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07/25/2023
Submodular Reinforcement Learning
In reinforcement learning (RL), rewards of states are typically consider...
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06/29/2022
Active Exploration via Experiment Design in Markov Chains
A key challenge in science and engineering is to design experiments to l...
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05/26/2022
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Optimal experimental design seeks to determine the most informative allo...
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03/26/2022
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Tuning machine parameters of particle accelerators is a repetitive and t...
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10/22/2021
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
In Bayesian Optimization (BO) we study black-box function optimization w...
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10/21/2021
Sensing Cox Processes via Posterior Sampling and Positive Bases
We study adaptive sensing of Cox point processes, a widely used model fr...
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09/26/2021
Data Summarization via Bilevel Optimization
The increasing availability of massive data sets poses a series of chall...
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01/21/2021
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Combinatorial bandits with semi-bandit feedback generalize multi-armed b...
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06/19/2020
Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
We present the first approach for learning – from a single trajectory – ...
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06/06/2020
Coresets via Bilevel Optimization for Continual Learning and Streaming
Coresets are small data summaries that are sufficient for model training...
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10/25/2019
Convergence Analysis of the Randomized Newton Method with Determinantal Sampling
We analyze the convergence rate of the Randomized Newton Method (RNM) in...
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02/08/2019
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Bayesian optimization is known to be difficult to scale to high dimensio...
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12/13/2015