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



Preprints


ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
, , , , , ,
[arxiv] [code]

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
, , , , , , ,
[arxiv] [code, data]

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



Publications


Multi-fidelity Gaussian Process Bandit Optimisation
, , , ,
Journal of Artificial Intelligence Research (JAIR) 2019   [arxiv] [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   [arxiv] [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 Best 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 - coming soon] [longer version on arxiv]

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]


  Denotes joint lead authors.



Theses


Tuning Hyperparameters without Grad Students: Scaling Up Bandit Optimisation

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




Invited Talks


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]