Andreas Krause 0001
292 publications
26 venues
H Index 78
Affiliation
ETH Zurich, SwitzerlandCalifornia Institute of Technology, Pasadena, CA, USA
Carnegie Mellon University, Pittsburgh, PA, USA
Technical University of Munich, Germany
Links
DBLPGoogle Scholar
ORCID
LinkedIn- https://las.inf.ethz.ch/krausea
- https://en.wikipedia.org/wiki/Andreas_Krause_(computer_scientist)
- https://openreview.net/profile?id=~Andreas_Krause1
- https://mathgenealogy.org/id.php?id=134141
- https://d-nb.info/gnd/1195482272
- https://d-nb.info/gnd/1135208301
- https://www.wikidata.org/entity/Q44689609
| Name | Venue | Year | citations |
|---|---|---|---|
Composing Unbalanced Flows for Flexible Docking and Relaxation.
|
ICLR | 2025 | 9 |
| Performance-Driven Constrained Optimal Auto-Tuner for MPC.
|
IEEE Robotics and Automation Letters | 2025 | 6 |
| Learning Safety Constraints for Large Language Models.
|
ICML | 2025 | 11 |
| Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs.
|
ICLR | 2025 | 20 |
| Provable Maximum Entropy Manifold Exploration via Diffusion Models.
|
ICML | 2025 | 8 |
| LITE: Efficiently Estimating Gaussian Probability of Maximality.
|
AISTATS | 2025 | 3 |
| Residual Deep Gaussian Processes on Manifolds.
|
ICLR | 2025 | 0 |
| Active Fine-Tuning of Multi-Task Policies.
|
ICML | 2025 | 0 |
| Generative Intervention Models for Causal Perturbation Modeling.
|
ICML | 2025 | 0 |
| All models are wrong, some are useful: Model Selection with Limited Labels.
|
AISTATS | 2025 | 0 |
| ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning.
|
ICLR | 2025 | 0 |
| Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design.
|
ICLR | 2025 | 0 |
| Standardizing Structural Causal Models.
|
ICLR | 2025 | 0 |
| MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization.
|
ICLR | 2025 | 0 |
| Transition Constrained Bayesian Optimization via Markov Decision Processes.
|
NIPS/NeurIPS | 2024 | 9 |
| NeoRL: Efficient Exploration for Nonepisodic RL.
|
NIPS/NeurIPS | 2024 | 7 |
| When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL.
|
NIPS/NeurIPS | 2024 | 6 |
| Bridging the Sim-to-Real Gap with Bayesian Inference.
|
IROS | 2024 | 10 |
| Bandits with Preference Feedback: A Stackelberg Game Perspective.
|
NIPS/NeurIPS | 2024 | 5 |
| Contextual Bilevel Reinforcement Learning for Incentive Alignment.
|
NIPS/NeurIPS | 2024 | 12 |
| Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction.
|
ICML | 2024 | 5 |
| Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL.
|
ICML | 2024 | 8 |
| Transductive Active Learning: Theory and Applications.
|
NIPS/NeurIPS | 2024 | 18 |
| Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm.
|
AISTATS | 2024 | 11 |
| Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods.
|
ICML | 2024 | 0 |
| Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning.
|
AAMAS | 2024 | 0 |
| Provably Learning Nash Policies in Constrained Markov Potential Games.
|
AAMAS | 2024 | 0 |
| Intrinsic Gaussian Vector Fields on Manifolds.
|
AISTATS | 2024 | 0 |
| Distributionally Robust Model-based Reinforcement Learning with Large State Spaces.
|
AISTATS | 2024 | 0 |
| Causal Modeling with Stationary Diffusions.
|
AISTATS | 2024 | 0 |
| Learning Safety Constraints from Demonstrations with Unknown Rewards.
|
AISTATS | 2024 | 0 |
| Submodular Reinforcement Learning.
|
ICLR | 2024 | 0 |
| Adversarial Causal Bayesian Optimization.
|
ICLR | 2024 | 0 |
| Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning.
|
IEEE Robotics and Automation Letters | 2024 | 0 |
| Data Summarization via Bilevel Optimization.
|
JMLR | 2024 | 0 |
| Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning.
