5 citations · 37 across the 41 of their papers we have counts for
9 papers · 1 filter
Contextual Bandits and Optimistically Universal Learning
Moise Blanchard, Steve Hanneke, Patrick Jaillet
We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This general…
Sample-Then-Optimize Batch Neural Thompson Sampling
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low +1
Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its objective function, is popular for black-box optimization. However, due to the limitation…
Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice
Yuan Deng, Negin Golrezaei, Patrick Jaillet +2
Online advertising channels have commonly focused on maximizing total advertiser value (or welfare) to enhance long-run retention and channel healthiness. Previous literature has s…
Weighted Maximum Entropy Inverse Reinforcement Learning
The Viet Bui, Tien Mai, Patrick Jaillet
We study inverse reinforcement learning (IRL) and imitation learning (IM), the problems of recovering a reward or policy function from expert's demonstrated trajectories. We propos…
On Provably Robust Meta-Bayesian Optimization
Zhongxiang Dai, Yizhou Chen, Haibin Yu +2
Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous ev…
Optimal Information Provision for Strategic Hybrid Workers
Sohil Shah, Saurabh Amin, Patrick Jaillet
We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated pu…
Federated Neural Bandits
Zhongxiang Dai, Yao Shu, Arun Verma +3
Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward…
Universal Regression with Adversarial Responses
Moïse Blanchard, Patrick Jaillet
We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assu…
Rectified Max-Value Entropy Search for Bayesian Optimization
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions…