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2020 – today
- 2024
[c33]Poompol Buathong, Jiayue Wan, Raul Astudillo, Samuel Daulton, Maximilian Balandat, Peter I. Frazier:
Bayesian Optimization of Function Networks with Partial Evaluations. ICML 2024- [c32]Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy:
Joint Composite Latent Space Bayesian Optimization. ICML 2024 - [c31]Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin T. Carlberg, Neil Walton, Kody J. H. Law:
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. ICML 2024 - [i24]Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law:
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. CoRR abs/2402.02111 (2024) - 2023
- [j2]Pamphile T. Roy
, Art B. Owen, Maximilian Balandat, Matt Haberland:
Quasi-Monte Carlo Methods in Python. J. Open Source Softw. 8(87): 5309 (2023) - [c30]Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson:
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. AISTATS 2023: 7021-7039 - [c29]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. ICML 2023: 7167-7204 - [c28]Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Unexpected Improvements to Expected Improvement for Bayesian Optimization. NeurIPS 2023 - [c27]Yuhao Li, Abhishek Gupta, Alex Yang, Peinan Chen, Joey Pinto, Brian Karrer, Mayank Pundir, Maximilian Balandat, Arun Kejariwal, Benjamin C. Lee:
HHVM Performance Optimization for Large Scale Web Services. ICPE 2023: 137-148 - [i23]Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson:
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. CoRR abs/2303.01774 (2023) - [i22]Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Unexpected Improvements to Expected Improvement for Bayesian Optimization. CoRR abs/2310.20708 (2023) - [i21]Poompol Buathong, Jiayue Wan, Samuel Daulton, Raul Astudillo, Maximilian Balandat, Peter I. Frazier:
Bayesian Optimization of Function Networks with Partial Evaluations. CoRR abs/2311.02146 (2023) - [i20]Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy:
Joint Composite Latent Space Bayesian Optimization. CoRR abs/2311.02213 (2023) - [i19]P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman, S. Kantamneni, J. Candy, C. Holland, Maximilian Balandat, Sebastian Ament, A. E. White:
Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers. CoRR abs/2312.12610 (2023) - 2022
- [c26]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML 2022: 4831-4866 - [c25]Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 2022 - [c24]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS 2022 - [c23]Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Multi-objective Bayesian optimization over high-dimensional search spaces. UAI 2022: 507-517 - [i18]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR abs/2202.07549 (2022) - [i17]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR abs/2210.10199 (2022) - 2021
- [c22]Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.:
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. ICASSP 2021: 8138-8142 - [c21]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. NeurIPS 2021: 2187-2200 - [c20]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. NeurIPS 2021: 19274-19287 - [c19]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS 2021: 20197-20209 - [c18]Zhizhen Zhong, Manya Ghobadi, Maximilian Balandat, Sanjeevkumar Katti, Abbas Kazerouni, Jonathan Leach, Mark McKillop, Ying Zhang:
BOW: First Real-World Demonstration of a Firewall-based Bayesian Optimization System for Wavelength Deployment. OFC 2021: 1-3 - [i16]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. CoRR abs/2105.08195 (2021) - [i15]David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat:
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization. CoRR abs/2106.11890 (2021) - [i14]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. CoRR abs/2106.12997 (2021) - [i13]Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR abs/2109.10964 (2021) - [i12]Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR abs/2111.00364 (2021) - [i11]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR abs/2111.06537 (2021) - [i10]Wesley J. Maddox, Qing Feng, Maximilian Balandat:
