Maximilian Balandat

72 papers A* 16A 2B 1C 3Misc 1Journal 35Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2026	J	`jnl`	Empirical Gaussian Processes. CoRR Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy
2026	J	`jnl`	MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment. CoRR Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghuraman Krishnamoorthi
2025	—	`conf`	Ax: A Platform for Adaptive Experimentation. AutoML Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy
2025	—	`conf`	Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. KDD (1) Qing Feng, Samuel Daulton, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
2025	J	`jnl`	Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. CoRR Qing Feng, Samuel Daulton, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
2025	J	`jnl`	Informed Initialization for Bayesian Optimization and Active Learning. CoRR Carl Hvarfner, David Eriksson, Eytan Bakshy, Maximilian Balandat
2025	J	`jnl`	LILO: Bayesian Optimization with Interactive Natural Language Feedback. CoRR Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham, Zhuokai Zhao, Maximilian Balandat, Eytan Bakshy
2025	A*	`conf`	Scalable Gaussian Processes with Latent Kronecker Structure. ICML Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes with Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2024	A*	`conf`	Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. ICML Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin T. Carlberg, Neil Walton, Kody J. H. Law
2024	J	`jnl`	Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. CoRR Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law
2024	A*	`conf`	Bayesian Optimization of Function Networks with Partial Evaluations. ICML Poompol Buathong, Jiayue Wan, Raul Astudillo, Samuel Daulton, Maximilian Balandat, Peter I. Frazier
2024	A*	`conf`	Joint Composite Latent Space Bayesian Optimization. ICML Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy
2024	A*	`conf`	Robust Gaussian Processes via Relevance Pursuit. NeurIPS Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Robust Gaussian Processes via Relevance Pursuit. CoRR Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Bayesian Optimization of Function Networks with Partial Evaluations. CoRR Poompol Buathong, Jiayue Wan, Samuel Daulton, Raul Astudillo, Maximilian Balandat, Peter I. Frazier
2023	A	`conf`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. AISTATS Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
2023	J	`jnl`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. CoRR Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
2023	J	`jnl`	Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers. CoRR P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman, S. Kantamneni, J. Candy, C. Holland, Maximilian Balandat, Sebastian Ament, A. E. White
2023	B	`conf`	HHVM Performance Optimization for Large Scale Web Services. ICPE Yuhao Li, Abhishek Gupta, Alex Yang, Peinan Chen, Joey Pinto, Brian Karrer, Mayank Pundir, Maximilian Balandat, Arun Kejariwal, Benjamin C. Lee
2023	A*	`conf`	Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. ICML Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Joint Composite Latent Space Bayesian Optimization. CoRR Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Quasi-Monte Carlo Methods in Python. J. Open Source Softw. Pamphile T. Roy, Art B. Owen, Maximilian Balandat, Matt Haberland
2023	A*	`conf`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. NeurIPS Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. CoRR Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A*	`conf`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	J	`jnl`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	A	`conf`	Multi-objective Bayesian optimization over high-dimensional search spaces. UAI Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A*	`conf`	Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
2022	J	`jnl`	Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
2022	—	`conf`	Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 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 C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2021	—	`conf`	BOW: First Real-World Demonstration of a Firewall-based Bayesian Optimization System for Wavelength Deployment. OFC Zhizhen Zhong, Manya Ghobadi, Maximilian Balandat, Sanjeevkumar Katti, Abbas Kazerouni, Jonathan Leach, Mark McKillop, Ying Zhang
2021	A*	`conf`	Bayesian Optimization with High-Dimensional Outputs. NeurIPS Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	J	`jnl`	Bayesian Optimization with High-Dimensional Outputs. CoRR Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	J	`jnl`	Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization. CoRR David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat
2021	J	`jnl`	Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2021	A*	`conf`	Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
2021	J	`jnl`	Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
2021	Misc	`conf`	Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. ICASSP Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.