|
JMLR | 2024 | 0 |
| Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning.
|
NIPS/NeurIPS | 2023 | 4 |
| Likelihood Ratio Confidence Sets for Sequential Decision Making.
|
NIPS/NeurIPS | 2023 | 12 |
| Contextual Stochastic Bilevel Optimization.
|
NIPS/NeurIPS | 2023 | 20 |
| Optimistic Active Exploration of Dynamical Systems.
|
NIPS/NeurIPS | 2023 | 31 |
| Efficient Exploration in Continuous-time Model-based Reinforcement Learning.
|
NIPS/NeurIPS | 2023 | 18 |
| Tuning Legged Locomotion Controllers via Safe Bayesian Optimization.
|
CoRL | 2023 | 17 |
| Hallucinated adversarial control for conservative offline policy evaluation.
|
UAI | 2023 | 14 |
| Learning To Dive In Branch And Bound.
|
NIPS/NeurIPS | 2023 | 15 |
| Safe Risk-Averse Bayesian Optimization for Controller Tuning.
|
IEEE Robotics and Automation Letters | 2023 | 11 |
| Aligned Diffusion Schrödinger Bridges.
|
UAI | 2023 | 49 |
| Stochastic Approximation Algorithms for Systems of Interacting Particles.
|
NIPS/NeurIPS | 2023 | 4 |
| A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks.
|
UAI | 2023 | 7 |
| Riemannian stochastic optimization methods avoid strict saddle points.
|
NIPS/NeurIPS | 2023 | 14 |
| Implicit Manifold Gaussian Process Regression.
|
NIPS/NeurIPS | 2023 | 8 |
| Anytime Model Selection in Linear Bandits.
|
NIPS/NeurIPS | 2023 | 7 |
| Gradient-Based Trajectory Optimization With Learned Dynamics.
|
ICRA | 2023 | 0 |
| Lifelong bandit optimization: no prior and no regret.
|
UAI | 2023 | 0 |
| The Schrödinger Bridge between Gaussian Measures has a Closed Form.
|
AISTATS | 2023 | 0 |
| Active Exploration via Experiment Design in Markov Chains.
|
AISTATS | 2023 | 0 |
| BaCaDI: Bayesian Causal Discovery with Unknown Interventions.
|
AISTATS | 2023 | 0 |
| Isotropic Gaussian Processes on Finite Spaces of Graphs.
|
AISTATS | 2023 | 0 |
| A Dynamical System View of Langevin-Based Non-Convex Sampling.
|
NIPS/NeurIPS | 2023 | 0 |
| Replicable Bandits.
|
ICLR | 2023 | 0 |
| Model-based Causal Bayesian Optimization.
|
ICLR | 2023 | 0 |
| Near-optimal Policy Identification in Active Reinforcement Learning.
|
ICLR | 2023 | 0 |
| MARS: Meta-learning as Score Matching in the Function Space.
|
ICLR | 2023 | 0 |
| Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics.
|
MLJ | 2023 | 0 |
| Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice.
|
JMLR | 2023 | 0 |
| Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications.
|
JMLR | 2023 | 0 |
| Instance-Dependent Generalization Bounds via Optimal Transport.
|
JMLR | 2023 | 0 |
| GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems.
|
Artificial Intelligence | 2023 | 0 |
| Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces.
|
NIPS/NeurIPS | 2022 | 12 |
| Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning.
|
ICML | 2022 | 82 |
| Near-Optimal Multi-Agent Learning for Safe Coverage Control.
|
NIPS/NeurIPS | 2022 | 19 |
| Graph Neural Network Bandits.
|
NIPS/NeurIPS | 2022 | 14 |
| Movement Penalized Bayesian Optimization with Application to Wind Energy Systems.
|
NIPS/NeurIPS | 2022 | 14 |
| A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits.
|
NIPS/NeurIPS | 2022 | 11 |
| Learning Long-Term Crop Management Strategies with CyclesGym.
|
NIPS/NeurIPS | 2022 | 19 |
| Supervised Training of Conditional Monge Maps.
|
NIPS/NeurIPS | 2022 | 79 |
| Meta-Learning Priors for Safe Bayesian Optimization.
