pwc-archive/papers-with-abstracts huggingface dataset:
An Effective Gram Matrix Characterizes Generalization in Deep Networks
Authors: Rubing Yang, Pratik Chaudhari
Date: 2025-04-23 00:00:00
Tasks: image-classification, Image Classification

We derive a differential equation that governs the evolution of the generalization gap when a deep network is trained by gradient descent. This differential equation is controlled by two quantities, a contraction factor that brings together trajectories corresponding to slightly different datasets, and a perturbation factor that accounts for them training on different datasets. We analyze this differential equation to compute an ``effective Gram matrix'' that characterizes the generalization gap after training in terms of the alignment between this Gram matrix and a certain initial ``residual''. Empirical evaluations on image classification datasets indicate that this analysis can predict the test loss accurately. Further, at any point during training, the residual predominantly lies in the subspace of the effective Gram matrix with the smallest eigenvalues. This indicates that the training process is benign, i.e., it does not lead to significant deterioration of the generalization gap (which is zero at initialization). The alignment between the effective Gram matrix and the residual is different for different datasets and architectures. The match/mismatch of the data and the architecture is primarily responsible for good/bad generalization.
Training Large Language Models to Reason via EM Policy Gradient
Authors: Tianbing Xu
Date: 2025-04-24 00:00:00
Tasks: GSM8K, Math, Problem Decomposition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, foundation models such as OpenAI's O1 and O3, along with DeepSeek's R1, have demonstrated strong reasoning capacities and problem-solving skills acquired through large-scale reinforcement learning (RL), with wide applications in mathematics, coding, science, intelligent agents, and virtual assistants. In this work, we introduce an off-policy reinforcement learning algorithm, EM Policy Gradient, aimed at enhancing LLM reasoning by optimizing expected return over reasoning trajectories. We frame the reasoning task as an Expectation-Maximization (EM) optimization problem, alternating between sampling diverse rationale trajectories and performing reward-guided fine-tuning. Unlike PPO and GRPO, which rely on complex importance weights and heuristic clipping, our method provides a simpler, more principled off-policy policy gradient approach, eliminating these complexities while maintaining strong performance. We evaluate the effectiveness of EM Policy Gradient on the GSM8K and MATH (HARD) datasets, where it achieves performance comparable to or slightly surpassing the state-of-the-art GRPO, while offering additional advantages in scalability, simplicity, and reasoning conciseness. Moreover, models fine-tuned with our method exhibit cognitive behaviors, such as sub-problem decomposition, self-verification, and backtracking, highlighting its potential to enhance both the interpretability and robustness of LLM reasoning.
Beyond Quadratic Costs in LQR: Bregman Divergence Control
Authors: Babak Hassibi, Joudi Hajar, Reza Ghane
Date: 2025-05-01 00:00:00
Tasks: Model Predictive Control

In the past couple of decades, the use of ``non-quadratic" convex cost functions has revolutionized signal processing, machine learning, and statistics, allowing one to customize solutions to have desired structures and properties. However, the situation is not the same in control where the use of quadratic costs still dominates, ostensibly because determining the ``value function", i.e., the optimal expected cost-to-go, which is critical to the construction of the optimal controller, becomes computationally intractable as soon as one considers general convex costs. As a result, practitioners often resort to heuristics and approximations, such as model predictive control that only looks a few steps into the future. In the quadratic case, the value function is easily determined by solving Riccati equations. In this work, we consider a special class of convex cost functions constructed from Bregman divergence and show how, with appropriate choices, they can be used to fully extend the framework developed for the quadratic case. The resulting optimal controllers are infinite horizon, come with stability guarantees, and have state-feedback, or estimated state-feedback, laws. They exhibit a much wider range of behavior than their quadratic counterparts since the feedback laws are nonlinear. The approach can be applied to several cases of interest, including safety control, sparse control, and bang-bang control.
Causal rule ensemble approach for multi-arm data
Authors: Ke Wan, Kensuke Tanioka, Toshio Shimokawa
Date: 2025-04-24 00:00:00
Tasks: Interpretable Machine Learning

