← Authors

Andreas Krause

225 papers · 2007–2025 · 14 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (51) 🌍 Conference Polyglot (14) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (18) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (8) 🏠 Conference Loyalist (82) 🌟 Keyword Trendsetter Combo (3) πŸ‘‘ Domain Dominant (130) 🀝 Dynamic Duo (18) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion (5) πŸ† Grand Slam 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (219) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (19) πŸš€ Conference Pioneer ⚑ Prolific Year (27) πŸ’Ž Century Club (225) ❓ The Questioner (2)

Conferences

NIPS (82) ICML (41) AISTATS (36) ICLR (20) JMLR (14) IJCAI (10) COLT (5) UAI (4) AAAI (3) CORL (3) L4DC (3) ICCV (2) ALT (1) AUTOML (1)

Top co-authors

Ilija Bogunovic (18) Mojmir Mutny (17) Amin Karbasi (15) Jonas Rothfuss (12) Pier Giuseppe Sessa (12) Johannes Kirschner (11) Maryam Kamgarpour (11) Mario Lucic (10) Sebastian Curi (10) Parnian Kassraie (9)

Research topics

Optimization (2) Methods (1) Applications (1) Differential Privacy (1)

Keywords

gaussian process (37) regret bound (27) bayesian optimization (25) submodular optimization (22) online learning (16) submodular function (15) active learning (15) variational inference (13) bayesian inference (11) submodular maximization (10) kernel methods (10) neural network (10) convex optimization (9) stochastic optimization (8) model-based reinforcement learning (8) greedy algorithm (8) data summarization (8) sample complexity (7) sequential decision making (7) multi-armed bandit (7)

Papers

Learning Safety Constraints for Large Language Models ICML 2025
Active Fine-Tuning of Multi-Task Policies ICML 2025
Residual Deep Gaussian Processes on Manifolds ICLR 2025
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning ICLR 2025
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs ICLR 2025
All models are wrong, some are useful: Model Selection with Limited Labels AISTATS 2025
Composing Unbalanced Flows for Flexible Docking and Relaxation ICLR 2025
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization ICLR 2025
Generative Intervention Models for Causal Perturbation Modeling ICML 2025
Provable Maximum Entropy Manifold Exploration via Diffusion Models ICML 2025
Standardizing Structural Causal Models ICLR 2025
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design ICLR 2025
LITE: Efficiently Estimating Gaussian Probability of Maximality AISTATS 2025
Causal Modeling with Stationary Diffusions 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
Learning Safety Constraints from Demonstrations with Unknown Rewards AISTATS 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
Submodular Reinforcement Learning ICLR 2024
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm AISTATS 2024
Adversarial Causal Bayesian Optimization ICLR 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
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 SchrΓΆdinger 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 SchrΓΆdinger 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
Amortized Inference for Causal Structure Learning NIPS 2022
Near-Optimal Multi-Agent Learning for Safe Coverage Control NIPS 2022
Active Bayesian Causal Inference NIPS 2022
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces NIPS 2022
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits NIPS 2022
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems NIPS 2022
Graph Neural Network Bandits NIPS 2022
The Dynamics of Riemannian Robbins-Monro Algorithms COLT 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
Constrained Policy Optimization via Bayesian World Models ICLR 2022
Automatic Termination for Hyperparameter Optimization AUTOML 2022
Meta-Learning Priors for Safe Bayesian Optimization CORL 2022
Adaptive Gaussian Process Change Point Detection ICML 2022
Meta-Learning Hypothesis Spaces for Sequential Decision-making ICML 2022
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking ICLR 2022
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning ICLR 2022
Neural Contextual Bandits without Regret AISTATS 2022
Proximal Optimal Transport Modeling of Population Dynamics AISTATS 2022
Sensing Cox Processes via Posterior Sampling and Positive Bases AISTATS 2022
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes AISTATS 2022
Active Exploration for Inverse Reinforcement Learning NIPS 2022
Supervised Training of Conditional Monge Maps NIPS 2022
Learning Long-Term Crop Management Strategies with CyclesGym NIPS 2022
Meta-Learning Reliable Priors in the Function Space NIPS 2021
Regret Bounds for Gaussian-Process Optimization in Large Domains NIPS 2021
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback ALT 2021
Fast Projection Onto Convex Smooth Constraints ICML 2021
Learning Graph Models for Retrosynthesis Prediction NIPS 2021
Hierarchical Skills for Efficient Exploration NIPS 2021
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems NIPS 2021
Risk-averse Heteroscedastic Bayesian Optimization NIPS 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
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
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator ICLR 2021
Risk-Averse Offline Reinforcement Learning ICLR 2021
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers ICML 2021
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning NIPS 2021
Addressing the Long-term Impact of ML Decisions via Policy Regret IJCAI 2021
Misspecified Gaussian Process Bandit Optimization NIPS 2021
Information Directed Reward Learning for Reinforcement Learning NIPS 2021
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases AAAI 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
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation ICCV 2021
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems AAAI 2020
Experimental Design for Optimization of Orthogonal Projection Pursuit Models AAAI 2020
Mixed Strategies for Robust Optimization of Unknown Objectives AISTATS 2020
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling AISTATS 2020
Contextual Games: Multi-Agent Learning with Side Information NIPS 2020
Learning to Play Sequential Games versus Unknown Opponents NIPS 2020
Gradient Estimation with Stochastic Softmax Tricks NIPS 2020
Mixed-Variable Bayesian Optimization IJCAI 2020
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models ICML 2020
Adaptive Sampling for Stochastic Risk-Averse Learning NIPS 2020
Multi-Player Bandits: The Adversarial Case JMLR 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
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning NIPS 2020
Information Directed Sampling for Linear Partial Monitoring COLT 2020
Coresets via Bilevel Optimization for Continual Learning and Streaming NIPS 2020
Safe Reinforcement Learning via Curriculum Induction NIPS 2020
Corruption-Tolerant Gaussian Process Bandit Optimization AISTATS 2020
Distributionally Robust Bayesian Optimization AISTATS 2020
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
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 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
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making NIPS 2018
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features NIPS 2018
Provable Variational Inference for Constrained Log-Submodular Models NIPS 2018
Differentiable Submodular Maximization IJCAI 2018
An Online Learning Approach to Generative Adversarial Networks ICLR 2018
Training Gaussian Mixture Models at Scale via Coresets JMLR 2018
Online Variance Reduction for Stochastic Optimization COLT 2018
Preventing Disparate Treatment in Sequential Decision Making IJCAI 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
Information Directed Sampling and Bandits with Heteroscedastic Noise COLT 2018
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests AISTATS 2017
Distributed and Provably Good Seedings for k-Means in Constant Rounds ICML 2017
Uniform Deviation Bounds for k-Means Clustering ICML 2017
Guarantees for Greedy Maximization of Non-submodular Functions with Applications ICML 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
Continuous DR-submodular Maximization: Structure and Algorithms NIPS 2017
Differentiable Learning of Submodular Models NIPS 2017
Deletion-Robust Submodular Maximization: Data Summarization with β€œthe 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
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains AISTATS 2017
Distributed Submodular Maximization JMLR 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
Variational Inference in Mixed Probabilistic Submodular Models NIPS 2016
Actively Learning Hemimetrics with Applications to Eliciting User Preferences ICML 2016
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization ICML 2016
Horizontally Scalable Submodular Maximization ICML 2016
Fast and Provably Good Seedings for k-Means NIPS 2016
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem JMLR 2016
Cooperative Graphical Models NIPS 2016
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation AISTATS 2016
Learning Sparse Additive Models with Interactions in High Dimensions AISTATS 2016
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016
Linear-Time Outlier Detection via Sensitivity IJCAI 2016
Non-Monotone Adaptive Submodular Maximization IJCAI 2015
Coresets for Nonparametric Estimation - the Case of DP-Means ICML 2015
Safe Exploration for Optimization with Gaussian Processes ICML 2015
Scalable Variational Inference in Log-supermodular Models ICML 2015
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning AISTATS 2015
Building Hierarchies of Concepts via Crowdsourcing IJCAI 2015
Information Gathering in Networks via Active Exploration IJCAI 2015
Distributed Submodular Cover: Succinctly Summarizing Massive Data NIPS 2015
Sampling from Probabilistic Submodular Models NIPS 2015
Higher-Order Inference for Multi-Class Log-Supermodular Models ICCV 2015
Sequential Information Maximization: When is Greedy Near-optimal? COLT 2015
Near Optimal Bayesian Active Learning for Decision Making AISTATS 2014
Active Detection via Adaptive Submodularity ICML 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
Efficient Partial Monitoring with Prior Information NIPS 2014
Near-Optimally Teaching the Crowd to Classify ICML 2014
High-Dimensional Gaussian Process Bandits NIPS 2013
Active Learning for Multi-Objective Optimization ICML 2013
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization ICML 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
Discriminative Clustering by Regularized Information Maximization NIPS 2010
SFO: A Toolbox for Submodular Function Optimization JMLR 2010
Efficient Minimization of Decomposable Submodular Functions NIPS 2010
Online Learning of Assignments NIPS 2009
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies JMLR 2008
Robust Submodular Observation Selection JMLR 2008
Selecting Observations against Adversarial Objectives NIPS 2007