|
CoRL | 2022 | 32 |
| Active Bayesian Causal Inference.
|
NIPS/NeurIPS | 2022 | 44 |
| Active Exploration for Inverse Reinforcement Learning.
|
NIPS/NeurIPS | 2022 | 34 |
| Adaptive Gaussian Process Change Point Detection.
|
ICML | 2022 | 13 |
| Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation.
|
ICML | 2022 | 22 |
| Meta-Learning Hypothesis Spaces for Sequential Decision-making.
|
ICML | 2022 | 6 |
| Constrained Policy Optimization via Bayesian World Models.
|
ICLR | 2022 | 70 |
| Interactively Learning Preference Constraints in Linear Bandits.
|
ICML | 2022 | 15 |
| Amortized Inference for Causal Structure Learning.
|
NIPS/NeurIPS | 2022 | 88 |
| The Dynamics of Riemannian Robbins-Monro Algorithms.
|
COLT | 2022 | 7 |
| Neural Contextual Bandits without Regret.
|
AISTATS | 2022 | 0 |
| Sensing Cox Processes via Posterior Sampling and Positive Bases.
|
AISTATS | 2022 | 0 |
| Proximal Optimal Transport Modeling of Population Dynamics.
|
AISTATS | 2022 | 0 |
| Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes.
|
AISTATS | 2022 | 0 |
| Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning.
|
ICLR | 2022 | 0 |
| Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
|
ICLR | 2022 | 0 |
| Meta-Learning Reliable Priors in the Function Space.
|
NIPS/NeurIPS | 2021 | 30 |
| Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models.
|
NIPS/NeurIPS | 2021 | 3 |
| Addressing the Long-term Impact of ML Decisions via Policy Regret.
|
IJCAI | 2021 | 8 |
| No-regret Algorithms for Capturing Events in Poisson Point Processes.
|
ICML | 2021 | 10 |
| Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems.
|
ICML | 2021 | 3 |
| Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems.
|
NIPS/NeurIPS | 2021 | 20 |
| Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning.
|
NIPS/NeurIPS | 2021 | 23 |
| Robust Generalization despite Distribution Shift via Minimum Discriminating Information.
|
NIPS/NeurIPS | 2021 | 13 |
| Risk-Averse Offline Reinforcement Learning.
|
ICLR | 2021 | 79 |
| Risk-averse Heteroscedastic Bayesian Optimization.
|
NIPS/NeurIPS | 2021 | 42 |
| Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning.
|
ICML | 2021 | 18 |
| PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.
|
ICML | 2021 | 2 |
| Fast Projection Onto Convex Smooth Constraints.
|
ICML | 2021 | 12 |
| Hierarchical Skills for Efficient Exploration.
|
NIPS/NeurIPS | 2021 | 48 |
| Regret Bounds for Gaussian-Process Optimization in Large Domains.
|
NIPS/NeurIPS | 2021 | 8 |
| Misspecified Gaussian Process Bandit Optimization.
|
NIPS/NeurIPS | 2021 | 58 |
| PopSkipJump: Decision-Based Attack for Probabilistic Classifiers.
|
ICML | 2021 | 3 |
| Information Directed Reward Learning for Reinforcement Learning.
|
NIPS/NeurIPS | 2021 | 25 |
| DiBS: Differentiable Bayesian Structure Learning.
|
NIPS/NeurIPS | 2021 | 115 |
| Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation.
|
ICCV | 2021 | 6 |
| Safe and Efficient Model-free Adaptive Control via Bayesian Optimization.
|
ICRA | 2021 | 36 |
| Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases.
|
AAAI | 2021 | 0 |
| Bias-Robust Bayesian Optimization via Dueling Bandits.
|
ICML | 2021 | 0 |
| Logistic Q-Learning.
|
AISTATS | 2021 | 0 |
| Online Active Model Selection for Pre-trained Classifiers.
|
AISTATS | 2021 | 0 |
| Stochastic Linear Bandits Robust to Adversarial Attacks.
|
AISTATS | 2021 | 0 |
| A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness.
|
AIES | 2021 | 0 |
| Multi-Scale Representation Learning on Proteins.
