Andreas Krause

Apertus: Democratizing Open and Compliant LLMs for Global Language Environments ACL 2026 Composing Unbalanced Flows for Flexible Docking and Relaxation ICLR 2025 Residual Deep Gaussian Processes on Manifolds ICLR 2025 Standardizing Structural Causal Models ICLR 2025 Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs ICLR 2025 MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization ICLR 2025 All models are wrong, some are useful: Model Selection with Limited Labels AISTATS 2025 Generative Intervention Models for Causal Perturbation Modeling ICML 2025 Provable Maximum Entropy Manifold Exploration via Diffusion Models ICML 2025 Learning Safety Constraints for Large Language Models ICML 2025 Active Fine-Tuning of Multi-Task Policies ICML 2025 LITE: Efficiently Estimating Gaussian Probability of Maximality AISTATS 2025 ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning ICLR 2025 Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design ICLR 2025 Causal Modeling with Stationary Diffusions AISTATS 2024 Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm AISTATS 2024 Learning Safety Constraints from Demonstrations with Unknown Rewards AISTATS 2024 Intrinsic Gaussian Vector Fields on Manifolds AISTATS 2024 Distributionally Robust Model-based Reinforcement Learning with Large State Spaces AISTATS 2024 Data Summarization via Bilevel Optimization JMLR 2024 Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning JMLR 2024 Contextual Bilevel Reinforcement Learning for Incentive Alignment NIPS 2024 Transductive Active Learning: Theory and Applications NIPS 2024 Transition Constrained Bayesian Optimization via Markov Decision Processes NIPS 2024 NeoRL: Efficient Exploration for Nonepisodic RL NIPS 2024 When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL NIPS 2024 Bandits with Preference Feedback: A Stackelberg Game Perspective NIPS 2024 Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL ICML 2024 Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods ICML 2024 Submodular Reinforcement Learning ICLR 2024 Adversarial Causal Bayesian Optimization ICLR 2024 Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction ICML 2024 A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks UAI 2023 Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications JMLR 2023 Replicable Bandits ICLR 2023 Near-optimal Policy Identification in Active Reinforcement Learning ICLR 2023 Model-based Causal Bayesian Optimization ICLR 2023 MARS: Meta-learning as Score Matching in the Function Space ICLR 2023 Aligned Diffusion Schrödinger Bridges UAI 2023 Lifelong bandit optimization: no prior and no regret UAI 2023 Hallucinated adversarial control for conservative offline policy evaluation UAI 2023 Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning NIPS 2023 Likelihood Ratio Confidence Sets for Sequential Decision Making NIPS 2023 Anytime Model Selection in Linear Bandits NIPS 2023 Riemannian stochastic optimization methods avoid strict saddle points NIPS 2023 Learning To Dive In Branch And Bound NIPS 2023 Optimistic Active Exploration of Dynamical Systems NIPS 2023 A Dynamical System View of Langevin-Based Non-Convex Sampling NIPS 2023 Efficient Exploration in Continuous-time Model-based Reinforcement Learning NIPS 2023 Stochastic Approximation Algorithms for Systems of Interacting Particles NIPS 2023 Implicit Manifold Gaussian Process Regression NIPS 2023 Contextual Stochastic Bilevel Optimization NIPS 2023 Tuning Legged Locomotion Controllers via Safe Bayesian Optimization CORL 2023 Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice JMLR 2023 Instance-Dependent Generalization Bounds via Optimal Transport JMLR 2023 BaCaDI: Bayesian Causal Discovery with Unknown Interventions AISTATS 2023 The Schrödinger Bridge between Gaussian Measures has a Closed Form AISTATS 2023 Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023 Active Exploration via Experiment Design in Markov Chains AISTATS 2023 Meta-Learning Priors for Safe Bayesian Optimization CORL 2022 Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking ICLR 2022 Neural Contextual Bandits without Regret AISTATS 2022 Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning ICLR 2022 Graph Neural Network Bandits NIPS 2022 The Dynamics of Riemannian Robbins-Monro Algorithms COLT 2022 Active Bayesian Causal Inference NIPS 2022 Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces NIPS 2022 Interactively Learning Preference Constraints in Linear Bandits ICML 2022 Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning ICML 2022 Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation ICML 2022 Amortized Inference for Causal Structure Learning NIPS 2022 Meta-Learning Hypothesis Spaces for Sequential Decision-making ICML 2022 A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits NIPS 2022 Learning Long-Term Crop Management Strategies with CyclesGym NIPS 2022 Constrained Policy Optimization via Bayesian World Models ICLR 2022 Movement Penalized Bayesian Optimization with Application to Wind Energy Systems NIPS 2022 Supervised Training of Conditional Monge Maps NIPS 2022 Active Exploration for Inverse Reinforcement Learning NIPS 2022 Near-Optimal Multi-Agent Learning for Safe Coverage Control NIPS 2022 Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes AISTATS 2022 Sensing Cox Processes via Posterior Sampling and Positive Bases AISTATS 2022 Proximal Optimal Transport Modeling of Population Dynamics AISTATS 2022 Automatic Termination for Hyperparameter Optimization AUTOML 2022 Adaptive Gaussian Process Change Point Detection ICML 2022 Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems NIPS 2021 Risk-Averse Offline Reinforcement Learning ICLR 2021 Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation ICCV 2021 Addressing the Long-term Impact of ML Decisions via Policy Regret IJCAI 2021 Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback ALT 2021 Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases AAAI 2021 Fast Projection Onto Convex Smooth Constraints ICML 2021 PopSkipJump: Decision-Based Attack for Probabilistic Classifiers ICML 2021 Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems ICML 2021 PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees ICML 2021 No-regret Algorithms for Capturing Events in Poisson Point Processes ICML 2021 Bias-Robust Bayesian Optimization via Dueling Bandits ICML 2021 Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning ICML 2021 Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory L4DC 2021 Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator ICLR 2021 Online Active Model Selection for Pre-trained Classifiers AISTATS 2021 Stochastic Linear Bandits Robust to Adversarial Attacks AISTATS 2021 Logistic Q-Learning AISTATS 2021 Meta-Learning Reliable Priors in the Function Space NIPS 2021 Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning NIPS 2021 Misspecified Gaussian Process Bandit Optimization NIPS 2021 Information Directed Reward Learning for Reinforcement Learning NIPS 2021 Regret Bounds for Gaussian-Process Optimization in Large Domains NIPS 2021 Learning Graph Models for Retrosynthesis Prediction NIPS 2021 Hierarchical Skills for Efficient Exploration NIPS 2021 Risk-averse Heteroscedastic Bayesian Optimization NIPS 2021 DiBS: Differentiable Bayesian Structure Learning NIPS 2021 Multi-Scale Representation Learning on Proteins NIPS 2021 Robust Generalization despite Distribution Shift via Minimum Discriminating Information NIPS 2021 Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models NIPS 2021 Information Directed Sampling for Linear Partial Monitoring COLT 2020 Contextual Games: Multi-Agent Learning with Side Information NIPS 2020 Safe non-smooth black-box optimization with application to policy search L4DC 2020 Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models L4DC 2020 Coresets via Bilevel Optimization for Continual Learning and Streaming NIPS 2020 From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models ICML 2020 Experimental Design for Optimization of Orthogonal Projection Pursuit Models AAAI 2020 Multi-Player Bandits: The Adversarial Case JMLR 2020 Corruption-Tolerant Gaussian Process Bandit Optimization AISTATS 2020 Distributionally Robust Bayesian Optimization AISTATS 2020 Mixed Strategies for Robust Optimization of Unknown Objectives AISTATS 2020 Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020 Safe Reinforcement Learning via Curriculum Induction NIPS 2020 Mixed-Variable Bayesian Optimization IJCAI 2020 Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning NIPS 2020 ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems AAAI 2020 Adaptive Sampling for Stochastic Risk-Averse Learning NIPS 2020 Gradient Estimation with Stochastic Softmax Tricks NIPS 2020 Learning to Play Sequential Games versus Unknown Opponents NIPS 2020 AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 2019 Teaching Multiple Concepts to a Forgetful Learner NIPS 2019 Adaptive Sequence Submodularity NIPS 2019 Safe Exploration for Interactive Machine Learning NIPS 2019 No-Regret Learning in Unknown Games with Correlated Payoffs NIPS 2019 A Domain Agnostic Measure for Monitoring and Evaluating GANs NIPS 2019 Stochastic Bandits with Context Distributions NIPS 2019 Efficiently Learning Fourier Sparse Set Functions NIPS 2019 Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs AISTATS 2019 Projection Free Online Learning over Smooth Sets AISTATS 2019 Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature AISTATS 2019 Safe Convex Learning under Uncertain Constraints AISTATS 2019 Consistent Online Optimization: Convex and Submodular AISTATS 2019 Information-Directed Exploration for Deep Reinforcement Learning ICLR 2019 Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference ICML 2019 Online Variance Reduction with Mixtures ICML 2019 Learning Generative Models across Incomparable Spaces ICML 2019 Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces ICML 2019 Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning IJCAI 2019 No-Regret Bayesian Optimization with Unknown Hyperparameters JMLR 2019 Differentiable Submodular Maximization IJCAI 2018 Preventing Disparate Treatment in Sequential Decision Making IJCAI 2018 Provable Variational Inference for Constrained Log-Submodular Models NIPS 2018 Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features NIPS 2018 Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making NIPS 2018 Information Directed Sampling and Bandits with Heteroscedastic Noise COLT 2018 Online Variance Reduction for Stochastic Optimization COLT 2018 The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems CORL 2018 Submodularity on Hypergraphs: From Sets to Sequences AISTATS 2018 An Online Learning Approach to Generative Adversarial Networks ICLR 2018 Training Gaussian Mixture Models at Scale via Coresets JMLR 2018 Continuous DR-submodular Maximization: Structure and Algorithms NIPS 2017 Uniform Deviation Bounds for k-Means Clustering ICML 2017 Distributed and Provably Good Seedings for k-Means in Constant Rounds ICML 2017 Guarantees for Greedy Maximization of Non-submodular Functions with Applications ICML 2017 Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten” ICML 2017 Differentially Private Submodular Maximization: Data Summarization in Disguise ICML 2017 Probabilistic Submodular Maximization in Sub-Linear Time ICML 2017 Near-optimal Bayesian Active Learning with Correlated and Noisy Tests AISTATS 2017 Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains AISTATS 2017 Interactive Submodular Bandit NIPS 2017 Stochastic Submodular Maximization: The Case of Coverage Functions NIPS 2017 Safe Model-based Reinforcement Learning with Stability Guarantees NIPS 2017 Differentiable Learning of Submodular Models NIPS 2017 Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation AISTATS 2016 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016 Actively Learning Hemimetrics with Applications to Eliciting User Preferences ICML 2016 Linear-Time Outlier Detection via Sensitivity IJCAI 2016 e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem JMLR 2016 Distributed Submodular Maximization JMLR 2016 Variational Inference in Mixed Probabilistic Submodular Models NIPS 2016 Cooperative Graphical Models NIPS 2016 Fast and Provably Good Seedings for k-Means NIPS 2016 Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation NIPS 2016 Safe Exploration in Finite Markov Decision Processes with Gaussian Processes NIPS 2016 Horizontally Scalable Submodular Maximization ICML 2016 Learning Sparse Additive Models with Interactions in High Dimensions AISTATS 2016 Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization ICML 2016 Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning AISTATS 2015 Sequential Information Maximization: When is Greedy Near-optimal? COLT 2015 Sampling from Probabilistic Submodular Models NIPS 2015 Higher-Order Inference for Multi-Class Log-Supermodular Models ICCV 2015 Scalable Variational Inference in Log-supermodular Models ICML 2015 Safe Exploration for Optimization with Gaussian Processes ICML 2015 Coresets for Nonparametric Estimation - the Case of DP-Means ICML 2015 Non-Monotone Adaptive Submodular Maximization IJCAI 2015 Information Gathering in Networks via Active Exploration IJCAI 2015 Distributed Submodular Cover: Succinctly Summarizing Massive Data NIPS 2015 Building Hierarchies of Concepts via Crowdsourcing IJCAI 2015 Efficient Partial Monitoring with Prior Information NIPS 2014 Active Detection via Adaptive Submodularity ICML 2014 Near-Optimally Teaching the Crowd to Classify ICML 2014 Near Optimal Bayesian Active Learning for Decision Making AISTATS 2014 Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization JMLR 2014 From MAP to Marginals: Variational Inference in Bayesian Submodular Models NIPS 2014 Efficient Sampling for Learning Sparse Additive Models in High Dimensions NIPS 2014 Active Learning for Multi-Objective Optimization ICML 2013 Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization ICML 2013 High-Dimensional Gaussian Process Bandits NIPS 2013 Active Learning for Level Set Estimation IJCAI 2013 Distributed Submodular Maximization: Identifying Representative Elements in Massive Data NIPS 2013 Learning Fourier Sparse Set Functions AISTATS 2012 Scalable Training of Mixture Models via Coresets NIPS 2011 Crowdclustering NIPS 2011 Contextual Gaussian Process Bandit Optimization NIPS 2011 Near-Optimal Bayesian Active Learning with Noisy Observations NIPS 2010 SFO: A Toolbox for Submodular Function Optimization JMLR 2010 Discriminative Clustering by Regularized Information Maximization NIPS 2010 Efficient Minimization of Decomposable Submodular Functions NIPS 2010 Online Learning of Assignments NIPS 2009 Robust Submodular Observation Selection JMLR 2008 Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies JMLR 2008 Selecting Observations against Adversarial Objectives NIPS 2007