The following is a list of suggested papers top cover in the second half of the course. I am also open to other paper recommendations!
Title | Topics | Authors | Venue | Link |
---|---|---|---|---|
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning | latent space BO | Antoine Grosnit, Rasul Tutunov, Alexandre Max Maraval, Ryan-Rhys Griffiths, Alexander I. Cowen-Rivers, Lin Yang, Lin Zhu, Wenlong Lyu, Zhitang Chen, Jun Wang, Jan Peters, Haitham Bou-Ammar | ArXiV, 2021 | |
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces | latent space BO | Aryan Deshwal, Janardhan Rao Doppa | NeurIPS, 2021 | |
Local Latent Space Bayesian Optimization over Structured Inputs | latent space BO, local BO | Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner | NeurIPS, 2023 | |
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders | latent space BO | Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson | ICML, 2023 | |
Scalable Global Optimization via Local Bayesian | local BO | David Eriksson, Michael Pearce, Jacob R Gardner, Ryan Turner, Matthias Poloczek | NeurIPS, 2019 | |
Discovering Many Diverse Solutions with Bayesian Optimization | latent space BO, local BO, misspecified BO | Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner | AISTATS, 2023 | |
The Behavior and Convergence of Local Bayesian Optimization | local BO | Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner | NeurIPS, 2023 | |
Deep Gaussian Processes for Multi-fidelity Modeling | greybox BO | Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González | ArXiV, 2019 | |
Bayesian Optimization of Function Networks | greybox BO | Raul Astudillo, Peter I. Frazier | NeurIPS, 2021 | |
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement | multi-objective BO | Samuel Daulton, Maximilian Balandat, Eytan Bakshy | NeurIPS, 2021 | |
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces | multi-objective BO | Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy | UAI, 2022 | |
Multi-Attribute Bayesian Optimization With Interactive Preference Learning | preference BO | Raul Astudillo, Peter I. Frazier | AISTATS, 2020 | |
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes | preference BO | Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy | AISTATS, 2022 | |
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization | preference BO | Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, Peter I. Frazier | AISTATS, 2023 | |
Preference-Aware Constrained Multi-Objective Bayesian Optimization | multi-objective BO, preference BO | Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa | NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022 | |
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes | preference BO | Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy | AISTATS, 2022 | |
Multi-Attribute Bayesian Optimization With Interactive Preference Learning | preference BO | Raul Astudillo, Peter I. Frazier | AISTATS, 2020 | |
Causal Bayesian Optimization | causal BO | Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González | AISTATS, 2020 | |
Dynamic Causal Bayesian Optimization | causal BO | Virginia Aglietti, Neil Dhir, Javier González, Theodoros Damoulas | NeurIPS, 2021 | |
Model-based Causal Bayesian Optimization | causal BO | Scott Sussex, Anastasiia Makarova, Andreas Krause | ICLR, 2023 | |
Functional Causal Bayesian Optimization | causal BO | Limor Gultchin, Virginia Aglietti, Alexis Bellot, Silvia Chiappa | UAI, 2023 | |
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees | non-myopic decision making | Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett | NeurIPS, 2020 | |
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs | cost aware BO, non-myopic decision making | Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier | NeurIPS, 2021 | |
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization | cost aware BO, non-myopic decision making | Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger | UAI, 2021 | |
Budgeted Bandit Problems with Continuous Random Costs | cost aware BO | Yingce Xia, Wenkui Ding, Xu-Dong Zhang, Nenghai Yu, Tao Qin | ACML, 2016 | |
Budgeted Multi–Armed Bandit in Continuous Action Space | cost aware BO | Francesco Trovo, Stefano Paladino, Marcello Restelli, Nicola Gatti | ECAI, 2016 | |
Efficient Nonmyopic Active Search | generalizations of BO, non-myopic decision making | Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett | ICML, 2017 | |
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information | generalizations of BO | Willie Neiswanger, Ke Alexander Wang, Stefano Ermon | ICML, 2021 | |
Bayesian Optimization with Conformal Coverage Guarantees | generalizations of BO | Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson | AISTATS, 2023 | |
Bayesian Optimization with Inequality Constraints | constrained BO | Jacob R. Gardner, Matt J. Kusner, Zhixiang Xu, Kilian Q. Weinberger, John P. Cunningham | ICML, 2014 | |
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization | mixed-space BO | Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy | NeurIPS, 2022 |