86 citations · 132 across the 10 of their papers we have counts for
10 papers
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…
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces
Mojmír Mutný, Andreas Krause
Optimal experimental design seeks to determine the most informative allocation of experiments to infer an unknown statistical quantity. In this work, we investigate the optimal des…
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Johannes Kirschner, Mojmir Mutný, Andreas Krause +3
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are ava…
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…
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Marc Jourdan, Mojmír Mutný, Johannes Kirschner +1
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 chose…
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…
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling
Mojmír Mutný, Michał Dereziński, Andreas Krause
We analyze the convergence rate of the randomized Newton-like method introduced by Qu et. al. (2016) for smooth and convex objectives, which uses random coordinate blocks of a Hess…
Parallel Stochastic Newton Method
Mojmír Mutný, Peter Richtárik
We propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions un…