January 2026

arXiv:2601.12931v1 Announce Type: cross Abstract: Online continual learning (OCL) methods adapt to changing environments without forgetting past knowledge. Similarly, online time series forecasting (OTSF) is a real-world problem where data evolve in time and success depends on both rapid adaptation and long-term memory. Indeed, time-varying and regime-switching forecasting models have been extensively studied, offering a strong justification for the use of OCL in these settings. Building on recent work that applies OCL to OTSF, this paper aims to […]

Ver mais

arXiv:2601.12707v1 Announce Type: cross Abstract: Estimating the unknown reward functions driving agents’ behaviors is of central interest in inverse reinforcement learning and game theory. To tackle this problem, we develop a unified framework for reward function recovery in two-player zero-sum matrix games and Markov games with entropy regularization, where we aim to reconstruct the underlying reward functions given observed players’ strategies and actions. This task is challenging due to the inherent ambiguity of inverse problems, the non-uniqueness of […]

Ver mais

arXiv:2601.12633v1 Announce Type: cross Abstract: Entropic optimal transport problems play an increasingly important role in machine learning and generative modelling. In contrast with optimal transport maps which often have limited applicability in high dimensions, Schrodinger bridges can be solved using the celebrated Sinkhorn’s algorithm, a.k.a. the iterative proportional fitting procedure. The stability properties of Sinkhorn bridges when the number of iterations tends to infinity is a very active research area in applied probability and machine learning. Traditional proofs […]

Ver mais

arXiv:2601.12612v1 Announce Type: cross Abstract: Computing $logdet(A)$ for large symmetric positive definite matrices arises in Gaussian process inference and Bayesian model comparison. Standard methods combine matrix-vector products with polynomial approximations. We study a different model: access to trace powers $p_k = tr(A^k)$, natural when matrix powers are available. Classical moment-based approximations Taylor-expand $log(lambda)$ around the arithmetic mean. This requires $|lambda – AM| < AM$ and diverges when $kappa > 4$. We work instead with the moment-generating function $M(t) […]

Ver mais

arXiv:2601.12518v1 Announce Type: cross Abstract: Cooperative MARL often assumes frequent access to global information in a data buffer, such as team rewards or other agents’ actions, which is typically unrealistic in decentralized MARL systems due to high communication costs. When communication is limited, agents must rely on outdated information to estimate gradients and update their policies. A common approach to handle missing data is called importance sampling, in which we reweigh old data from a base policy to […]

Ver mais

arXiv:2601.12380v1 Announce Type: cross Abstract: Real-world tabular databases routinely combine continuous measurements and categorical records, yet missing entries are pervasive and can distort downstream analysis. We propose Statistical-Neural Interaction (SNI), an interpretable mixed-type imputation framework that couples correlation-derived statistical priors with neural feature attention through a Controllable-Prior Feature Attention (CPFA) module. CPFA learns head-wise prior-strength coefficients ${lambda_h}$ that softly regularize attention toward the prior while allowing data-driven deviations when nonlinear patterns appear to be present in the data. […]

Ver mais

arXiv:2601.12343v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to predict human behavior. We propose a measure for evaluating how much knowledge a pretrained LLM brings to such a prediction: its equivalent sample size, defined as the amount of task-specific data needed to match the predictive accuracy of the LLM. We estimate this measure by comparing the prediction error of a fixed LLM in a given domain to that of flexible machine learning models trained […]

Ver mais

arXiv:2601.12213v1 Announce Type: cross Abstract: Matrix completion is a classical problem that has received recurring interest across a wide range of fields. In this paper, we revisit this problem in an ultra-sparse sampling regime, where each entry of an unknown, $ntimes d$ matrix $M$ (with $n ge d$) is observed independently with probability $p = C / d$, for a fixed integer $C ge 2$. This setting is motivated by applications involving large, sparse panel datasets, where the […]

Ver mais

arXiv:2601.12178v1 Announce Type: cross Abstract: We propose a federated learning framework for the calibration of parametric insurance indices under heterogeneous renewable energy production losses. Producers locally model their losses using Tweedie generalized linear models and private data, while a common index is learned through federated optimization without sharing raw observations. The approach accommodates heterogeneity in variance and link functions and directly minimizes a global deviance objective in a distributed setting. We implement and compare FedAvg, FedProx and FedOpt, […]

Ver mais

arXiv:2601.12120v1 Announce Type: cross Abstract: Instrumental variable based estimation of a causal effect has emerged as a standard approach to mitigate confounding bias in the social sciences and epidemiology, where conducting randomized experiments can be too costly or impossible. However, justifying the validity of the instrument often poses a significant challenge. In this work, we highlight a problem generally neglected in arguments for instrumental variable validity: the presence of an ”aggregate treatment variable”, where the treatment (e.g., education, […]

Ver mais