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Roman Garnett · José Miguel Hernández-Lobato · Eytan Bakshy · Syrine Belakaria · Stefanie Jegelka
Author Information
Roman Garnett (Washington University in St. Louis)
José Miguel Hernández-Lobato (University of Cambridge)
Eytan Bakshy (Meta)
Syrine Belakaria (Washington State University)
Stefanie Jegelka (MIT)
More from the Same Authors
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2020 : Scalable Combinatorial Bayesian Optimization with Tractable Statistical models »
Aryan Deshwal · Syrine Belakaria · Janardhan Rao Doppa -
2020 : Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations »
Syrine Belakaria · Aryan Deshwal · Janardhan Rao Doppa -
2021 : Practical Policy Optimization with PersonalizedExperimentation »
Mia Garrard · Hanson Wang · Ben Letham · Zehui Wang · Yin Huang · Yichun Hu · Chad Zhou · Norm Zhou · Eytan Bakshy -
2021 : A Fresh Look at De Novo Molecular Design Benchmarks »
Austin Tripp · Gregor Simm · José Miguel Hernández-Lobato -
2021 : Depth Uncertainty Networks for Active Learning »
Chelsea Murray · James Allingham · Javier Antorán · José Miguel Hernández-Lobato -
2022 : Preference-Aware Constrained Multi-Objective Bayesian Optimization »
Alaleh Ahmadian · Syrine Belakaria · Janardhan Rao Doppa -
2022 : Sparse Bayesian Optimization »
Sulin Liu · Qing Feng · David Eriksson · Ben Letham · Eytan Bakshy -
2022 : Preference-Aware Constrained Multi-Objective Bayesian Optimization »
Alaleh Ahmadian · Syrine Belakaria · Janardhan Rao Doppa -
2022 : Flow Annealed Importance Sampling Bootstrap »
Laurence Midgley · Vincent Stimper · Gregor Simm · Bernhard Schölkopf · José Miguel Hernández-Lobato -
2022 : Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction »
Wenlin Chen · Austin Tripp · José Miguel Hernández-Lobato -
2022 : Learning Generative Models with Invariance to Symmetries »
James Allingham · Javier Antorán · Shreyas Padhy · Eric Nalisnick · José Miguel Hernández-Lobato -
2022 : Preference-Aware Constrained Multi-Objective Bayesian Optimization For Analog Circuit Design »
Alaleh Ahmadian · Syrine Belakaria · Jana Doppa -
2022 : One-Shot Optimal Design for Gaussian Process Analysis of Randomized Experiments »
Jelena Markovic · Qing Feng · Eytan Bakshy -
2023 Poster: Limits, approximation and size transferability for GNNs on sparse graphs via graphops »
Thien Le · Stefanie Jegelka -
2023 Poster: Expressive Sign Equivariant Networks for Spectral Geometric Learning »
Derek Lim · Joshua Robinson · Stefanie Jegelka · Haggai Maron -
2023 Poster: Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent »
Jihao Andreas Lin · Javier Antorán · Shreyas Padhy · David Janz · José Miguel Hernández-Lobato · Alexander Terenin -
2023 Poster: Tanimoto Random Features for Scalable Molecular Machine Learning »
Austin Tripp · Sergio Bacallado · Sukriti Singh · José Miguel Hernández-Lobato -
2023 Poster: Faster Relative Entropy Coding with Greedy Rejection Coding »
Gergely Flamich · Efstratios Markou · José Miguel Hernández-Lobato -
2023 Poster: SE(3) Equivariant Augmented Coupling Flows »
Laurence Midgley · Vincent Stimper · Javier Antorán · Emile Mathieu · Bernhard Schölkopf · José Miguel Hernández-Lobato -
2023 Poster: The Behavior and Convergence of Local Bayesian Optimization »
Kaiwen Wu · Kyurae Kim · Roman Garnett · Jacob Gardner -
2023 Poster: The Exact Sample Complexity Gain from Invariances for Kernel Regression »
Behrooz Tahmasebi · Stefanie Jegelka -
2023 Poster: Compression with Bayesian Implicit Neural Representations »
Zongyu Guo · Gergely Flamich · Jiajun He · Zhibo Chen · José Miguel Hernández-Lobato -
2023 Poster: What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models »
Khashayar Gatmiry · Zhiyuan Li · Tengyu Ma · Sashank Reddi · Stefanie Jegelka · Ching-Yao Chuang -
2023 Poster: Unexpected Improvements to Expected Improvement for Bayesian Optimization »
Sebastian Ament · Samuel Daulton · David Eriksson · Maximilian Balandat · Eytan Bakshy -
2023 Oral: Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent »
Jihao Andreas Lin · Javier Antorán · Shreyas Padhy · David Janz · José Miguel Hernández-Lobato · Alexander Terenin -
2023 Workshop: Heavy Tails in ML: Structure, Stability, Dynamics »
Mert Gurbuzbalaban · Stefanie Jegelka · Michael Mahoney · Umut Simsekli -
2022 Workshop: New Frontiers in Graph Learning »
Jiaxuan You · Marinka Zitnik · Rex Ying · Yizhou Sun · Hanjun Dai · Stefanie Jegelka -
