Tag: math.OC

#math.OC

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Academic · 1 min

An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis

arXiv:2604.01308v1 Announce Type: new Abstract: Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to …

Oluwamayowa O. Amusat, Luka Grbcic, Remi Patureau, M. Jibran S. Zuberi, Dan Gunter, Michael Wetter

8 views Apr 3

Academic · 1 min

Residuals-based Offline Reinforcement Learning

arXiv:2604.01378v1 Announce Type: new Abstract: Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real …

Qing Zhu, Xian Yu

4 views Apr 3

Academic · 1 min

Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method

arXiv:2604.01279v1 Announce Type: new Abstract: We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss …

Samuel Bright-Thonney, Thomas R. Harvey, Andre Lukas, Jesse Thaler

4 views Apr 3

Academic · 1 min

Boost Like a (Var)Pro: Trust-Region Gradient Boosting via Variable Projection

arXiv:2603.23658v1 Announce Type: new Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach …

Abhijit Chowdhary, Elizabeth Newman, Deepanshu Verma

14 views Mar 26

Academic · 1 min

Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models

arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable …

Kuepon Aueawatthanaphisut, Kuepon Aueawatthanaphisut

15 views Mar 26

Academic · 1 min

Resolving gradient pathology in physics-informed epidemiological models

arXiv:2603.23799v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental …

Nickson Golooba, Woldegebriel Assefa Woldegerima

12 views Mar 26

Academic · 1 min

Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs

arXiv:2603.23926v1 Announce Type: new Abstract: Online reinforcement learning in infinite-horizon Markov decision processes (MDPs) remains less theoretically and algorithmically developed than its episodic counterpart, with …

Guy Zamir, Matthew Zurek, Yudong Chen

17 views Mar 26

Academic · 1 min

Delightful Distributed Policy Gradient

arXiv:2603.20521v1 Announce Type: new Abstract: Distributed reinforcement learning trains on data from stale, buggy, or mismatched actors, producing actions with high surprisal (negative log-probability) under …

Ian Osband

12 views Mar 24

Academic · 1 min

Centrality-Based Pruning for Efficient Echo State Networks

arXiv:2603.20684v1 Announce Type: new Abstract: Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the …

Sudip Laudari

13 views Mar 24

Academic · 1 min

Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Control

arXiv:2603.19463v1 Announce Type: new Abstract: We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for …

Samuel N. Cohen, Filippo de Feo, Jackson Hebner, Justin Sirignano

15 views Mar 23

Academic · 1 min

Global Convergence of Multiplicative Updates for the Matrix Mechanism: A Collaborative Proof with Gemini 3

arXiv:2603.19465v1 Announce Type: new Abstract: We analyze a fixed-point iteration $v \leftarrow \phi(v)$ arising in the optimization of a regularized nuclear norm objective involving the …

Keith Rush

14 views Mar 23

Academic · 1 min

On Performance Guarantees for Federated Learning with Personalized Constraints

arXiv:2603.19617v1 Announce Type: new Abstract: Federated learning (FL) has emerged as a communication-efficient algorithmic framework for distributed learning across multiple agents. While standard FL formulations …

Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian

16 views Mar 23

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#math.OC

An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis

Residuals-based Offline Reinforcement Learning

Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method

Boost Like a (Var)Pro: Trust-Region Gradient Boosting via Variable Projection

Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models

Resolving gradient pathology in physics-informed epidemiological models

Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs

Delightful Distributed Policy Gradient

Centrality-Based Pruning for Efficient Echo State Networks

Deep Hilbert--Galerkin Methods for Infinite-Dimensional PDEs and Optimal Control

Global Convergence of Multiplicative Updates for the Matrix Mechanism: A Collaborative Proof with Gemini 3

On Performance Guarantees for Federated Learning with Personalized Constraints

JCG, PC

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