Hugging Face Trending Papers July 30, 2026

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

Read the original on Hugging Face Trending Papers →

We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions. Building on the doubly lifted, measure-valued formulation of Transformer dynamics, we view data sets as probability laws on pairs of empirical input-output measures, allowing us to interpret the training problem as a finite-horizon Markovian control problem.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jul 31

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

arXiv:2607. 27975v1 Announce Type: new Abstract: We derive finite-sample generalization bounds for Transformers trained with dynamic programming recursions.

By Ka\u{g}an Akman, Naci Saldi, Serdar Y\"uksel

llms efficiency

arXiv Machine Learning

Jul 17

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces

arXiv:2412. 20556v2 Announce Type: replace-cross Abstract: We study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem.

By Linglingzhi Zhu, Yunqin Zhu, Yao Xie

arXiv Machine Learning

Jun 18

Generative models for decision-making under distributional shift

arXiv:2604. 04342v2 Announce Type: replace Abstract: Many data-driven decision problems are formulated using a nominal distribution estimated from historical data, while performance is ultimately determined by a deployment distribution that may be shifted, context-dependent, partially observed, or stress-induced.

By Xiuyuan Cheng, Yunqin Zhu, Yao Xie

arXiv AI

Jun 9

Reachability and asymptotics of Gaussian Transformer dynamics

arXiv:2606. 07600v1 Announce Type: cross Abstract: We formulate data propagation through the Transformer, the machine learning architecture powering large language models, as a nonlinear control system on the space of probability measures.

By Albert Alcalde, Zhengping Ji, Enrique Zuazua

llms

arXiv AI

Jun 11

Unifying Learning Dynamics and Generalization in Transformers Scaling Law

arXiv:2512. 22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computational resources.

By Chiwun Yang

llms

arXiv AI

Jul 14

Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning

arXiv:2506. 07040v4 Announce Type: replace-cross Abstract: We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs).

By Yang Xu, Swetha Ganesh, Vaneet Aggarwal

reinforcement-learning

Related stories

Generalization Bounds on Optimal Control for Transformer Training and Wasserstein Distributional Robustness

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces

Generative models for decision-making under distributional shift

Reachability and asymptotics of Gaussian Transformer dynamics

Unifying Learning Dynamics and Generalization in Transformers Scaling Law

Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning