Hugging Face Trending Papers

Information-theoretic receding-horizon active learning of nonlinear dynamical systems

arXiv Machine Learning

4d ago

Information-theoretic receding-horizon active learning of nonlinear dynamical systems

arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...

By Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow

arXiv Machine Learning

Sep 15

Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs

arXiv:2502.00550v2 Announce Type: replace Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design...

By Bongseok Kim, Haoyang Zheng, Michael Penwarden, Guang Lin

arXiv Machine Learning

Sep 22

PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems

arXiv:2609.24117v1 Announce Type: new Abstract: Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system id...

By Chenfeng Huang, George Michailidis

arXiv Machine Learning

Sep 23

BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference

arXiv:2609.26021v1 Announce Type: new Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...

By Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho

benchmarks

arXiv Machine Learning

Jul 27

On the Identifiability of Controlled World Models

arXiv:2607. 22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control.

By Xiangteng Zhang, Yang Guan, Bo Zhang, Ya-Qin Zhang, Shengbo Eben Li

rag

arXiv Machine Learning

Aug 14

On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective

arXiv:2608. 13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency.

By Nestor R. Barraza, Gabriel Pena

llms agents

arXiv Machine Learning

Jul 30

Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret

arXiv:2607. 26370v1 Announce Type: cross Abstract: We propose a self-adaptive online learning for control method for tracking unknown target dynamics.

By Atharva Navsalkar, Hongyu Zhou, Vasileios Tzoumas

robotics safety

arXiv AI

Jun 12

WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

arXiv:2604. 08958v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task.

By Mintae Kim, Koushil Sreenath

reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 26

Learning Probabilistic Filters with Strictly Proper Scoring Rules

arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.

By Eviatar Bach, Ricardo Baptista, Jochen Br\"ocker, Bohan Chen, Andrew Stuart

llms multimodal safety

arXiv Machine Learning

Jun 16

DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising

arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.

By Paolo Giaretta, Zeyang Li, Navid Azizan

diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 25

Efficient Adaptive Data Acquisition via Pretrained Belief Representations

arXiv:2606. 25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.

By Daolang Huang, Zhuoyue Huang, Conor Hassan, Luigi Acerbi, Samuel Kaski, Tom Rainforth

benchmarks

arXiv Machine Learning

Aug 4

Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

arXiv:2608. 01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints.

By Francesco Cordiano, Kanghui He, Bart De Schutter

reinforcement-learning

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