2021	J	`jnl`	Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization. CoRR Wesley J. Maddox, Qing Feng, Maximilian Balandat
2021	A*	`conf`	Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. NeurIPS Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2021	J	`jnl`	Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. CoRR Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2021	J	`jnl`	Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR 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 C. Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood
2020	A*	`conf`	BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2020	A*	`conf`	Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. NeurIPS Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2020	J	`jnl`	Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. CoRR Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2020	A*	`conf`	Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. NeurIPS Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
2020	J	`jnl`	Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. CoRR Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
2020	J	`jnl`	Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. CoRR Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.
2019	J	`jnl`	BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2018	—	`conf`	Estimation and Targeting of Residential Households for Hour-Ahead Demand Response Interventions - A Case Study in California. CCTA Datong P. Zhou, Maximilian Balandat, Claire J. Tomlin
2017	—	`conf`	Eliciting private user information for residential demand response. CDC Datong P. Zhou, Maximilian Balandat, Munther A. Dahleh, Claire J. Tomlin
2017	—	`conf`	Estimating Treatment Effects of a Residential Demand Response Program Using Non-experimental Data. ICDM Workshops Datong Paul Zhou, Maximilian Balandat, Claire Jennifer Tomlin
2017	J	`jnl`	Incentive Design in Human-in-the-Loop Cyber-Physical Systems: A Case Study on Demand Response in California. CoRR Datong P. Zhou, Maximilian Balandat, Claire J. Tomlin
2016	J	`jnl`	A Bayesian Perspective on Residential Demand Response Using Smart Meter Data. CoRR Datong Zhou, Maximilian Balandat, Claire J. Tomlin
2016	—	`conf`	A Bayesian perspective on Residential Demand Response using smart meter data. Allerton Datong Zhou, Maximilian Balandat, Claire J. Tomlin
2016	J	`jnl`	Building Model Identification during Regular Operation - Empirical Results and Challenges. CoRR Qie Hu, Frauke Oldewurtel, Maximilian Balandat, Evangelos Vrettos, Datong Zhou, Claire J. Tomlin
2016	C	`conf`	Building model identification during regular operation - empirical results and challenges. ACC Qie Hu, Frauke Oldewurtel, Maximilian Balandat, Evangelos Vrettos, Datong Zhou, Claire J. Tomlin
2016	J	`jnl`	Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games. CoRR Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen
2016	—	`conf`	Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games. NIPS Maximilian Balandat, Walid Krichene, Claire J. Tomlin, Alexandre M. Bayen
2016	J	`jnl`	Residential Demand Response Targeting Using Machine Learning with Observational Data. CoRR Datong Zhou, Maximilian Balandat, Claire J. Tomlin
2016	—	`conf`	Residential demand response targeting using machine learning with observational data. CDC Datong Zhou, Maximilian Balandat, Claire J. Tomlin
2015	C	`conf`	A mean field equilibrium for a model of interbank lending. ACC Maximilian Balandat, Ilya Tkachev, Alessandro Abate, Claire J. Tomlin
2015	A*	`conf`	The Hedge Algorithm on a Continuum. ICML Walid Krichene, Maximilian Balandat, Claire J. Tomlin, Alexandre M. Bayen
2014	—	`conf`	Contract design for frequency regulation by aggregations of commercial buildings. Allerton Maximilian Balandat, Frauke Oldewurtel, Mo Chen, Claire J. Tomlin
2013	—	`conf`	A dynamic VCG mechanism for random allocation spaces. Allerton Maximilian Balandat, Claire J. Tomlin
2013	C	`conf`	On efficiency in mean field differential games. ACC Maximilian Balandat, Claire J. Tomlin
2012	J	`jnl`	On infinite horizon switched LQR problems with state and control constraints. Syst. Control. Lett. Maximilian Balandat, Wei Zhang, Alessandro Abate
2011	—	`conf`	Interpolation in output-feedback tube-based robust MPC. CDC/ECC Maximilian Balandat
2010	—	`conf`	On the infinite horizon constrained switched LQR problem. CDC Maximilian Balandat, Wei Zhang, Alessandro Abate