|
NIPS/NeurIPS | 2021 | 0 |
| Learning Graph Models for Retrosynthesis Prediction.
|
NIPS/NeurIPS | 2021 | 0 |
| Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator.
|
ICLR | 2021 | 0 |
| Mixed Strategies for Robust Optimization of Unknown Objectives.
|
AISTATS | 2020 | 11 |
| Safe Reinforcement Learning via Curriculum Induction.
|
NIPS/NeurIPS | 2020 | 100 |
| Mixed-Variable Bayesian Optimization.
|
IJCAI | 2020 | 59 |
| Experimental Design for Optimization of Orthogonal Projection Pursuit Models.
|
AAAI | 2020 | 7 |
| From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models.
|
ICML | 2020 | 7 |
| A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models.
|
ICRA | 2020 | 6 |
| Learning to Play Sequential Games versus Unknown Opponents.
|
NIPS/NeurIPS | 2020 | 29 |
| Gradient Estimation with Stochastic Softmax Tricks.
|
NIPS/NeurIPS | 2020 | 95 |
| Coresets via Bilevel Optimization for Continual Learning and Streaming.
|
NIPS/NeurIPS | 2020 | 291 |
| Distributionally Robust Bayesian Optimization.
|
AISTATS | 2020 | 91 |
| Corruption-Tolerant Gaussian Process Bandit Optimization.
|
AISTATS | 2020 | 57 |
| Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning.
|
NIPS/NeurIPS | 2020 | 106 |
| Information Directed Sampling for Linear Partial Monitoring.
|
COLT | 2020 | 52 |
| ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems.
|
AAAI | 2020 | 0 |
| Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization.
|
ICRA | 2020 | 0 |
| Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling.
|
AISTATS | 2020 | 0 |
| Adaptive Sampling for Stochastic Risk-Averse Learning.
|
NIPS/NeurIPS | 2020 | 0 |
| Contextual Games: Multi-Agent Learning with Side Information.
|
NIPS/NeurIPS | 2020 | 0 |
| Multi-Player Bandits: The Adversarial Case.
|
JMLR | 2020 | 0 |
| Online Variance Reduction with Mixtures.
|
ICML | 2019 | 15 |
| Projection Free Online Learning over Smooth Sets.
|
AISTATS | 2019 | 36 |
| Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature.
|
AISTATS | 2019 | 10 |
| Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces.
|
ICML | 2019 | 176 |
| Stochastic Bandits with Context Distributions.
|
NIPS/NeurIPS | 2019 | 32 |
| No-Regret Learning in Unknown Games with Correlated Payoffs.
|
NIPS/NeurIPS | 2019 | 44 |
| Safe Convex Learning under Uncertain Constraints.
|
AISTATS | 2019 | 46 |
| Adaptive Sequence Submodularity.
|
NIPS/NeurIPS | 2019 | 28 |
| Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning.
|
IJCAI | 2019 | 80 |
| Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning.
|
KDD | 2019 | 224 |
| Safe Exploration for Interactive Machine Learning.
|
NIPS/NeurIPS | 2019 | 95 |
| Consistent Online Optimization: Convex and Submodular.
|
AISTATS | 2019 | 16 |
| No-Regret Bayesian Optimization with Unknown Hyperparameters.
|
JMLR | 2019 | 85 |
| Mobile Robotic Painting of Texture.
|
ICRA | 2019 | 18 |
| Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference.
|
ICML | 2019 | 34 |
| Efficiently Learning Fourier Sparse Set Functions.
|
NIPS/NeurIPS | 2019 | 17 |
| Learning Generative Models across Incomparable Spaces.
|
ICML | 2019 | 118 |
| AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs.
|
ICML | 2019 | 0 |
| Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs.
|
AISTATS | 2019 | 0 |
| Teaching Multiple Concepts to a Forgetful Learner.
|
NIPS/NeurIPS | 2019 | 0 |
| A Domain Agnostic Measure for Monitoring and Evaluating GANs.
|
NIPS/NeurIPS | 2019 | 0 |
| Online Variance Reduction for Stochastic Optimization.
|
COLT | 2018 | 25 |
| Information Directed Sampling and Bandits with Heteroscedastic Noise.