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization. CoRR abs/2111.14911 (2021) - 2020
- [c17]Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett:
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. NeurIPS 2020 - [c16]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS 2020 - [c15]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. NeurIPS 2020 - [i9]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. CoRR abs/2006.05078 (2020) - [i8]Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett:
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. CoRR abs/2006.15779 (2020) - [i7]Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.:
Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. CoRR abs/2010.13710 (2020)
2010 – 2019
- 2019
- [i6]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR abs/1910.06403 (2019) - 2018
- [c14]Datong P. Zhou, Maximilian Balandat, Claire J. Tomlin:
Estimation and Targeting of Residential Households for Hour-Ahead Demand Response Interventions - A Case Study in California. CCTA 2018: 18-23 - 2017
- [c13]Datong P. Zhou, Maximilian Balandat, Munther A. Dahleh, Claire J. Tomlin:
Eliciting private user information for residential demand response. CDC 2017: 189-195 - [c12]Datong Paul Zhou, Maximilian Balandat, Claire Jennifer Tomlin:
Estimating Treatment Effects of a Residential Demand Response Program Using Non-experimental Data. ICDM Workshops 2017: 95-102 - [i5]Datong P. Zhou, Maximilian Balandat, Claire J. Tomlin:
Incentive Design in Human-in-the-Loop Cyber-Physical Systems: A Case Study on Demand Response in California. CoRR abs/1710.03190 (2017) - 2016
- [c11]Datong Zhou, Maximilian Balandat, Claire J. Tomlin:
A Bayesian perspective on Residential Demand Response using smart meter data. Allerton 2016: 1212-1219 - [c10]Qie Hu, Frauke Oldewurtel, Maximilian Balandat, Evangelos Vrettos, Datong Zhou, Claire J. Tomlin:
Building model identification during regular operation - empirical results and challenges. ACC 2016: 605-610 - [c9]Datong Zhou, Maximilian Balandat, Claire J. Tomlin:
Residential demand response targeting using machine learning with observational data. CDC 2016: 6663-6668 - [c8]Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen:
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games. NIPS 2016: 154-162 - [i4]Qie Hu, Frauke Oldewurtel, Maximilian Balandat, Evangelos Vrettos, Datong Zhou, Claire J. Tomlin:
Building Model Identification during Regular Operation - Empirical Results and Challenges. CoRR abs/1603.06872 (2016) - [i3]Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen:
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games. CoRR abs/1606.01261 (2016) - [i2]Datong Zhou, Maximilian Balandat, Claire J. Tomlin:
Residential Demand Response Targeting Using Machine Learning with Observational Data. CoRR abs/1607.00595 (2016) - [i1]Datong Zhou, Maximilian Balandat, Claire J. Tomlin:
A Bayesian Perspective on Residential Demand Response Using Smart Meter Data. CoRR abs/1608.03862 (2016) - 2015
- [c7]Maximilian Balandat, Ilya Tkachev, Alessandro Abate, Claire J. Tomlin:
A mean field equilibrium for a model of interbank lending. ACC 2015: 1752-1757 - [c6]Walid Krichene, Maximilian Balandat, Claire J. Tomlin, Alexandre M. Bayen:
The Hedge Algorithm on a Continuum. ICML 2015: 824-832 - 2014
- [c5]Maximilian Balandat, Frauke Oldewurtel, Mo Chen, Claire J. Tomlin:
Contract design for frequency regulation by aggregations of commercial buildings. Allerton 2014: 38-45 - 2013
- [c4]Maximilian Balandat, Claire J. Tomlin:
A dynamic VCG mechanism for random allocation spaces. Allerton 2013: 925-931 - [c3]Maximilian Balandat, Claire J. Tomlin:
On efficiency in mean field differential games. ACC 2013: 2527-2532 - 2012
- [j1]Maximilian Balandat, Wei Zhang, Alessandro Abate:
On infinite horizon switched LQR problems with state and control constraints. Syst. Control. Lett. 61(4): 464-471 (2012) - 2011
- [c2]Maximilian Balandat:
Interpolation in output-feedback tube-based robust MPC. CDC/ECC 2011: 1904-1909 - 2010
- [c1]Maximilian Balandat, Wei Zhang, Alessandro Abate:
On the infinite horizon constrained switched LQR problem. CDC 2010: 2131-2136
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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