2022 Poster: Local Bayesian optimization via maximizing probability of descent »
Quan Nguyen · Kaiwen Wu · Jacob Gardner · Roman Garnett -
2022 Poster: Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks »
Ching-Yao Chuang · Stefanie Jegelka -
2022 Poster: Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions »
Nikolaos Karalias · Joshua Robinson · Andreas Loukas · Stefanie Jegelka -
2022 Poster: On the generalization of learning algorithms that do not converge »
Nisha Chandramoorthy · Andreas Loukas · Khashayar Gatmiry · Stefanie Jegelka -
2022 Poster: Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization »
Samuel Daulton · Xingchen Wan · David Eriksson · Maximilian Balandat · Michael A Osborne · Eytan Bakshy -
2022 Poster: Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo »
Ignacio Peis · Chao Ma · José Miguel Hernández-Lobato -
2021 Workshop: Deep Generative Models and Downstream Applications »
José Miguel Hernández-Lobato · Yingzhen Li · Yichuan Zhang · Cheng Zhang · Austin Tripp · Weiwei Pan · Oren Rippel -
2021 Poster: Functional Variational Inference based on Stochastic Process Generators »
Chao Ma · José Miguel Hernández-Lobato -
2021 Poster: Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs »
Raul Astudillo · Daniel Jiang · Maximilian Balandat · Eytan Bakshy · Peter Frazier -
2021 Poster: Improving black-box optimization in VAE latent space using decoder uncertainty »
Pascal Notin · José Miguel Hernández-Lobato · Yarin Gal -
2021 Poster: Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement »
Samuel Daulton · Maximilian Balandat · Eytan Bakshy -
2021 Poster: Bayesian Optimization with High-Dimensional Outputs »
Wesley Maddox · Maximilian Balandat · Andrew Wilson · Eytan Bakshy -
2020 Workshop: Machine Learning for Molecules »
José Miguel Hernández-Lobato · Matt Kusner · Brooks Paige · Marwin Segler · Jennifer Wei -
2020 : Jose Miguel Hernandez Lobato »
José Miguel Hernández-Lobato -
2020 Poster: Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding »
Gergely Flamich · Marton Havasi · José Miguel Hernández-Lobato -
2020 Poster: Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization »
Samuel Daulton · Maximilian Balandat · Eytan Bakshy -
2020 Poster: Adaptive Sampling for Stochastic Risk-Averse Learning »
Sebastian Curi · Kfir Y. Levy · Stefanie Jegelka · Andreas Krause -
2020 Poster: Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining »
Austin Tripp · Erik Daxberger · José Miguel Hernández-Lobato -
2020 Poster: Depth Uncertainty in Neural Networks »
Javier Antorán · James Allingham · José Miguel Hernández-Lobato -
2020 Poster: VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data »
Chao Ma · Sebastian Tschiatschek · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2020 Poster: BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization »
Maximilian Balandat · Brian Karrer · Daniel Jiang · Samuel Daulton · Ben Letham · Andrew Wilson · Eytan Bakshy -
2020 Poster: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Poster: Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization »
Ben Letham · Roberto Calandra · Akshara Rai · Eytan Bakshy -
2020 Spotlight: Barking up the right tree: an approach to search over molecule synthesis DAGs »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2020 Session: Orals & Spotlights Track 15: COVID/Applications/Composition »
José Miguel Hernández-Lobato · Oliver Stegle -
2020 Poster: Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees »
Shali Jiang · Daniel Jiang · Maximilian Balandat · Brian Karrer · Jacob Gardner · Roman Garnett -
2020 Poster: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization »
Qing Feng · Ben Letham · Hongzi Mao · Eytan Bakshy -
2020 Spotlight: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization »
Qing Feng · Ben Letham · Hongzi Mao · Eytan Bakshy -
2020 Poster: Debiased Contrastive Learning »
Ching-Yao Chuang · Joshua Robinson · Yen-Chen Lin · Antonio Torralba · Stefanie Jegelka -
2020 Spotlight: Debiased Contrastive Learning »
Ching-Yao Chuang · Joshua Robinson · Yen-Chen Lin · Antonio Torralba · Stefanie Jegelka -
2019 : Invited Speaker: Eytan Bakshy »
Eytan Bakshy -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2019 Workshop: Graph Representation Learning »
Will Hamilton · Rianne van den Berg · Michael Bronstein · Stefanie Jegelka · Thomas Kipf · Jure Leskovec · Renjie Liao · Yizhou Sun · Petar Veličković -