|
COLT | 2018 | 129 |
| Preventing Disparate Treatment in Sequential Decision Making.
|
IJCAI | 2018 | 48 |
| Provable Variational Inference for Constrained Log-Submodular Models.
|
NIPS/NeurIPS | 2018 | 5 |
| Learning to Interact With Learning Agents.
|
AAAI | 2018 | 12 |
| Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features.
|
NIPS/NeurIPS | 2018 | 193 |
| Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making.
|
NIPS/NeurIPS | 2018 | 144 |
| The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems.
|
CoRL | 2018 | 262 |
| Discrete Sampling using Semigradient-based Product Mixtures.
|
UAI | 2018 | 2 |
| Submodularity on Hypergraphs: From Sets to Sequences.
|
AISTATS | 2018 | 18 |
| Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations.
|
IEEE Robotics and Automation Letters | 2018 | 176 |
| Incentive-Compatible Forecasting Competitions.
|
AAAI | 2018 | 35 |
| Differentiable Submodular Maximization.
|
IJCAI | 2018 | 47 |
| Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly.
|
AAAI | 2018 | 0 |
| Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints.
|
AAAI | 2018 | 0 |
| Learning User Preferences to Incentivize Exploration in the Sharing Economy.
|
AAAI | 2018 | 0 |
| Scalable k -Means Clustering via Lightweight Coresets.
|
KDD | 2018 | 0 |
| Proper Proxy Scoring Rules.
|
AAAI | 2017 | 26 |
| Differentially Private Submodular Maximization: Data Summarization in Disguise.
|
ICML | 2017 | 39 |
| Interactive Submodular Bandit.
|
NIPS/NeurIPS | 2017 | 29 |
| Improving Optimization-Based Approximate Inference by Clamping Variables.
|
UAI | 2017 | 0 |
| Training Gaussian Mixture Models at Scale via Coresets.
|
JMLR | 2017 | 108 |
| Probabilistic Submodular Maximization in Sub-Linear Time.
|
ICML | 2017 | 36 |
| Safe Model-based Reinforcement Learning with Stability Guarantees.
|
NIPS/NeurIPS | 2017 | 956 |
| Selecting Sequences of Items via Submodular Maximization.
|
AAAI | 2017 | 57 |
| Uniform Deviation Bounds for k-Means Clustering.
|
ICML | 2017 | 20 |
| Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting.
|
UAI | 2017 | 14 |
| Guarantees for Greedy Maximization of Non-submodular Functions with Applications.
|
ICML | 2017 | 0 |
| Differentiable Learning of Submodular Functions.
|
NIPS/NeurIPS | 2017 | 3 |
| Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms.
|
NIPS/NeurIPS | 2017 | 18 |
| Distributed and Provably Good Seedings for k-Means in Constant Rounds.
|
ICML | 2017 | 29 |
| Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten".
|
ICML | 2017 | 92 |
| Stochastic Submodular Maximization: The Case of Coverage Functions.
|
NIPS/NeurIPS | 2017 | 61 |
| Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization.
|
ICRA | 2017 | 136 |
| Near-optimal Bayesian Active Learning with Correlated and Noisy Tests.
|
AISTATS | 2017 | 0 |
| Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains.
|
AISTATS | 2017 | 0 |
| Cooperative Graphical Models.
|
NIPS/NeurIPS | 2016 | 1 |
| Better safe than sorry: Risky function exploitation through safe optimization.
|
Cognitive Science | 2016 | 7 |
| Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization.
|
ICML | 2016 | 27 |
| Horizontally Scalable Submodular Maximization.
|
ICML | 2016 | 8 |
| Approximate K-Means++ in Sublinear Time.
|
AAAI | 2016 | 155 |
| Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation.
|
NIPS/NeurIPS | 2016 | 96 |
| Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation.
|
AISTATS | 2016 | 37 |
| Learning Sparse Additive Models with Interactions in High Dimensions.
|
AISTATS | 2016 | 15 |
| e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem.
|
JMLR | 2016 | 108 |
| Distributed Submodular Maximization.
|
JMLR | 2016 | 7 |
| Safe Exploration in Finite Markov Decision Processes with Gaussian Processes.
