355 citations · 744 across the 12 of their papers we have counts for
5 papers · 1 filter
Sustainable AI: Environmental Implications, Challenges and Opportunities
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta +22
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize t…
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
Optimizing multiple competing black-box objectives is a challenging problem in many fields, including science, engineering, and machine learning. Multi-objective Bayesian optimizat…
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel R. Jiang, Maximilian Balandat +3
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a hig…
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang +4
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental…
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin +1
We study a general version of the adversarial online learning problem. We are given a decision set in a reflexive Banach space and a sequence of reward vectors in…