86 citations · 132 across the 10 of their papers we have counts for
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Active Exploration via Experiment Design in Markov Chains
Mojmír Mutný, Tadeusz Janik, Andreas Krause
A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest. Classical experimental design optimally allocates the experime…
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes
Elvis Nava, Mojmír Mutný, Andreas Krause
In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors. Convergence of BO can be greatly sped up by batching, where…
Sensing Cox Processes via Posterior Sampling and Positive Bases
Mojmír Mutný, Andreas Krause
We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization of captured events, search for the maximum of…
Data Summarization via Bilevel Optimization
Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi +1
The increasing availability of massive data sets poses a series of challenges for machine learning. Prominent among these is the need to learn models under hardware or human resour…
Coresets via Bilevel Optimization for Continual Learning and Streaming
Zalán Borsos, Mojmír Mutný, Andreas Krause
Coresets are small data summaries that are sufficient for model training. They can be maintained online, enabling efficient handling of large data streams under resource constraint…