[preprints]   [publications]   [theses]   [invited talks]

Denotes joint lead authors,   Denotes alphabetical order.

Preprints

Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents
, , ,
[arxiv]

Online Learning Demands in Max-min Fairness
, , , ,
[arxiv] [Simons talk: slides]

Publications

Learning to Price Homogeneous Data
, ,
Advances in Neural Information Processing Systems (NeurIPS) 2024   [arxiv]

Bandit Profit Maximization for Targeted Marketing
, ,
ACM Conference on Economics and Computation (EC) 2024   [arxiv]

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
,
International Conference on Machine Learning (ICML) 2024   [pdf]

Mechanism Design for Collaborative Normal Mean Estimation
, ,
Advances in Neural Information Processing Systems (NeurIPS) 2023   [arxiv] [Stanford slides]

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
, , ,
ACM Conference on Economics and Computation (EC) 2023   [arxiv] [EC talk: slides] [WSB talk: slides]
🏆 Exemplary Artificial Intelligence Track paper (link)

Cilantro: A Framework for Performance-Aware Resource Allocation for General Objectives via Online Feedback
, , , , , ,
Operating Systems Design and Implementation (OSDI) 2023   [pdf]

Active Cost-aware Labeling of Streaming Data
, ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023   [pdf]

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
, , ,
Journal of Machine Learning Research (JMLR) 2022, to appear   [pdf]

Online Learning of Competitive Equilibria in Exchange Economies
, , , ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022   [arxiv]

Elastic Hyperparameter Tuning on the Cloud
, , , , , ,
Symposium on Cloud Computing (SoCC) 2021   [pdf]

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
, , , , ,
International Conference on Machine Learning (ICML) 2021   [arxiv] [Google talk: slides, video]

RubberBand: Cloud-based Hyperparameter Tuning
, , , , , , ,
European Conference on Computer Systems (EuroSys) 2021   [pdf]

Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning
, , , Han Wang, Sven Burke, , , ,
Cell Reports - Physical Science 2020   [pdf] [code, data]

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
, , , , , , ,
Journal of Machine Learning Research (JMLR) 2020   [pdf] [code, data]

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
, , , , , ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020   [arxiv] [code]

Offline Contextual Bayesian Optimization
, , , , , , ,
Advances in Neural Information Processing Systems (NeurIPS) 2019   [pdf - coming soon]

Multi-fidelity Gaussian Process Bandit Optimisation
, , , ,
Journal of Artificial Intelligence Research (JAIR) 2019   [pdf] [code] [École slides]
Abridged version at:   Neural Information Processing Systems (NeurIPS) 2016   [pdf]

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
, , , , ,
International Conference on Machine Learning (ICML) 2019   [pdf] [code]

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
, ,
Conference on Uncertainty in Artificial Intelligence (UAI) 2019   [pdf] [code]

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
, ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2019   [pdf]

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
, , , ,
Advances in Neural Information Processing Systems (NeurIPS) 2018   [pdf] [code, data] [spotlight video] [Uber slides]

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
, ,
International Conference on Machine Learning (ICML) 2018   [pdf]

Parallelised Bayesian Optimisation via Thompson Sampling
, , ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2018   [pdf] [code] [AISTATS slides]

Multi-fidelity Bayesian Optimisation with Continuous Approximations
, , ,
International Conference on Machine Learning (ICML) 2017   [pdf] [slides] [talk: video]

Batch Policy Gradient Methods for Improving Neural Conversation Models
, , , ,
International Conference on Learning Representations (ICLR) 2017   [pdf]

Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
, ,
Artificial Intelligence Journal (AIJ) 2017   [aij] [arxiv] [code (from David Fleming)]
Abridged version at:   International Joint Conference on Artificial Intelligence (IJCAI) 2015   [pdf]
🏆 IJCAI 2015 Distinguished Paper Award (Top 2 out of 1996 submissions, link)

