1 citations · 1 across the 18 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
stat.ML2026
Is Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?
Yiran Zhang, Mo Zhou, Weihang Xu +2
Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in machine learning. It is known…
cs.LG2026
Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
Runlong Zhou, Zihan Zhang, Maryam Fazel +1
We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with states, actions, horizon , and per-trajectory total…
cs.CL2026
Cold-Start Personalization via Training-Free Priors from Structured World Models
Avinandan Bose, Shuyue Stella Li, Faeze Brahman +6
Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each…