57 citations · 479 across the 53 of their papers we have counts for
16 papers · 1 filter
An Information-Theoretic Framework for Unifying Active Learning Problems
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant.…
Top- Ranking Bayesian Optimization
Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low +1
This paper presents a novel approach to top- ranking Bayesian optimization (top- ranking BO) which is a practical and significant generalization of preferential BO to handle…
Variational Bayesian Unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We frame this problem as one of minimizing the Ku…
Federated Bayesian Optimization via Thompson Sampling
Zhongxiang Dai, Kian Hsiang Low, Patrick Jaillet
Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge devices such as mobile phon…
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low +2
This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested age…
Inverse Reinforcement Learning with Missing Data
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low +1
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in…