Andreas Krause

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ETH Zurich

Professor of Computer Science at ETH Zurich

Information Directed Reward Learning for Reinforcement Learning

For many reinforcement learning (RL) applications, specifying a reward i...
02/24/2021 ∙ by David Lindner, et al. ∙ 0 ∙ share

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Risk-Averse Offline Reinforcement Learning

Training Reinforcement Learning (RL) agents in high-stakes applications ...
02/10/2021 ∙ by Núria Armengol Urpí, et al. ∙ 0 ∙ share

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Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback

Combinatorial bandits with semi-bandit feedback generalize multi-armed b...
01/21/2021 ∙ by Marc Jourdan, et al. ∙ 0 ∙ share

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Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Adaptive control approaches yield high-performance controllers when a pr...
01/19/2021 ∙ by Christopher König, et al. ∙ 0 ∙ share

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Incentive-Compatible Forecasting Competitions

We initiate the study of incentive-compatible forecasting competitions i...
01/05/2021 ∙ by Jens Witkowski, et al. ∙ 0 ∙ share

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Logistic Q-Learning

We propose a new reinforcement learning algorithm derived from a regular...
10/21/2020 ∙ by Joan Bas-Serrano, et al. ∙ 0 ∙ share

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Online Active Model Selection for Pre-trained Classifiers

Given k pre-trained classifiers and a stream of unlabeled data examples,...
10/19/2020 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ share

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Semi-supervised Batch Active Learning via Bilevel Optimization

Active learning is an effective technique for reducing the labeling cost...
10/19/2020 ∙ by Zalán Borsos, et al. ∙ 0 ∙ share

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Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Gradient estimation in models with discrete latent variables is a challe...
10/09/2020 ∙ by Max B. Paulus, et al. ∙ 8 ∙ share

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Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases

Many applications of machine learning on discrete domains, such as learn...
10/01/2020 ∙ by Chris Wendler, et al. ∙ 16 ∙ share

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Learning to Play Sequential Games versus Unknown Opponents

We consider a repeated sequential game between a learner, who plays firs...
07/10/2020 ∙ by Pier Giuseppe Sessa, et al. ∙ 0 ∙ share

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Stochastic Linear Bandits Robust to Adversarial Attacks

We consider a stochastic linear bandit problem in which the rewards are ...
07/07/2020 ∙ by Ilija Bogunovic, et al. ∙ 0 ∙ share

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Continuous Submodular Function Maximization

Continuous submodular functions are a category of generally non-convex/n...
06/24/2020 ∙ by Yatao Bian, et al. ∙ 4 ∙ share

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Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory

We present the first approach for learning – from a single trajectory – ...
06/19/2020 ∙ by Lenart Treven, et al. ∙ 0 ∙ share

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Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Model-based reinforcement learning algorithms with probabilistic dynamic...
06/15/2020 ∙ by Sebastian Curi, et al. ∙ 23 ∙ share

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Gradient Estimation with Stochastic Softmax Tricks

The Gumbel-Max trick is the basis of many relaxed gradient estimators. T...
06/15/2020 ∙ by Max B. Paulus, et al. ∙ 0 ∙ share

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Learning Graph Models for Template-Free Retrosynthesis

Retrosynthesis prediction is a fundamental problem in organic synthesis,...
06/12/2020 ∙ by Vignesh Ram Somnath, et al. ∙ 17 ∙ share

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Coresets via Bilevel Optimization for Continual Learning and Streaming

Coresets are small data summaries that are sufficient for model training...
06/06/2020 ∙ by Zalán Borsos, et al. ∙ 9 ∙ share

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From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Submodular functions have been studied extensively in machine learning a...
06/01/2020 ∙ by Aytunc Sahin, et al. ∙ 18 ∙ share

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Hierarchical Image Classification using Entailment Cone Embeddings

Image classification has been studied extensively, but there has been li...
04/02/2020 ∙ by Ankit Dhall, et al. ∙ 4 ∙ share

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SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

Gaussian processes are an important regression tool with excellent analy...
03/05/2020 ∙ by Emmanouil Angelis, et al. ∙ 0 ∙ share

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Corruption-Tolerant Gaussian Process Bandit Optimization

We consider the problem of optimizing an unknown (typically non-convex) ...
03/04/2020 ∙ by Ilija Bogunovic, et al. ∙ 0 ∙ share

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Mixed Strategies for Robust Optimization of Unknown Objectives

We consider robust optimization problems, where the goal is to optimize ...
02/28/2020 ∙ by Pier Giuseppe Sessa, et al. ∙ 8 ∙ share

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Information Directed Sampling for Linear Partial Monitoring

Partial monitoring is a rich framework for sequential decision making un...
02/25/2020 ∙ by Johannes Kirschner, et al. ∙ 21 ∙ share

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Distributionally Robust Bayesian Optimization

Robustness to distributional shift is one of the key challenges of conte...
02/20/2020 ∙ by Johannes Kirschner, et al. ∙ 6 ∙ share

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PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees

