, and the tissue is classified based on its deformation. The exact deformation of the tissue is also computed. Conclusions: The surgeons can render precise forces and detect tumors using the proposed method. The rarely discussed efficacy of computing the deformation level is also demonstrated.
Transfer Learning for High-dimensional Reduced Rank Time Series Models
Authors: Mingliang Ma Abolfazl Safikhani
Date: 2025-04-22 00:00:00
Tasks: Time Series, Transfer Learning

The objective of transfer learning is to enhance estimation and inference in a target data by leveraging knowledge gained from additional sources. Recent studies have explored transfer learning for independent observations in complex, high-dimensional models assuming sparsity, yet research on time series models remains limited. Our focus is on transfer learning for sequences of observations with temporal dependencies and a more intricate model parameter structure. Specifically, we investigate the vector autoregressive model (VAR), a widely recognized model for time series data, where the transition matrix can be deconstructed into a combination of a sparse matrix and a low-rank one. We propose a new transfer learning algorithm tailored for estimating high-dimensional VAR models characterized by low-rank and sparse structures. Additionally, we present a novel approach for selecting informative observations from auxiliary datasets. Theoretical guarantees are established, encompassing model parameter consistency, informative set selection, and the asymptotic distribution of estimators under mild conditions. The latter facilitates the construction of entry-wise confidence intervals for model parameters. Finally, we demonstrate the empirical efficacy of our methodologies through both simulated and real-world datasets.
Dimension-Free Decision Calibration for Nonlinear Loss Functions
Authors: Jingwu Tang, Jiayun Wu, Zhiwei Steven Wu, Jiahao Zhang
Date: 2025-04-22 00:00:00

When model predictions inform downstream decision making, a natural question is under what conditions can the decision-makers simply respond to the predictions as if they were the true outcomes. Calibration suffices to guarantee that simple best-response to predictions is optimal. However, calibration for high-dimensional prediction outcome spaces requires exponential computational and statistical complexity. The recent relaxation known as decision calibration ensures the optimality of the simple best-response rule while requiring only polynomial sample complexity in the dimension of outcomes. However, known results on calibration and decision calibration crucially rely on linear loss functions for establishing best-response optimality. A natural approach to handle nonlinear losses is to map outcomes $y$ into a feature space $\phi(y)$ of dimension $m$, then approximate losses with linear functions of $\phi(y)$. Unfortunately, even simple classes of nonlinear functions can demand exponentially large or infinite feature dimensions $m$. A key open problem is whether it is possible to achieve decision calibration with sample complexity independent of~$m$. We begin with a negative result: even verifying decision calibration under standard deterministic best response inherently requires sample complexity polynomial in~$m$. Motivated by this lower bound, we investigate a smooth version of decision calibration in which decision-makers follow a smooth best-response. This smooth relaxation enables dimension-free decision calibration algorithms. We introduce algorithms that, given $\mathrm{poly}(|A|,1/\epsilon)$ samples and any initial predictor~$p$, can efficiently post-process it to satisfy decision calibration without worsening accuracy. Our algorithms apply broadly to function classes that can be well-approximated by bounded-norm functions in (possibly infinite-dimensional) separable RKHS.
Theoretical Grid-Forming Extreme of Inverters
Authors: Qianxi Tang, Li Peng
Date: 2025-04-29 00:00:00

What are the theoretical and physical limits of a grid-forming inverter? This letter proposes that the extreme grid-forming ability of inverters is limited by their dc-side, ac-side, circuit topology dynamics, but not control. While many papers focus on how to improve grid-forming inverters stability, power sharing, inertia emulation, fault response, few, if any, formally define the fundamental theoretical limits or extremes of grid-forming behavior. It seems that the grid-forming can be improved endlessly. No physical system can support a grid indefinitely without limitations, especially under increasing levels of disturbance or uncertainty. Therefore, this boundary is explicitly shown by a mathematical expression in this letter. Consequently, the results show that relatively low dc-side voltage and high active power injection could damage the grid-forming ability. Poor consideration of dc-side, ac-side, and circuit topology dynamics in real practice will cause jeopardizing oscillation even by the theoretical best grid-forming control strategy.
Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners
Authors: Rebecca Knowlton, Layla Parast
Date: 2025-04-21 00:00:00

Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes extends beyond these settings and may be more pronounced in real-world public health and social science research, where randomized trials are often impractical. Research on identifying surrogates in real-world non-randomized data is scarce, as available statistical approaches for evaluating surrogate markers tend to rely on the assumption that treatment is randomized. While the few methods that allow for non-randomized treatment/exposure appropriately handle confounding individual characteristics, they do not offer a way to examine surrogate heterogeneity with respect to patient characteristics. In this paper, we propose a framework to assess surrogate heterogeneity in real-world, i.e., non-randomized, data and implement this framework using various meta-learners. Our approach allows us to quantify heterogeneity in surrogate strength with respect to patient characteristics while accommodating confounders through the use of flexible, off-the-shelf machine learning methods. In addition, we use our framework to identify individuals for whom the surrogate is a valid replacement of the primary outcome. We examine the performance of our methods via a simulation study and application to examine heterogeneity in the surrogacy of hemoglobin A1c as a surrogate for fasting plasma glucose.
A state reduction approach for learning-based model predictive control for train rescheduling
Authors: Caio Fabio Oliveira da Silva, Xiaoyu Liu, Azita Dabiri, Bart De Schutter
Date: 2025-04-28 00:00:00
Tasks: Model Predictive Control

This paper proposes a state reduction method for learning-based model predictive control (MPC) for train rescheduling in urban rail transit systems. The state reduction integrates into a control framework where the discrete decision variables are determined by a learning-based classifier and the continuous decision variables are computed by MPC. Herein, the state representation is designed separately for each component of the control framework. While a reduced state is employed for learning, a full state is used in MPC. Simulations on a large-scale train network highlight the effectiveness of the state reduction mechanism in improving the performance and reducing the memory usage.
Bayesian Federated Learning for Continual Training
Authors: Usevalad Milasheuski, Luca Barbieri, Sanaz Kianoush, Monica Nicoli, Stefano Savazzi
Date: 2025-04-21 00:00:00
Tasks: Continual Learning, Federated Learning, Uncertainty Quantification

Bayesian Federated Learning (BFL) enables uncertainty quantification and robust adaptation in distributed learning. In contrast to the frequentist approach, it estimates the posterior distribution of a global model, offering insights into model reliability. However, current BFL methods neglect continual learning challenges in dynamic environments where data distributions shift over time. We propose a continual BFL framework applied to human sensing with radar data collected over several days. Using Stochastic Gradient Langevin Dynamics (SGLD), our approach sequentially updates the model, leveraging past posteriors to construct the prior for the new tasks. We assess the accuracy, the expected calibration error (ECE) and the convergence speed of our approach against several baselines. Results highlight the effectiveness of continual Bayesian updates in preserving knowledge and adapting to evolving data.