Andreas Krause

is this you? claim profile

∙ 0 followers
ETH Zurich

Professor of Computer Science at ETH Zurich

PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

Most current classifiers are vulnerable to adversarial examples, small i...
06/14/2021 ∙ by Carl-Johann Simon-Gabriel, et al. ∙ 0 ∙ share

read it
JKOnet: Proximal Optimal Transport Modeling of Population Dynamics

Consider a heterogeneous population of points evolving with time. While ...
06/11/2021 ∙ by Charlotte Bunne, et al. ∙ 0 ∙ share

read it
Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Training models that perform well under distribution shifts is a central...
06/08/2021 ∙ by Tobias Sutter, et al. ∙ 0 ∙ share

read it
Meta-Learning Reliable Priors in the Function Space

Meta-Learning promises to enable more data-efficient inference by harnes...
06/06/2021 ∙ by Jonas Rothfuss, et al. ∙ 0 ∙ share

read it
Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations

Valuation problems, such as attribution-based feature interpretation, da...
06/05/2021 ∙ by Yatao Bian, et al. ∙ 0 ∙ share

read it
Addressing the Long-term Impact of ML Decisions via Policy Regret

Machine Learning (ML) increasingly informs the allocation of opportuniti...
06/02/2021 ∙ by David Lindner, et al. ∙ 0 ∙ share

read it
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation

We propose an end-to-end trainable framework that processes large-scale ...
05/29/2021 ∙ by Mikhail Usvyatsov, et al. ∙ 14 ∙ share

read it
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning

Causal structure learning is a key problem in many domains. Causal struc...
05/28/2021 ∙ by Scott Sussex, et al. ∙ 0 ∙ share

read it
DiBS: Differentiable Bayesian Structure Learning

Bayesian structure learning allows inferring Bayesian network structure ...
05/25/2021 ∙ by Lars Lorch, et al. ∙ 21 ∙ share

read it
Bias-Robust Bayesian Optimization via Dueling Bandit

We consider Bayesian optimization in settings where observations can be ...
05/25/2021 ∙ by Johannes Kirschner, et al. ∙ 0 ∙ share

read it
Regret Bounds for Gaussian-Process Optimization in Large Domains

The goal of this paper is to characterize Gaussian-Process optimization ...
04/29/2021 ∙ by Manuel Wüthrich, et al. ∙ 11 ∙ share

read it
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution

Bayesian Optimization (BO) is a successful methodology to tune the hyper...
04/16/2021 ∙ by Anastasia Makarova, et al. ∙ 0 ∙ share

read it
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning

In real-world tasks, reinforcement learning (RL) agents frequently encou...
03/18/2021 ∙ by Sebastian Curi, et al. ∙ 19 ∙ share

read it
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

read it
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

read it
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

read it
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

read it
Incentive-Compatible Forecasting Competitions

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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it
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

read it

Andreas Krause

Featured Co-authors