|
NIPS/NeurIPS | 2016 | 200 |
| Variational Inference in Mixed Probabilistic Submodular Models.
|
NIPS/NeurIPS | 2016 | 23 |
| Linear-Time Outlier Detection via Sensitivity.
|
IJCAI | 2016 | 13 |
| Fast and Provably Good Seedings for k-Means.
|
NIPS/NeurIPS | 2016 | 148 |
| Actively Learning Hemimetrics with Applications to Eliciting User Preferences.
|
ICML | 2016 | 17 |
| Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization.
|
AAAI | 2016 | 0 |
| Safe controller optimization for quadrotors with Gaussian processes.
|
ICRA | 2016 | 0 |
| Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures.
|
AISTATS | 2016 | 0 |
| Sequential Information Maximization: When is Greedy Near-optimal?
|
COLT | 2015 | 78 |
| Higher-Order Inference for Multi-class Log-Supermodular Models.
|
ICCV | 2015 | 18 |
| Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning.
|
AISTATS | 2015 | 16 |
| Building Hierarchies of Concepts via Crowdsourcing.
|
IJCAI | 2015 | 40 |
| Efficient visual exploration and coverage with a micro aerial vehicle in unknown environments.
|
ICRA | 2015 | 129 |
| Safe Exploration for Optimization with Gaussian Processes.
|
ICML | 2015 | 432 |
| Distributed Submodular Cover: Succinctly Summarizing Massive Data.
|
NIPS/NeurIPS | 2015 | 60 |
| Sampling from Probabilistic Submodular Models.
|
NIPS/NeurIPS | 2015 | 36 |
| Scalable Variational Inference in Log-supermodular Models.
|
ICML | 2015 | 29 |
| Discovering Valuable items from Massive Data.
|
KDD | 2015 | 37 |
| Coresets for Nonparametric Estimation - the Case of DP-Means.
|
ICML | 2015 | 110 |
| Incentivizing Users for Balancing Bike Sharing Systems.
|
AAAI | 2015 | 216 |
| Submodular Surrogates for Value of Information.
|
AAAI | 2015 | 53 |
| Information Gathering in Networks via Active Exploration.
|
IJCAI | 2015 | 15 |
| Non-Monotone Adaptive Submodular Maximization.
|
IJCAI | 2015 | 33 |
| Lazier Than Lazy Greedy.
|
AAAI | 2015 | 0 |
| Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation.
|
IJRR | 2015 | 0 |
| Near-Optimally Teaching the Crowd to Classify.
|
ICML | 2014 | 131 |
| Streaming submodular maximization: massive data summarization on the fly.
|
KDD | 2014 | 372 |
| Fully autonomous focused exploration for robotic environmental monitoring.
|
ICRA | 2014 | 79 |
| Active Detection via Adaptive Submodularity.
|
ICML | 2014 | 49 |
| From MAP to Marginals: Variational Inference in Bayesian Submodular Models.
|
NIPS/NeurIPS | 2014 | 80 |
| Near Optimal Bayesian Active Learning for Decision Making.
|
AISTATS | 2014 | 57 |
| Efficient Sampling for Learning Sparse Additive Models in High Dimensions.
|
NIPS/NeurIPS | 2014 | 11 |
| Efficient Partial Monitoring with Prior Information.
|
NIPS/NeurIPS | 2014 | 17 |
| Explore-exploit in top-N recommender systems via Gaussian processes.
|
RecSys | 2014 | 97 |
| Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization.
|
JMLR | 2014 | 0 |
| Active Learning for Level Set Estimation.
|
IJCAI | 2013 | 180 |
| Robust landmark selection for mobile robot navigation.
|
IROS | 2013 | 19 |
| Optimizing waypoints for monitoring spatiotemporal phenomena.
|
IJRR | 2013 | 125 |
| Active Learning for Multi-Objective Optimization.
|
ICML | 2013 | 181 |
| High-Dimensional Gaussian Process Bandits.
|
NIPS/NeurIPS | 2013 | 188 |
| Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization.
|
ICML | 2013 | 156 |
| Robot navigation in dense human crowds: the case for cooperation.
|
ICRA | 2013 | 167 |
| Truthful incentives in crowdsourcing tasks using regret minimization mechanisms.
