arXiv Machine Learning

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

arXiv Machine Learning

Sep 10

Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning

arXiv:2603.02935v2 Announce Type: replace Abstract: Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task repres...

By Mohammadreza Nakheai, Aidan Scannell, Kevin Luck, Joni Pajarinen

diffusion reinforcement-learning benchmarks

arXiv AI

Jul 22

Variational meta-learning inference for low dimensional neural system identification

arXiv:2607. 18965v1 Announce Type: cross Abstract: Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack reliable uncertainty quantification.

By Matteo Rufolo, Dario Piga, Marco Forgione

benchmarks

arXiv Machine Learning

Jul 7

MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions

arXiv:2510. 22298v2 Announce Type: replace-cross Abstract: Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions.

By Hans Jarett Ong, Yoichi Chikahara, Tomoharu Iwata

benchmarks

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

Hugging Face Trending Papers

5d ago

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

Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challenge for stochastic controlled nonlinear dynamical...

arXiv AI

Jul 22

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.

By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

rag reinforcement-learning benchmarks

arXiv AI

Jul 8

To Retain or to Adapt? Generalizing Continual Learning

arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.

By Giulia Lanzillotta, Mandana Samiei, Doina Precup, Razvan Pascanu, Claire Vernade

computer-vision reinforcement-learning benchmarks safety

arXiv AI

Jun 15

Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.

By Octavio Pappalardo

agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

arXiv:2605. 27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules.

By Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu

arXiv AI

Jun 29

The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference

arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.

By Pablo Montero-Manso, Marcel Scharth

benchmarks safety

arXiv Statistics ML

3d ago

Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models

arXiv:2509.18349v4 Announce Type: replace Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of l...

By Saptati Datta, Nicolas W. Hengartner, Yulia Pimonova, Natalie E. Klein, Nicholas Lubbers

arXiv Machine Learning

Jun 30

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks

arXiv:2503. 09679v2 Announce Type: replace Abstract: Meta-learning represents a strong class of approaches for solving few-shot learning tasks.

By Wei Cui, Tongzi Wu, Jesse C. Cresswell, Yi Sui, Keyvan Golestan

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