Heterogeneous treatment effect (HTE) estimation is critical in medical research. It provides insights into how treatment effects vary among individuals, which can provide statistical evidence for precision medicine. While most existing methods focus on binary treatment situations, real-world applications often involve multiple interventions. However, current HTE estimation methods are primarily designed for binary comparisons and often rely on black-box models, which limit their applicability and interpretability in multi-arm settings. To address these challenges, we propose an interpretable machine learning framework for HTE estimation in multi-arm trials. Our method employs a rule-based ensemble approach consisting of rule generation, rule ensemble, and HTE estimation, ensuring both predictive accuracy and interpretability. Through extensive simulation studies and real data applications, the performance of our method was evaluated against state-of-the-art multi-arm HTE estimation approaches. The results indicate that our approach achieved lower bias and higher estimation accuracy compared with those of existing methods. Furthermore, the interpretability of our framework allows clearer insights into how covariates influence treatment effects, facilitating clinical decision making. By bridging the gap between accuracy and interpretability, our study contributes a valuable tool for multi-arm HTE estimation, supporting precision medicine.
Climate Science and Control Engineering: Insights, Parallels, and Connections
Authors: Salma M. Elsherif, Ahmad F. Taha
Date: 2025-04-29 00:00:00

Climate science is the multidisciplinary field that studies the Earth's climate and its evolution. At the very core of climate science are indispensable climate models that predict future climate scenarios, inform policy decisions, and dictate how a country's economy should change in light of the changing climate. Climate models capture a wide range of interacting dynamic processes via extremely complex ordinary and partial differential equations. To model these large-scale complex processes, climate science leverages supercomputers, advanced simulations, and statistical methods to predict future climate. An area of engineering that is rarely studied in climate science is control engineering. Given that climate systems are inherently dynamic, it is intuitive to analyze them within the framework of dynamic system science. This perspective has been underexplored in the literature. In this manuscript, we provide a tutorial that: (i) introduces the control engineering community to climate dynamics and modeling, including spatiotemporal scales and challenges in climate modeling; (ii) offers a fresh perspective on climate models from a control systems viewpoint; and (iii) explores the relevance and applicability of various advanced graph and network control-based approaches in building a physics-informed framework for learning, control and estimation in climate systems. We also present simple and then more complex climate models, depicting fundamental ideas and processes that are instrumental in building climate change projections. This tutorial also builds parallels and observes connections between various contemporary problems at the forefront of climate science and their control theoretic counterparts. We specifically observe that an abundance of climate science problems can be linguistically reworded and mathematically framed as control theoretic ones.
OUI Need to Talk About Weight Decay: A New Perspective on Overfitting Detection
Authors: Alberto Fernández-Hernández, Jose I. Mestre, Manuel F. Dolz, Jose Duato, Enrique S. Quintana-Ortí
Date: 2025-04-24 00:00:00

We introduce the Overfitting-Underfitting Indicator (OUI), a novel tool for monitoring the training dynamics of Deep Neural Networks (DNNs) and identifying optimal regularization hyperparameters. Specifically, we validate that OUI can effectively guide the selection of the Weight Decay (WD) hyperparameter by indicating whether a model is overfitting or underfitting during training without requiring validation data. Through experiments on DenseNet-BC-100 with CIFAR- 100, EfficientNet-B0 with TinyImageNet and ResNet-34 with ImageNet-1K, we show that maintaining OUI within a prescribed interval correlates strongly with improved generalization and validation scores. Notably, OUI converges significantly faster than traditional metrics such as loss or accuracy, enabling practitioners to identify optimal WD (hyperparameter) values within the early stages of training. By leveraging OUI as a reliable indicator, we can determine early in training whether the chosen WD value leads the model to underfit the training data, overfit, or strike a well-balanced trade-off that maximizes validation scores. This enables more precise WD tuning for optimal performance on the tested datasets and DNNs. All code for reproducing these experiments is available at https://github.com/AlbertoFdezHdez/OUI.
Pinching-Antenna Systems (PASS): Power Radiation Model and Optimal Beamforming Design
Authors: Xiaoxia Xu, Xidong Mu, Zhaolin Wang, Yuanwei Liu, Arumugam Nallanathan
Date: 2025-04-30 00:00:00

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning the spacing between pinching antennas and waveguides. A closed-form pinching antenna spacing arrangement strategy is derived to achieve the commonly assumed equal-power radiation. Based on this, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user's minimum rate requirement. (1) To solve the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios, which is guaranteed to converge to globally optimal solutions. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete
Optimal Dynamic Control of Bounded Jacobian Discrete-Time Systems via Interval Observers
Authors: Mohammad Khajenejad
Date: 2025-04-30 00:00:00

