Mojmír Mutný

Mojmír Mutný

PhD Student

ETH Zurich

I am a senior PhD student at ETH Zurich under the supervisior of Andreas Krause in Learning and Adaptive Systems group. My current research work mostly focuses on modern instances of Experimental Design – a branch of statistics addressing the search for the most informative experiments in order to infer an unknown statistical quantity.

I am also interested in/have worked on Bayesian optimization, kernel methods, sensor selection, control, bandit algorithms and convex optimization. Some of my algorithms have found application in machine calibration, spatial analysis and directed evolution.


  • Mathematical Optimization
  • Machine Learning
  • Active Learning
  • Experimental design
  • Bandit Algorithms


  • PhD in Computer Science, 2018-Now

    ETH Zurich

  • MSc in Computational Science, 2017

    ETH Zurich

  • BSc in Mathematical Physics, 2015

    University of Edinburgh

Recent Posts


Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

In this work we introduced DPP-BBO, a natural and easily applicable framework for enhancing batch diversity in BBO algorithms which …

Experimental Design of Linear Functionals in Reproducing Kernel Hilbert Spaces

Optimal experimental design seeks to determine the most informative allocation of experiments to infer an unknown statistical …

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many …

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. Classical …

Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory

We present the first approach for learning–from a single trajectory–a linear quadratic regulator (LQR), even for unstable …

Data Summarization via Bilevel Optimization

The increasing availability of massive data sets poses a series of challenges for machine learning. Prominent among these is the need …

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 …

No-regret Algorithms for Capturing Events in Poisson Point Processes

Inhomogeneous Poisson point processes are widely used models of event occurrences. We address emphadaptive sensing of Poisson Point …

MakeSense: Automated Sensor Design for Proprioceptive Soft Robots

Soft robots have applications in safe human–robot interactions, manipulation of fragile objects, and locomotion in challenging and …

Sensing Cox Processes via Posterior Sampling and Positive Bases

We study adaptive sensing of Cox point processes, a widely used model from spatial statistics. We introduce three tasks: maximization …

Coresets via Bilevel Optimization for Continual Learning and Streaming

Coresets are small data summaries that are sufficient for model training. They can be maintained online, enabling efficient handling of …

Experimental Design for Optimization of Orthogonal Projection Pursuit Models

Bayesian optimization and kernelized bandit algorithms are widely used techniques for sequential black box function optimization with …

Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling

We analyze the convergence rate of the randomized Newton-like method introduced by Qu et. al. (2016) for smooth and convex objectives, …

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex …

Bayesian Optimization for Fast and Safe Parameter Tuning of SwissFEL

Parameter tuning is a notoriously time-consuming task in accelerator facilities. As tool for global optimization with noisy …

Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features

We develop an efficient and provably no-regret Bayesian optimization (BO) algorithm for optimization of black-box functions in high …

Parallel Stochastic Newton Method

We propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the …

Stochastic second-order optimization via von Neumann series

A stochastic iterative algorithm approximating second-order information using von Neumann series is discussed. We present convergence …


Quadrature Fourier Features (QFF) for Python

This repository includes the code used in paper:Mojmir Mutny & Andreas Krause, “Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features”, NIPS 2018 It provides an efficient finite basis approximation for RBF and Matern kernels in low dimensions.