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20182022
most citedBayesian Optimization with Unknown Search Space

25 citations · 51 across the 17 of their papers we have counts for

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8 papers · 1 filter

stat.ML20211 cited

Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support

Hung Tran-The, Sunil Gupta, Thanh Nguyen-Tang +2

We address policy learning with logged data in contextual bandits. Current offline-policy learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring…

stat.ML20201 cited

High Dimensional Level Set Estimation with Bayesian Neural Network

Huong Ha, Sunil Gupta, Santu Rana +1

Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques…

stat.ML20205 cited

Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling

Cheng Li, Sunil Gupta, Santu Rana +3

Scientific experiments are usually expensive due to complex experimental preparation and processing. Experimental design is therefore involved with the task of finding the optimal…

stat.ML201925 cited

Bayesian Optimization with Unknown Search Space

Huong Ha, Santu Rana, Sunil Gupta +3

Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Ba…

stat.ML2019

Accelerating Experimental Design by Incorporating Experimenter Hunches

Cheng Li, Santu Rana, Sunil Gupta +8

Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when…

stat.ML2018

Hybrid Generative-Discriminative Models for Inverse Materials Design

Phuoc Nguyen, Truyen Tran, Sunil Gupta +2

Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an ext…