This paper presents an optimal dynamic control framework for bounded Jacobian nonlinear discrete-time (DT) systems with nonlinear observations affected by both state and process noise. Rather than directly stabilizing the uncertain system, we focus on stabilizing an interval observer in a higher dimensional space, whose states bound the true system states. Our nonlinear dynamic control method introduces added flexibility over traditional static and linear approaches, effectively compensating for system nonlinearities and enabling potentially tighter closed-loop intervals. Additionally, we establish a separation principle that allows for the design of observer and control gains. We further derive tractable matrix inequalities to ensure system stability in the closed-loop configuration. The simulation results show that the proposed dynamic control approach significantly outperforms a static counterpart method.
Probabilistic Time Series Forecasting of Residential Loads -- A Copula Approach
Authors: Marco Jeschke, Timm Faulwasser, Roland Fried
Date: 2025-04-30 00:00:00
Tasks: Density Estimation, Probabilistic Time Series Forecasting, Time Series, Time Series Forecasting

Predicting the time series of future evolutions of renewable injections and demands is of utmost importance for the operation of power systems. However, the current state of the art is mostly focused on mean-value time series predictions and only very few methods provide probabilistic forecasts. In this paper, we rely on kernel density estimation and vine copulas to construct probabilistic models for individual load profiles of private households. Our approach allows the quantification of variability of individual energy consumption in general and of daily peak loads in particular. We draw upon an Australian distribution grid dataset to illustrate our findings. We generate synthetic loads that follow the distribution of the real data.
A Weighted-likelihood framework for class imbalance in Bayesian prediction models
Authors: Stanley E. Lazic
Date: 2025-04-23 00:00:00

Class imbalance occurs when data used for training classification models has a different number of observations or samples within each category or class. Models built on such data can be biased towards the majority class and have poor predictive performance and generalisation for the minority class. We propose a Bayesian weighted-likelihood (power-likelihood) approach to deal with class imbalance: each observation's likelihood is raised to a weight inversely proportional to its class proportion, with weights normalized to sum to the number of samples. This embeds cost-sensitive learning directly into Bayesian updating and is applicable to binary, multinomial and ordered logistic prediction models. Example models are implemented in Stan, PyMC, and Turing.jl, and all code and reproducible scripts are archived on Github: https://github.com/stanlazic/weighted_likelihoods. This approach is simple to implement and extends naturally to arbitrary error-cost matrices.
(Im)possibility of Automated Hallucination Detection in Large Language Models
Authors: Amin Karbasi, Omar Montasser, John Sous, Grigoris Velegkas
Date: 2025-04-23 00:00:00
Tasks: Hallucination, Language Identification, Text Generation

Is automated hallucination detection possible? In this work, we introduce a theoretical framework to analyze the feasibility of automatically detecting hallucinations produced by large language models (LLMs). Inspired by the classical Gold-Angluin framework for language identification and its recent adaptation to language generation by Kleinberg and Mullainathan, we investigate whether an algorithm, trained on examples drawn from an unknown target language $K$ (selected from a countable collection) and given access to an LLM, can reliably determine whether the LLM's outputs are correct or constitute hallucinations. First, we establish an equivalence between hallucination detection and the classical task of language identification. We prove that any hallucination detection method can be converted into a language identification method, and conversely, algorithms solving language identification can be adapted for hallucination detection. Given the inherent difficulty of language identification, this implies that hallucination detection is fundamentally impossible for most language collections if the detector is trained using only correct examples from the target language. Second, we show that the use of expert-labeled feedback, i.e., training the detector with both positive examples (correct statements) and negative examples (explicitly labeled incorrect statements), dramatically changes this conclusion. Under this enriched training regime, automated hallucination detection becomes possible for all countable language collections. These results highlight the essential role of expert-labeled examples in training hallucination detectors and provide theoretical support for feedback-based methods, such as reinforcement learning with human feedback (RLHF), which have proven critical for reliable LLM deployment.
Measurement-Based Line-Impedance Estimation in the Absence of Phasor Measurement Units
Authors: Plouton Grammatikos, Ali Mohamed Ali, Fabrizio Sossan
Date: 2025-04-30 00:00:00