The Multi-fidelity Multi-armed Bandit
, , ,
Advances in Neural Information Processing Systems (NeurIPS) 2016   [pdf]

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
, ,
Advances in Neural Information Processing Systems (NeurIPS) 2016   [pdf] [code]

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
,
International Conference on Machine Learning (ICML) 2016   [pdf] [code] [talk: video]

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
, , ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2016   [pdf]

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
, , , ,
Advances in Neural Information Processing Systems (NeurIPS) 2015   [pdf]

High Dimensional Bayesian Optimisation and Bandits via Additive Models
, ,
International Conference on Machine Learning (ICML) 2015   [pdf] [code] [talk: video, slides]

On Estimating L₂² Divergence
, , ,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2015   [pdf]

Nonparametric Estimation of Renyi-Divergence and Friends
, , ,
International Conference on Machine Learning (ICML) 2014   [pdf]

Latent Beta Topographic Mapping

International Conference on Tools with Artificial Intelligence 2012   [pdf]

Unpublished

PAC Best Arm Identification Under a Deadline
, , , , ,
[arxiv] [Google talk: slides, video]

ProBO: A Framework for Using Probabilistic Programming in Bayesian Optimization
, , ,
[arxiv]

Theses

Tuning Hyperparameters without Grad Students: Scaling Up Bandit Optimisation

School of Computer Science, Carnegie Mellon University, October 2018   [pdf]

Invited Talks

Data without Borders: Game-theoretic Challenges in Democratizing Data
Midwest Machine Learnining Symposium, Minneapolis, MN   May 2024    [slides]
Longer version at Wisconsin Statistics Seminar,   April 2024    [slides]

Mechanism Design for Collabrative Normal Mean Estimation
Stanford University, Palo Alto, CA   April 2024    [slides]

Frontiers in Machine Learning and Game Theory
University of Moratuwa, Moratuwa, Sri Lanka,   January 2024    [slides]

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wisconsin School of Business Seminar, Madison, WI   February 2023    [slides]

Learning with (Bandit) Feedback from Strategic Stakeholders in Fair Division
Simons Institute, Berkeley, CA   May 2022    [slides]

Resource Allocation in Multi-armed Bandits
Google Research,   June 2021    [slides, video]

Bayesian Methods for Adaptive Experimentation
Symposium on Autonomous Experimentation, University of Maryland, College Park, MD   August 2019    [slides]

Scaling up Bandits and Friends
UC Berkeley, Berkeley, CA   April 2019    [slides]

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Uber AI Labs, San Francisco, CA   November 2018    [slides]

Design of Experiments via Probabilistic Modeling: Applications in Materials Science, Model Selection, and Astrophysics
Covestro, Pittsburgh, PA,   August 2018

Active Bayesian Design of Experiments via Posterior Sampling
Machine Learning in Science & Engineering Conference, Pittsburgh, PA,   June 2018    [slides]

Bayesian Design of Experiments via Posterior Sampling
Lawrence Berkeley National Lab, Berkeley, CA,   June 2018    [slides]

Scalable Bandit Methods for Hyper-parameter Tuning
Guest Lecture - Machine Learning for Biology, University of Pittsburgh, Pittsburgh, PA,   November 2017    [slides]

Bayesian Optimisation for Materials Science
Electrochemical Energy Symposium, Pittsburgh, PA,   November 2017    [slides]

Parallelised Bayesian Optimisation via Thompson Sampling
Google Research, Mountain View, CA,   September 2017    [slides]

Multi-fidelity Bayesian Optimisation
Facebook Inc., Menlo Park, CA,   September 2017    [slides]

Stochastic Bandits
University of Moratuwa, Moratuwa, Sri Lanka,   August 2017    [slides]

Bandit Optimisation with Approximations
École Polytechnique, Paris, France,   April 2017     [slides]

Multi-fidelity Bandit Optimisation
University College London, London, UK,   July 2016     [slides]