conftrace_

L4DC 2025

120 papers

Anytime Safe Reinforcement Learning

Pol Mestres, Arnau Marzabal, Jorge Cortes

Formation Shape Control using the Gromov-Wasserstein Metric

Haruto Nakashima, Siddhartha Ganguly, Kohei Morimoto, Kenji Kashima

Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles

Jose Leopoldo Contreras, Ola Shorinwa, Mac Schwager

Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems

Luke Bhan, Peijia Qin, Miroslav Krstic, Yuanyuan Shi

Controlling Participation in Federated Learning with Feedback

Michael Cummins, Guner Dilsad Er, Michael Muehlebach

Orthogonal projection-based regularization for efficient model augmentation

Bendeguz Mate Györök, Jan H. Hoekstra, Johan Kon, Tamas Peni, Maarten Schoukens

Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions

William D. Compton, Max H. Cohen, Aaron D. Ames

Scalable Natural Policy Gradient for General-Sum Linear Quadratic Games with Known Parameters

Mostafa Shibl, Wesley Suttle, Vijay Gupta

Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots

Feihan Li, Abulikemu Abuduweili, Yifan Sun, Rui Chen, Weiye Zhao

The Complexity of Sequential Prediction in Dynamical Systems

Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari

Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control

Hansung Kim, Edward L. Zhu, Chang Seok Lim, Francesco Borrelli

Interacting Particle Systems for Fast Linear Quadratic RL

Anant A. Joshi, Heng-Sheng Chang, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn

Learning Temporal Logic Predicates from Data with Statistical Guarantees

Emi Soroka, Rohan Sinha, Sanjay Lall

Asymptotics of Linear Regression with Linearly Dependent Data

Behrad Moniri, Hamed Hassani

DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle Dynamics Modeling in Autonomous Racing

Jingyun Ning, Madhur Behl

DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization

Anjian Li, Zihan Ding, Adji Bousso Dieng, Ryne Beeson

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning

Batuhan Yardim, Niao He

A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning

Ingvar Ziemann

Multi-agent Stochastic Bandits Robust to Adversarial Corruptions

Fatemeh Ghaffari, Xuchuang Wang, Jinhang Zuo, Mohammad Hajiesmaili

State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?

Ingvar Ziemann, Nikolai Matni, George Pappas