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03/28/2023
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Preferential Bayesian optimization (PBO) is a framework for optimizing a...
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01/29/2023
Smooth Non-Stationary Bandits
In many applications of online decision making, the environment is non-s...
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10/27/2022
Regret Bounds and Experimental Design for Estimate-then-Optimize
In practical applications, data is used to make decisions in two steps: ...
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05/19/2022
Dynamic Pricing Provides Robust Equilibria in Stochastic Ridesharing Networks
Ridesharing markets are complex: drivers are strategic, rider demand and...
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03/29/2022
Near-optimality for infinite-horizon restless bandits with many arms
Restless bandits are an important class of problems with applications in...
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03/21/2022
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
We consider Bayesian optimization of expensive-to-evaluate experiments t...
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01/02/2022
Thinking inside the box: A tutorial on grey-box Bayesian optimization
Bayesian optimization (BO) is a framework for global optimization of exp...
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12/31/2021
Bayesian Optimization of Function Networks
We consider Bayesian optimization of the output of a network of function...
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11/15/2021
Correlation Improves Group Testing
Population-wide screening to identify and isolate infectious individuals...
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11/12/2021
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Bayesian optimization (BO) is a sample-efficient approach to optimizing ...
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07/25/2021
Restless Bandits with Many Arms: Beating the Central Limit Theorem
We consider finite-horizon restless bandits with multiple pulls per peri...
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11/14/2019
Bayesian Optimization with Uncertain Preferences over Attributes
We consider black-box global optimization of time-consuming-to-evaluate ...
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06/04/2019
Bayesian Optimization of Composite Functions
We consider optimization of composite objective functions, i.e., of the ...
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03/12/2019
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Bayesian optimization is popular for optimizing time-consuming black-box...
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07/08/2018
A Tutorial on Bayesian Optimization
Bayesian optimization is an approach to optimizing objective functions t...
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03/23/2018
Bayesian Optimization with Expensive Integrands
We propose a Bayesian optimization algorithm for objective functions tha...
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07/20/2017
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
This paper studies Bayesian ranking and selection (R&S) problems with co...
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03/13/2017
Bayesian Optimization with Gradients
Bayesian optimization has been successful at global optimization of expe...
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02/24/2017
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning
We analyze the problem of learning a single user's preferences in an act...
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08/11/2016
Warm Starting Bayesian Optimization
We develop a framework for warm-starting Bayesian optimization, that red...
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06/14/2016
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
In many applications of black-box optimization, one can evaluate multipl...
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03/01/2016
Multi-Information Source Optimization
We consider Bayesian optimization of an expensive-to-evaluate black-box ...
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02/16/2016
Parallel Bayesian Global Optimization of Expensive Functions
We consider parallel global optimization of derivative-free expensive-to...
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02/07/2016
Stratified Bayesian Optimization
We consider derivative-free black-box global optimization of expensive n...
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12/31/2015
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies
We consider effort allocation in crowdsourcing, where we wish to assign ...
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06/03/2015
Bayesian optimization for materials design
We introduce Bayesian optimization, a technique developed for optimizing...
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05/25/2015
Clustering via Content-Augmented Stochastic Blockmodels
Much of the data being created on the web contains interactions between ...
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07/16/2014
Probabilistic Group Testing under Sum Observations: A Parallelizable 2-Approximation for Entropy Loss
We consider the problem of group testing with sum observations and noise...
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07/10/2014
A New Optimal Stepsize For Approximate Dynamic Programming
Approximate dynamic programming (ADP) has proven itself in a wide range ...
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10/25/2011
Distance Dependent Infinite Latent Feature Models
Latent feature models are widely used to decompose data into a small num...
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10/06/2009