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06/10/2023
Mechanism Design for Collaborative Normal Mean Estimation
We study collaborative normal mean estimation, where m strategic agents ...
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04/13/2023
Active Cost-aware Labeling of Streaming Data
We study actively labeling streaming data, where an active learner is fa...
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02/20/2023
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
In online marketplaces, customers have access to hundreds of reviews for...
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06/11/2021
Online Learning of Competitive Equilibria in Exchange Economies
The sharing of scarce resources among multiple rational agents is one of...
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06/06/2021
PAC Best Arm Identification Under a Deadline
We study (ϵ, δ)-PAC best arm identification, where a decision-maker must...
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12/15/2020
Online Learning Demands in Max-min Fairness
We describe mechanisms for the allocation of a scarce resource among mul...
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10/31/2020
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism
We study exploration in stochastic multi-armed bandits when we have acce...
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04/19/2020
Mechanism Design with Bandit Feedback
We study a multi-round welfare-maximising mechanism design problem, wher...
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08/05/2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
We describe ChemBO, a Bayesian Optimization framework for generating and...
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03/15/2019
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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01/31/2019
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensive-to-query function is a common task in science an...
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10/24/2018
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
We study the problem of black-box optimization of a noisy function in th...
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05/30/2018
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
Many real world applications can be framed as multi-objective optimizati...
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05/25/2018
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of...
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02/11/2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optim...
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05/25/2017
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
We design and analyse variations of the classical Thompson sampling (TS)...
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03/18/2017
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Bandit methods for black-box optimisation, such as Bayesian optimisation...
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02/10/2017
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural networ...
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02/03/2017
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
A common problem in disciplines of applied Statistics research such as A...
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09/21/2016
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Recently, there has been a surge of interest in using spectral methods f...
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03/20/2016
Multi-fidelity Gaussian Process Bandit Optimisation
In many scientific and engineering applications, we are tasked with the ...
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01/31/2016
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
High dimensional nonparametric regression is an inherently difficult pro...
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03/05/2015
High Dimensional Bayesian Optimisation and Bandits via Additive Models
Bayesian Optimisation (BO) is a technique used in optimising a D-dimensi...
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11/17/2014
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
We propose and analyze estimators for statistical functionals of one or ...
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10/30/2014
On Estimating L_2^2 Divergence
We give a comprehensive theoretical characterization of a nonparametric ...
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02/12/2014