|
WWW | 2013 | 297 |
| Distributed Submodular Maximization: Identifying Representative Elements in Massive Data.
|
NIPS/NeurIPS | 2013 | 0 |
| Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization.
|
ICML | 2012 | 492 |
| Joint Optimization and Variable Selection of High-dimensional Gaussian Processes.
|
ICML | 2012 | 91 |
| Learning Fourier Sparse Set Functions.
|
AISTATS | 2012 | 55 |
| Dynamic Resource Allocation in Conservation Planning.
|
AAAI | 2011 | 34 |
| Contextual Gaussian Process Bandit Optimization.
|
NIPS/NeurIPS | 2011 | 430 |
| Randomized Sensing in Adversarial Environments.
|
IJCAI | 2011 | 26 |
| Scalable Training of Mixture Models via Coresets.
|
NIPS/NeurIPS | 2011 | 167 |
| Crowdclustering.
|
NIPS/NeurIPS | 2011 | 152 |
| Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization.
|
JAIR | 2011 | 0 |
| Inferring networks of diffusion and influence.
|
KDD | 2010 | 1155 |
| Discriminative Clustering by Regularized Information Maximization.
|
NIPS/NeurIPS | 2010 | 392 |
| A Utility-Theoretic Approach to Privacy in Online Services.
|
JAIR | 2010 | 119 |
| Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization.
|
COLT | 2010 | 134 |
| Near-Optimal Bayesian Active Learning with Noisy Observations.
|
NIPS/NeurIPS | 2010 | 208 |
| Submodular Dictionary Selection for Sparse Representation.
|
ICML | 2010 | 139 |
| Unfreezing the robot: Navigation in dense, interacting crowds.
|
IROS | 2010 | 663 |
| Budgeted Nonparametric Learning from Data Streams.
|
ICML | 2010 | 130 |
| SFO: A Toolbox for Submodular Function Optimization.
|
JMLR | 2010 | 1 |
| Informative path planning for an autonomous underwater vehicle.
|
ICRA | 2010 | 157 |
| Efficient Minimization of Decomposable Submodular Functions.
|
NIPS/NeurIPS | 2010 | 129 |
| Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.
|
ICML | 2010 | 0 |
| Optimal Value of Information in Graphical Models.
|
JAIR | 2009 | 127 |
| Nonmyopic Adaptive Informative Path Planning for Multiple Robots.
|
IJCAI | 2009 | 144 |
| Online Learning of Assignments.
|
NIPS/NeurIPS | 2009 | 86 |
| Efficient Informative Sensing using Multiple Robots.
|
JAIR | 2009 | 0 |
| Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies.
|
JMLR | 2008 | 465 |
| A Utility-Theoretic Approach to Privacy and Personalization.
|
AAAI | 2008 | 78 |
| Near-optimal Observation Selection using Submodular Functions.
|
AAAI | 2007 | 376 |
| Nonmyopic Informative Path Planning in Spatio-Temporal Models.
|
AAAI | 2007 | 106 |
| Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach.
|
ICML | 2007 | 259 |
| Cost-effective outbreak detection in networks.
|
KDD | 2007 | 2716 |
| Robust, low-cost, non-intrusive sensing and recognition of seated postures.
|
UIST | 2007 | 139 |
| Selecting Observations against Adversarial Objectives.
|
NIPS/NeurIPS | 2007 | 48 |
| Efficient Planning of Informative Paths for Multiple Robots.
|
IJCAI | 2007 | 0 |
| Data association for topic intensity tracking.
|
ICML | 2006 | 66 |
| Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing.
|
ISWC | 2005 | 159 |
| Near-optimal sensor placements in Gaussian processes.
|
ICML | 2005 | 1924 |
| Near-optimal Nonmyopic Value of Information in Graphical Models.
|
UAI | 2005 | 476 |
| Optimal Nonmyopic Value of Information in Graphical Models - Efficient Algorithms and Theoretical Limits.
|
IJCAI | 2005 | 82 |
| Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing.
|
ISWC | 2003 | 186 |
| SenSay: A Context-Aware Mobile Phone.
|
ISWC | 2003 | 372 |