Andreas Krause

research

∙ 09/05/2023

Distributionally Robust Model-based Reinforcement Learning with Large State Spaces

Three major challenges in reinforcement learning are the complex dynamic...

0 Shyam Sundhar Ramesh, et al. ∙

research

∙ 08/03/2023

Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning

Multitask learning is a powerful framework that enables one to simultane...

0 Pier Giuseppe Sessa, et al. ∙

research

∙ 07/31/2023

Adversarial Causal Bayesian Optimization

In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown...

0 Scott Sussex, et al. ∙

research

∙ 07/25/2023

Submodular Reinforcement Learning

In reinforcement learning (RL), rewards of states are typically consider...

0 Manish Prajapat, et al. ∙

research

∙ 07/24/2023

Anytime Model Selection in Linear Bandits

Model selection in the context of bandit optimization is a challenging p...

0 Parnian Kassraie, et al. ∙

research

∙ 06/29/2023

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning

Many applications, e.g., in shared mobility, require coordinating a larg...

0 Matej Jusup, et al. ∙

research

∙ 06/23/2023

Safe Risk-averse Bayesian Optimization for Controller Tuning

Controller tuning and parameter optimization are crucial in system desig...

0 Christopher Koenig, et al. ∙

research

∙ 06/21/2023

Optimistic Active Exploration of Dynamical Systems

Reinforcement learning algorithms commonly seek to optimize policies for...

0 Bhavya Sukhija, et al. ∙

research

∙ 06/15/2023

Unbalanced Diffusion Schrödinger Bridge

Schrödinger bridges (SBs) provide an elegant framework for modeling the ...

0 Matteo Pariset, et al. ∙

research

∙ 06/13/2023

Provably Learning Nash Policies in Constrained Markov Potential Games

Multi-agent reinforcement learning (MARL) addresses sequential decision-...

0 Pragnya Alatur, et al. ∙

research

∙ 06/12/2023

Tuning Legged Locomotion Controllers via Safe Bayesian Optimization

In this paper, we present a data-driven strategy to simplify the deploym...

0 Daniel Widmer, et al. ∙

research

∙ 05/25/2023

Learning Safety Constraints from Demonstrations with Unknown Rewards

We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...

0 David Lindner, et al. ∙

research

∙ 05/16/2023

A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks

Despite the capacity of neural nets to learn arbitrary functions, models...

0 Ali Gorji, et al. ∙

research

∙ 05/09/2023

Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement

Spinal fusion surgery requires highly accurate implantation of pedicle s...

0 Yunke Ao, et al. ∙

research

∙ 03/02/2023

Hallucinated Adversarial Control for Conservative Offline Policy Evaluation

We study the problem of conservative off-policy evaluation (COPE) where ...

0 Jonas Rothfuss, et al. ∙

research

∙ 02/22/2023

Aligned Diffusion Schrödinger Bridges

Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful ...

0 Vignesh Ram Somnath, et al. ∙

research

∙ 02/07/2023

Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications

Partial monitoring is an expressive framework for sequential decision-ma...

0 Johannes Kirschner, et al. ∙

research

∙ 01/24/2023

Learning To Dive In Branch And Bound

Primal heuristics are important for solving mixed integer linear program...

0 Max B. Paulus, et al. ∙

research

∙ 11/18/2022

Model-based Causal Bayesian Optimization

How should we intervene on an unknown structural causal model to maximiz...

0 Scott Sussex, et al. ∙

research

∙ 11/14/2022

PAC-Bayesian Meta-Learning: From Theory to Practice

Meta-Learning aims to accelerate the learning on new tasks by acquiring ...

0 Jonas Rothfuss, et al. ∙

research

∙ 11/03/2022

Isotropic Gaussian Processes on Finite Spaces of Graphs

We propose a principled way to define Gaussian process priors on various...

0 Viacheslav Borovitskiy, et al. ∙

research

∙ 11/02/2022

Instance-Dependent Generalization Bounds via Optimal Transport

Existing generalization bounds fail to explain crucial factors that driv...

0 Songyan Hou, et al. ∙

research

∙ 10/27/2022

Lifelong Bandit Optimization: No Prior and No Regret

In practical applications, machine learning algorithms are often repeate...

0 Felix Schur, et al. ∙

research

∙ 10/26/2022

Leveraging Demonstrations with Latent Space Priors

Demonstrations provide insight into relevant state or action space regio...

0 Jonas Gehring, et al. ∙

research

∙ 10/25/2022

A Dynamical System View of Langevin-Based Non-Convex Sampling

Non-convex sampling is a key challenge in machine learning, central to n...

