Andreas Krause

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ETH Zurich

Professor of Computer Science at ETH Zurich

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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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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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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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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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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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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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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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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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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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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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 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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Evaluating GANs via Duality

Generative Adversarial Networks (GANs) have shown great results in accur...
11/13/2018 ∙ by Paulina Grnarova, et al. ∙ 6 ∙ 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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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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A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity

Equality of opportunity (EOP) is an extensively studied conception of fa...
09/10/2018 ∙ by Hoda Heidari, et al. ∙ 4 ∙ 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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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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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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Unsupervised Imitation Learning

We introduce a novel method to learn a policy from unsupervised demonstr...
06/19/2018 ∙ by Sebastian Curi, et al. ∙ 2 ∙ 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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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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Learning User Preferences to Incentivize Exploration in the Sharing Economy

We study platforms in the sharing economy and discuss the need for incen...
11/17/2017 ∙ by Christoph Hirnschall, et al. ∙ 0 ∙ share

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Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms

DR-submodular continuous functions are important objectives with wide re...
11/04/2017 ∙ by An Bian, et al. ∙ 0 ∙ share

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Learning Implicit Generative Models Using Differentiable Graph Tests

Recently, there has been a growing interest in the problem of learning r...
09/04/2017 ∙ by Josip Djolonga, et al. ∙ 0 ∙ share

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An Online Learning Approach to Generative Adversarial Networks

We consider the problem of training generative models with a Generative ...
06/10/2017 ∙ by Paulina Grnarova, et al. ∙ 0 ∙ share

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Safe Model-based Reinforcement Learning with Stability Guarantees

Reinforcement learning is a powerful paradigm for learning optimal polic...
05/23/2017 ∙ by Felix Berkenkamp, et al. ∙ 0 ∙ share

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Training Mixture Models at Scale via Coresets

How can we train a statistical mixture model on a massive data set? In t...
03/23/2017 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Practical Coreset Constructions for Machine Learning

We investigate coresets - succinct, small summaries of large data sets -...
03/19/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting

We consider the optimal value of information (VoI) problem, where the go...
03/16/2017 ∙ by Yuxin Chen, et al. ∙ 0 ∙ share

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Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Uniform deviation bounds limit the difference between a model's expected...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Scalable and Distributed Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models train...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Coordinated Online Learning With Applications to Learning User Preferences

We study an online multi-task learning setting, in which instances of re...
02/09/2017 ∙ by Christoph Hirnschall, et al. ∙ 0 ∙ share

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Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation

We present a new algorithm, truncated variance reduction (TruVaR), that ...
10/24/2016 ∙ by Ilija Bogunovic, et al. ∙ 0 ∙ share

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Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated co...
06/02/2015 ∙ by Hastagiri P. Vanchinathan, et al. ∙ 0 ∙ share

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Building Hierarchies of Concepts via Crowdsourcing

Hierarchies of concepts are useful in many applications from navigation ...
04/27/2015 ∙ by Yuyin Sun, et al. ∙ 0 ∙ share

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Information Gathering in Networks via Active Exploration

How should we gather information in a network, where each node's visibil...
04/24/2015 ∙ by Adish Singla, et al. ∙ 0 ∙ share

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Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

In classical reinforcement learning, when exploring an environment, agen...
06/15/2016 ∙ by Matteo Turchetta, et al. ∙ 0 ∙ share

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Horizontally Scalable Submodular Maximization

A variety of large-scale machine learning problems can be cast as instan...
05/31/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Motivated by an application of eliciting users' preferences, we investig...
05/23/2016 ∙ by Adish Singla, et al. ∙ 0 ∙ share

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Distributed Submodular Maximization

Many large-scale machine learning problems--clustering, non-parametric l...
11/03/2014 ∙ by Baharan Mirzasoleiman, et al. ∙ 0 ∙ share

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Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions

A function f: R^d →R is a Sparse Additive Model (SPAM), if it is of the ...
05/02/2016 ∙ by Hemant Tyagi, et al. ∙ 0 ∙ share

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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

Faced with massive data, is it possible to trade off (statistical) risk,...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Linear-time Outlier Detection via Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques a...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Learning Sparse Additive Models with Interactions in High Dimensions

A function f: R^d →R is referred to as a Sparse Additive Model (SPAM), i...
04/18/2016 ∙ by Hemant Tyagi, et al. ∙ 0 ∙ share

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Better safe than sorry: Risky function exploitation through safe optimization

Exploration-exploitation of functions, that is learning and optimizing a...
02/02/2016 ∙ by Eric Schulz, et al. ∙ 0 ∙ share

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Near Optimal Bayesian Active Learning for Decision Making

How should we gather information to make effective decisions? We address...
02/24/2014 ∙ by Shervin Javdani, et al. ∙ 0 ∙ share

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A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applic...
01/16/2014 ∙ by Andreas Krause, et al. ∙ 0 ∙ share

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Optimal Value of Information in Graphical Models

Many real-world decision making tasks require us to choose among several...
01/15/2014 ∙ by Andreas Krause, et al. ∙ 0 ∙ share

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Andreas Krause

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