This paper proposes and compares experimentally several methods to estimate the series resistance and reactance (i.e., the transversal components of the $\pi$-model of a line) of low-voltage lines in distribution grids. It first shows that if phasor measurements are available and the grid nodal voltages and power injections are known, the problem can be formulated and solved as a conventional load flow with properly adjusted unknowns. To solve this problem, we propose an analytical derivation of the Jacobian matrix. If only RMS values are available, such as from smart meters, integrating information from multiple intervals becomes necessary, ultimately opening to least-squares estimations, widely adopted in the literature. In this context, applying the proposed Jacobian contributes to accelerating the problem resolution of existing algorithms. The methods are compared in terms of estimation performance and convergence by using measurements from an experimental distribution grid interfacing real-world components and with realistic size implemented at the Gridlab at HES-SO Valais.
Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
Authors: Manuel Quintero, William T. Stephenson, Advik Shreekumar, Tamara Broderick
Date: 2025-04-23 00:00:00
Tasks: Attribute, Econometrics

In science and social science, we often wish to explain why an outcome is different in two populations. For instance, if a jobs program benefits members of one city more than another, is that due to differences in program participants (particular covariates) or the local labor markets (outcomes given covariates)? The Kitagawa-Oaxaca-Blinder (KOB) decomposition is a standard tool in econometrics that explains the difference in the mean outcome across two populations. However, the KOB decomposition assumes a linear relationship between covariates and outcomes, while the true relationship may be meaningfully nonlinear. Modern machine learning boasts a variety of nonlinear functional decompositions for the relationship between outcomes and covariates in one population. It seems natural to extend the KOB decomposition using these functional decompositions. We observe that a successful extension should not attribute the differences to covariates -- or, respectively, to outcomes given covariates -- if those are the same in the two populations. Unfortunately, we demonstrate that, even in simple examples, two common decompositions -- functional ANOVA and Accumulated Local Effects -- can attribute differences to outcomes given covariates, even when they are identical in two populations. We provide a characterization of when functional ANOVA misattributes, as well as a general property that any discrete decomposition must satisfy to avoid misattribution. We show that if the decomposition is independent of its input distribution, it does not misattribute. We further conjecture that misattribution arises in any reasonable additive decomposition that depends on the distribution of the covariates.
Switching Transients in Constrained Transformer-Line/Cable Configurations
Authors: Y. Xiang, L. Wu, K. Velitsikakis, A. L. J. Janssen
Date: 2025-04-30 00:00:00

This paper investigates the transient phenomena that occur in two special cases in the Netherlands: (A) during the energization of a power transformer via a cable feeder and (B) the energization of a power transformer together with an overhead line (OHL). In Case A a 7 km long 150 kV cable and a 150/50 kV transformer are connected and energized at the same time. In Case B a 150/50 kV transformer and a short 50 kV OHL are connected and energized simultaneously. The reason behind this kind of situations is related to space restrictions and cost efficiency.
Adaptive Neural Control with Desired Approximation: An Integral Lyapunov Function Approach
Authors: Mingxuan Sun, Shengxiang Zou
Date: 2025-04-30 00:00:00

The inherent approximation ability of neural networks plays an essential role in adaptive neural control, where the prerequisite for existence of the compact set is crucial in the control designs. Instead of using practical system state, in this paper, the desired approximation approach is characterized to tackle such a problem, where the desired state signal is required only as the input to the network. An integral Lyapunov function-based adaptive controller is designed, in the sense of the error tracking, where the treatment of the state-dependent input gain is adopted. Theoretical results for the performance analysis of the integral and incremental adaptation algorithms are presented in details. In particular, the boundedness of the variables in the closed-loop is characterized, while the transient performance of the output error is analytically quantified. It is shown that the proposed control schemes assure that the tracking error converges to an adjustable set without any requirement on the knowledge of the region that the practical variables evolve, and remove the requirement for the setting of initial conditions including system states and weight estimates.
Stability of Open Multi-agent Systems over Dynamic Signed Graphs
Authors: Pelin Sekercioglu, Angela Fontan, Dimos V. Dimarogonas
Date: 2025-04-30 00:00:00

This paper addresses the bipartite consensus-control problem in open multi-agent systems containing both cooperative and antagonistic interactions. In these systems, new agents can join and new interactions can be formed over time. Moreover, the types of interactions, cooperative or antagonistic, may change. To model these structural changes, we represent the system as a switched system interconnected over a dynamic signed graph. Using the signed edge-based agreement protocol and constructing strict Lyapunov functions for signed edge-Laplacian matrices with multiple zero eigenvalues, we establish global asymptotic stability of the bipartite consensus control. Numerical simulations validate our theoretical results.