0 Mohammad Reza Karimi, et al. ∙

research

∙ 10/24/2022

MARS: Meta-Learning as Score Matching in the Function Space

Meta-learning aims to extract useful inductive biases from a set of rela...

0 Krunoslav Lehman Pavasovic, et al. ∙

research

∙ 10/14/2022

Movement Penalized Bayesian Optimization with Application to Wind Energy Systems

Contextual Bayesian optimization (CBO) is a powerful framework for seque...

0 Shyam Sundhar Ramesh, et al. ∙

research

∙ 10/12/2022

Near-Optimal Multi-Agent Learning for Safe Coverage Control

In multi-agent coverage control problems, agents navigate their environm...

0 Manish Prajapat, et al. ∙

research

∙ 10/04/2022

Reproducible Bandits

In this paper, we introduce the notion of reproducible policies in the c...

0 Hossein Esfandiari, et al. ∙

research

∙ 10/03/2022

Meta-Learning Priors for Safe Bayesian Optimization

In robotics, optimizing controller parameters under safety constraints i...

0 Jonas Rothfuss, et al. ∙

research

∙ 07/21/2022

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning

Optimizing noisy functions online, when evaluating the objective require...

0 Ilnura Usmanova, et al. ∙

research

∙ 07/18/2022

Active Exploration for Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferrin...

0 David Lindner, et al. ∙

research

∙ 07/13/2022

Graph Neural Network Bandits

We consider the bandit optimization problem with the reward function def...

42 Parnian Kassraie, et al. ∙

research

∙ 07/04/2022

Safe Reinforcement Learning via Confidence-Based Filters

Ensuring safety is a crucial challenge when deploying reinforcement lear...

0 Sebastian Curi, et al. ∙

research

∙ 06/29/2022

Active Exploration via Experiment Design in Markov Chains

A key challenge in science and engineering is to design experiments to l...

0 Mojmir Mutny, et al. ∙

research

∙ 06/28/2022

Supervised Training of Conditional Monge Maps

Optimal transport (OT) theory describes general principles to define and...

0 Charlotte Bunne, et al. ∙

research

∙ 06/27/2022

Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning

Cutting planes are essential for solving mixed-integer linear problems (...

2 Max B. Paulus, et al. ∙

research

∙ 06/23/2022

Invariant Causal Mechanisms through Distribution Matching

Learning representations that capture the underlying data generating pro...

0 Mathieu Chevalley, et al. ∙

research

∙ 06/14/2022

The Dynamics of Riemannian Robbins-Monro Algorithms

Many important learning algorithms, such as stochastic gradient methods,...

0 Mohammad Reza Karimi, et al. ∙

research

∙ 06/10/2022

Interactively Learning Preference Constraints in Linear Bandits

We study sequential decision-making with known rewards and unknown const...

0 David Lindner, et al. ∙

research

∙ 06/04/2022

Active Bayesian Causal Inference

Causal discovery and causal reasoning are classically treated as separat...

7 Christian Toth, et al. ∙

research

∙ 06/03/2022

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

Learning causal structures from observation and experimentation is a cen...

18 Alexander Hägele, et al. ∙

research

∙ 05/26/2022

Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces

Optimal experimental design seeks to determine the most informative allo...

0 Mojmir Mutny, et al. ∙

research

∙ 05/25/2022

Amortized Inference for Causal Structure Learning

Learning causal structure poses a combinatorial search problem that typi...

0 Lars Lorch, et al. ∙

research

∙ 04/04/2022

Multi-Scale Representation Learning on Proteins

Proteins are fundamental biological entities mediating key roles in cell...

0 Vignesh Ram Somnath, et al. ∙

research

∙ 03/26/2022

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

Tuning machine parameters of particle accelerators is a repetitive and t...

0 Johannes Kirschner, et al. ∙

research

∙ 03/14/2022

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

We consider model-based multi-agent reinforcement learning, where the en...

0 Pier Giuseppe Sessa, et al. ∙

research

∙ 02/11/2022

Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges

We propose a new framework to reconstruct a stochastic process {ℙ_t: t ∈...

0 Charlotte Bunne, et al. ∙

research

∙ 02/01/2022

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Obtaining reliable, adaptive confidence sets for prediction functions (h...

0 Parnian Kassraie, et al. ∙

research

∙ 01/24/2022

Constrained Policy Optimization via Bayesian World Models

Improving sample-efficiency and safety are crucial challenges when deplo...

0 Yarden As, et al. ∙

Andreas Krause

Featured Co-authors