Publications by Andreas Krause

Related publications (10)

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Daniel Kuhn, Andreas Krause, Tobias Sutter

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

2021

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

Volkan Cevher, Jonathan Mark Scarlett, Andreas Krause, Ilija Bogunovic

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set ...

2016

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

3DReact: Geometric Deep Learning for Chemical Reactions

Vignesh Ram Somnath, Andreas Krause, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Puck Elisabeth van Gerwen, Ksenia Briling, Charlotte Cäcilie Gertrud Bunne

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we int ...

2024

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Andreas Krause, Matthias Seeger

Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low norm in a reproducing ...

Institute of Electrical and Electronics Engineers2012

High-Dimensional Gaussian Process Bandits

Volkan Cevher, Andreas Krause

Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain. We address this notoriously hard challenge, under the assumptions that the function varies onl ...

2013

Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints

Boi Faltings, Andreas Krause, Goran Radanovic

We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information. This problem arises in many real world applications that rely on crowdsourced data sets, but where the process of ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Andreas Krause, Maryam Kamgarpour

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

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

Andreas Krause, Martin Josifoski

Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021