2019 Poster: Cost Effective Active Search »
Shali Jiang · Roman Garnett · Benjamin Moseley -
2019 Poster: Bayesian Batch Active Learning as Sparse Subset Approximation »
Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato -
2019 Poster: Max-value Entropy Search for Multi-Objective Bayesian Optimization »
Syrine Belakaria · Aryan Deshwal · Janardhan Rao Doppa -
2019 Poster: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model »
Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard Turner · José Miguel Hernández-Lobato · Cheng Zhang -
2019 Poster: A Model to Search for Synthesizable Molecules »
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato -
2019 Poster: D-VAE: A Variational Autoencoder for Directed Acyclic Graphs »
Muhan Zhang · Shali Jiang · Zhicheng Cui · Roman Garnett · Yixin Chen -
2019 Poster: Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning »
David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek -
2018 Workshop: Machine Learning for Molecules and Materials »
José Miguel Hernández-Lobato · Klaus-Robert Müller · Brooks Paige · Matt Kusner · Stefan Chmiela · Kristof Schütt -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Poster: Efficient nonmyopic batch active search »
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett -
2018 Spotlight: Efficient nonmyopic batch active search »
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett -
2018 Poster: Automating Bayesian optimization with Bayesian optimization »
Gustavo Malkomes · Roman Garnett -
2018 Poster: Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo »
Marton Havasi · José Miguel Hernández-Lobato · Juan J. Murillo-Fuentes -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Bayesian optimization for science and engineering »
Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez -
2017 : Closing remarks »
José Miguel Hernández-Lobato -
2017 Workshop: Machine Learning for Molecules and Materials »
Kristof Schütt · Klaus-Robert Müller · Anatole von Lilienfeld · José Miguel Hernández-Lobato · Klaus-Robert Müller · Alan Aspuru-Guzik · Bharath Ramsundar · Matt Kusner · Brooks Paige · Stefan Chmiela · Alexandre Tkatchenko · Anatole von Lilienfeld · Koji Tsuda -
2016 : Panel Discussion »
Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal -
2016 : Automatic Chemical Design using Variational Autoencoders »
José Miguel Hernández-Lobato -
2016 : Alpha divergence minimization for Bayesian deep learning »
José Miguel Hernández-Lobato -
2016 Poster: Bayesian optimization for automated model selection »
Gustavo Malkomes · Charles Schaff · Roman Garnett -
2015 : *Roman Garnett* Bayesian Quadrature: Lessons Learned and Looking Forwards »
Roman Garnett -
2015 Poster: Bayesian Active Model Selection with an Application to Automated Audiometry »
Jacob Gardner · Gustavo Malkomes · Roman Garnett · Kilian Weinberger · Dennis Barbour · John Cunningham -
2015 Poster: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2015 Spotlight: Stochastic Expectation Propagation »
Yingzhen Li · José Miguel Hernández-Lobato · Richard Turner -
2014 Poster: Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature »
Tom Gunter · Michael A Osborne · Roman Garnett · Philipp Hennig · Stephen J Roberts -
2014 Poster: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2014 Poster: Gaussian Process Volatility Model »
Yue Wu · José Miguel Hernández-Lobato · Zoubin Ghahramani -
2014 Spotlight: Predictive Entropy Search for Efficient Global Optimization of Black-box Functions »
José Miguel Hernández-Lobato · Matthew Hoffman · Zoubin Ghahramani -
2013 Poster: Learning Feature Selection Dependencies in Multi-task Learning »
Daniel Hernández-lobato · José Miguel Hernández-Lobato -
2013 Poster: Gaussian Process Conditional Copulas with Applications to Financial Time Series »
José Miguel Hernández-Lobato · James R Lloyd · Daniel Hernández-lobato -
2013 Poster: Σ-Optimality for Active Learning on Gaussian Random Fields »
Yifei Ma · Roman Garnett · Jeff Schneider -
2012 Poster: Collaborative Gaussian Processes for Preference Learning »
Neil Houlsby · José Miguel Hernández-Lobato · Ferenc Huszar · Zoubin Ghahramani -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2011 Poster: Robust Multi-Class Gaussian Process Classification »
Daniel Hernández-lobato · José Miguel Hernández-Lobato · Pierre Dupont -
2007 Poster: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach »
José Miguel Hernández-Lobato · Tjeerd M Dijkstra · Tom Heskes