Meta-learning can successfully acquire useful inductive biases from data...
02/13/2020 ∙ by Jonas Rothfuss, et al. ∙ 3 ∙ share

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Log Barriers for Safe Non-convex Black-box Optimization

We address the problem of minimizing a smooth function f^0(x) over a com...
12/19/2019 ∙ by Ilnura Usmanova, et al. ∙ 0 ∙ share

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Safe non-smooth black-box optimization with application to policy search

For safety-critical black-box optimization tasks, observations of the co...
12/19/2019 ∙ by Ilnura Usmanova, et al. ∙ 0 ∙ share

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A Human-in-the-loop Framework to Construct Context-dependent Mathematical Formulations of Fairness

Despite the recent surge of interest in designing and guaranteeing mathe...
11/08/2019 ∙ by Mohammad Yaghini, et al. ∙ 18 ∙ share

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Safe Exploration for Interactive Machine Learning

In Interactive Machine Learning (IML), we iteratively make decisions and...
10/30/2019 ∙ by Matteo Turchetta, et al. ∙ 20 ∙ share

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Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

In reinforcement learning (RL), an autonomous agent learns to perform co...
10/29/2019 ∙ by Matteo Turchetta, et al. ∙ 10 ∙ share

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Adaptive Sampling for Stochastic Risk-Averse Learning

We consider the problem of training machine learning models in a risk-av...
10/28/2019 ∙ by Sebastian Curi, et al. ∙ 24 ∙ share

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Convergence Analysis of the Randomized Newton Method with Determinantal Sampling

We analyze the convergence rate of the Randomized Newton Method (RNM) in...
10/25/2019 ∙ by Mojmir Mutny, et al. ∙ 0 ∙ share

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No-Regret Learning in Unknown Games with Correlated Payoffs

We consider the problem of learning to play a repeated multi-agent game ...
09/18/2019 ∙ by Pier Giuseppe Sessa, et al. ∙ 33 ∙ share

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Noise Regularization for Conditional Density Estimation

Modelling statistical relationships beyond the conditional mean is cruci...
07/21/2019 ∙ by Jonas Rothfuss, et al. ∙ 3 ∙ share

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Structured Variational Inference in Unstable Gaussian Process State Space Models

Gaussian processes are expressive, non-parametric statistical models tha...
07/16/2019 ∙ by Silvan Melchior, et al. ∙ 2 ∙ share

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Mixed-Variable Bayesian Optimization

The optimization of expensive to evaluate, black-box, mixed-variable fun...
07/02/2019 ∙ by Erik Daxberger, et al. ∙ 12 ∙ share

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Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

We tune one of the most common heating, ventilation, and air conditionin...
06/28/2019 ∙ by Marcello Fiducioso, et al. ∙ 3 ∙ share

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Stochastic Bandits with Context Distributions

We introduce a novel stochastic contextual bandit model, where at each s...
06/06/2019 ∙ by Johannes Kirschner, et al. ∙ 6 ∙ share

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Learning Generative Models across Incomparable Spaces

Generative Adversarial Networks have shown remarkable success in learnin...
05/14/2019 ∙ by Charlotte Bunne, et al. ∙ 1 ∙ share

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Online Variance Reduction with Mixtures

Adaptive importance sampling for stochastic optimization is a promising ...
03/29/2019 ∙ by Zalán Borsos, et al. ∙ 20 ∙ share

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Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature

Games with continuous strategy sets arise in several machine learning pr...
03/03/2019 ∙ by Pier Giuseppe Sessa, et al. ∙ 0 ∙ share

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AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in man...
02/22/2019 ∙ by Gabriele Abbati, et al. ∙ 12 ∙ share

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Multi-Player Bandits: The Adversarial Case

We consider a setting where multiple players sequentially choose among a...
02/21/2019 ∙ by Pragnya Alatur, et al. ∙ 0 ∙ share

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ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Parameter inference in ordinary differential equations is an important p...
02/17/2019 ∙ by Philippe Wenk, et al. ∙ 10 ∙ share

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Adaptive Sequence Submodularity

In many machine learning applications, one needs to interactively select...
02/15/2019 ∙ by Marko Mitrovic, et al. ∙ 8 ∙ share

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Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning

Fairness for Machine Learning has received considerable attention, recen...
02/13/2019 ∙ by Megha Srivastava, et al. ∙ 0 ∙ share

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

Bayesian optimization is known to be difficult to scale to high dimensio...
02/08/2019 ∙ by Johannes Kirschner, et al. ∙ 30 ∙ share

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No-regret Bayesian Optimization with Unknown Hyperparameters

Bayesian optimization (BO) based on Gaussian process models is a powerfu...
01/10/2019 ∙ by Felix Berkenkamp, et al. ∙ 26 ∙ share

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Information-Directed Exploration for Deep Reinforcement Learning

Efficient exploration remains a major challenge for reinforcement learni...
12/18/2018 ∙ by Nikolay Nikolov, et al. ∙ 18 ∙ share

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Andreas Krause

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