Author

Ho-Ang Ta

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Preprint Aug 2026

Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments

We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration process is dominated by stochastic variance. VarDE is built on a fundamental principle: sampling effort should be allocated to minimize the uncertainty of the final decisio...

Khang A. Luong, N. Nguyen, Ho-Ang Ta et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Nearly Optimal Fixed-Confidence Best-Arm Identification with 1-Bit Feedback

We study fixed-confidence best-arm identification under strict 1-bit feedback constraints. At each round, the learner selects an arm and a query set, and receives only a single bit indicating whether the sampled reward belongs to that set. We consider a distribution-free finite-variance setting with arm-wise localizati...

Khang A. Luong, Sơn Thái Đinh, Ho-Ang Ta et al. · 0 citations

#machine learning Preprint Sep 2026

Graph-Based Stochastic Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation

Tree-based Monte-Carlo Tree Search (MCTS) duplicates the same state when it is reached through different trajectories, which can waste simulations in stochastic MDPs. We introduce Graph-Based Stochastic-Power-UCT (GS-Power-UCT), which shares states reached at the same planning depth while keeping separate values for st...

T. Tran, Viet Bao Mai, Ho-Ang Ta et al. · 0 citations