Maximilian Balandat
Maximilian Balandat
Research Scientist Manager, Meta
Verified email at - Homepage
Cited by
Cited by
BoTorch: A framework for efficient Monte-Carlo Bayesian optimization
M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy
Advances in neural information processing systems 33, 2020
Contract design for frequency regulation by aggregations of commercial buildings
M Balandat, F Oldewurtel, M Chen, C Tomlin
2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
S Daulton, M Balandat, E Bakshy
arXiv preprint arXiv:2006.05078, 2020
Residential demand response targeting using machine learning with observational data
D Zhou, M Balandat, C Tomlin
2016 IEEE 55th conference on decision and control (CDC), 6663-6668, 2016
On infinite horizon switched LQR problems with state and control constraints
M Balandat, W Zhang, A Abate
Systems & Control Letters 61 (4), 464-471, 2012
Constrained robust optimal trajectory tracking: Model predictive control approaches
M Balandat
Master's Thesis, Technische Universitat Darmstadt, 2010
The hedge algorithm on a continuum
W Krichene, M Balandat, C Tomlin, A Bayen
International Conference on Machine Learning, 824-832, 2015
Building model identification during regular operation-empirical results and challenges
Q Hu, F Oldewurtel, M Balandat, E Vrettos, D Zhou, CJ Tomlin
2016 American Control Conference (ACC), 605-610, 2016
A bayesian perspective on residential demand response using smart meter data
D Zhou, M Balandat, C Tomlin
2016 54th Annual Allerton Conference on Communication, Control, and …, 2016
On efficiency in mean field differential games
M Balandat, CJ Tomlin
2013 American Control Conference, 2527-2532, 2013
Efficient nonmyopic Bayesian optimization via one-shot multi-step trees
S Jiang, D Jiang, M Balandat, B Karrer, J Gardner, R Garnett
Advances in Neural Information Processing Systems 33, 2020
Eliciting private user information for residential demand response
DP Zhou, M Balandat, MA Dahleh, CJ Tomlin
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 189-195, 2017
Optimizing coverage and capacity in cellular networks using machine learning
RM Dreifuerst, S Daulton, Y Qian, P Varkey, M Balandat, S Kasturia, ...
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
Minimizing regret on reflexive Banach spaces and Nash equilibria in continuous zero-sum games
M Balandat, W Krichene, C Tomlin, A Bayen
Advances in Neural Information Processing Systems, 154-162, 2016
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
M Balandat, B Karrer, DR Jiang, S Daulton, B Letham, AG Wilson, ...
arXiv preprint arXiv:1910.06403, 2019
Estimating treatment effects of a residential demand response program using non-experimental data
DP Zhou, M Balandat, CJ Tomlin
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 95-102, 2017
Minimizing regret on reflexive banach spaces and learning nash equilibria in continuous zero-sum games
M Balandat, W Krichene, C Tomlin, A Bayen
arXiv preprint arXiv:1606.01261, 2016
New tools for econometric analysis of high-frequency time series data-application to demand-side management in electricity markets
M Balandat
University of California, Berkeley, 2016
Sustainable ai: Environmental implications, challenges and opportunities
CJ Wu, R Raghavendra, U Gupta, B Acun, N Ardalani, K Maeng, G Chang, ...
arXiv preprint arXiv:2111.00364, 2021
Welfare effects of dynamic electricity pricing
C Campaigne, M Balandat, L Ratliff
Working Paper, 2016
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