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Found 11 papers, 4 papers with code
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.
Submodular Reinforcement Learning
In many important applications, such as coverage control, experiment design and informative path planning, rewards naturally have diminishing returns, i. e., their value decreases in light of similar states visited previously.
Active Exploration via Experiment Design in Markov Chains
A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest.
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
In this work, we investigate the optimal design of experiments for {\em estimation of linear functionals in reproducing kernel Hilbert spaces (RKHSs)}.
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors.
Sensing Cox Processes via Posterior Sampling and Positive Bases
We study adaptive sensing of Cox point processes, a widely used model from spatial statistics.
Data Summarization via Bilevel Optimization
We show the effectiveness of our framework for a wide range of models in various settings, including training non-convex models online and batch active learning.
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Combinatorial bandits with semi-bandit feedback generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chosen set.
Coresets via Bilevel Optimization for Continual Learning and Streaming
Coresets are small data summaries that are sufficient for model training.
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
We analyze the convergence rate of the randomized Newton-like method introduced by Qu et.
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
In order to scale the method and keep its benefits, we propose an algorithm (LineBO) that restricts the problem to a sequence of iteratively chosen one-dimensional sub-problems that can be solved efficiently.
