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Andreas Krause 0001
Person information
- affiliation: ETH Zurich, Switzerland
- affiliation: California Institute of Technology, Pasadena, CA, USA
- affiliation: Carnegie Mellon University, Pittsburgh, PA, USA
- affiliation: Technical University of Munich, Germany
Other persons with the same name
- Andreas Krause — disambiguation page
- Andreas Krause 0002 — University of Bath, School of Management, UK
- Andreas Krause 0003 — European Research Center for Information Systems, Münster, Germany
- Andreas Krause 0004 — Immanuel Hospital Berlin, Germany (and 1 more)
- Andreas Krause 0005 — IPH Hannover, Germany
- Andreas Krause 0006 — Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland (and 4 more)
- Andreas Krause 0007 — Philips Semiconductors, Hamburg, Germany
- Andreas Krause 0008 — University of Göttingen, Germany
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2020 – today
- 2025
- [j45]Yunke Ao, Hooman Esfandiari, Fabio Carrillo, Christoph J. Laux, Yarden As, Ruixuan Li, Kaat Van Assche, Ayoob Davoodi, Nicola Alessandro Cavalcanti, Mazda Farshad, Benjamin F. Grewe, Emmanuel B. Vander Poorten, Andreas Krause, Philipp Fürnstahl:
SafeRPlan: Safe deep reinforcement learning for intraoperative planning of pedicle screw placement. Medical Image Anal. 99: 103345 (2025) - 2024
- [j44]Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminboeuf:
3DReact: Geometric Deep Learning for Chemical Reactions. J. Chem. Inf. Model. 64(15): 5771-5785 (2024) - [j43]Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause:
Data Summarization via Bilevel Optimization. J. Mach. Learn. Res. 25: 73:1-73:53 (2024) - [j42]Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause:
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning. J. Mach. Learn. Res. 25: 171:1-171:54 (2024) - [j41]Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros, Andreas Krause:
Data-Efficient Task Generalization via Probabilistic Model-Based Meta Reinforcement Learning. IEEE Robotics Autom. Lett. 9(4): 3918-3925 (2024) - [c287]Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic:
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces. AISTATS 2024: 100-108 - [c286]Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy:
Intrinsic Gaussian Vector Fields on Manifolds. AISTATS 2024: 1306-1314 - [c285]Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. AISTATS 2024: 1927-1935 - [c284]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. AISTATS 2024: 2386-2394 - [c283]Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause:
Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm. AISTATS 2024: 4186-4194 - [c282]Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause:
Provably Learning Nash Policies in Constrained Markov Potential Games. AAMAS 2024: 31-39 - [c281]Matej Jusup, Barna Pásztor, Tadeusz Janik, Kenan Zhang, Francesco Corman, Andreas Krause, Ilija Bogunovic:
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning. AAMAS 2024: 973-982 - [c280]Manish Prajapat, Mojmir Mutny, Melanie N. Zeilinger, Andreas Krause:
Submodular Reinforcement Learning. ICLR 2024 - [c279]Scott Sussex, Pier Giuseppe Sessa, Anastasia Makarova, Andreas Krause:
Adversarial Causal Bayesian Optimization. ICLR 2024 - [c278]Jiawei Huang, Niao He, Andreas Krause:
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. ICML 2024 - [c277]Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause:
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. ICML 2024 - [c276]Riccardo De Santi, Manish Prajapat, Andreas Krause:
Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. ICML 2024 - [i232]Jiawei Huang, Niao He, Andreas Krause:
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL. CoRR abs/2402.05724 (2024) - [i231]Manish Prajapat, Johannes Köhler, Matteo Turchetta, Andreas Krause, Melanie N. Zeilinger:
Safe Guaranteed Exploration for Non-linear Systems. CoRR abs/2402.06562 (2024) - [i230]Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmír Mutný:
Transition Constrained Bayesian Optimization via Markov Decision Processes. CoRR abs/2402.08406 (2024) - [i229]Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause:
Active Few-Shot Fine-Tuning. CoRR abs/2402.15441 (2024) - [i228]Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause:
Information-based Transductive Active Learning. CoRR abs/2402.15898 (2024) - [i227]Jonas Rothfuss, Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause:
Bridging the Sim-to-Real Gap with Bayesian Inference. CoRR abs/2403.16644 (2024) - [i226]Yarden As, Bhavya Sukhija, Andreas Krause:
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization. CoRR abs/2405.05890 (2024) - [i225]Lenart Treven, Bhavya Sukhija, Yarden As, Florian Dörfler, Andreas Krause:
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL. CoRR abs/2406.01163 (2024) - [i224]Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause:
NeoRL: Efficient Exploration for Nonepisodic RL. CoRR abs/2406.01175 (2024) - [i223]Vinzenz Thoma, Barna Pasztor, Andreas Krause, Giorgia Ramponi, Yifan Hu:
Stochastic Bilevel Optimization with Lower-Level Contextual Markov Decision Processes. CoRR abs/2406.01575 (2024) - [i222]Omar G. Younis, Luca Corinzia, Ioannis N. Athanasiadis, Andreas Krause, Joachim M. Buhmann, Matteo Turchetta:
Breeding Programs Optimization with Reinforcement Learning. CoRR abs/2406.03932 (2024) - [i221]Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause:
Standardizing Structural Causal Models. CoRR abs/2406.11601 (2024) - [i220]Barna Pásztor, Parnian Kassraie, Andreas Krause:
Bandits with Preference Feedback: A Stackelberg Game Perspective. CoRR abs/2406.16745 (2024) - [i219]Riccardo De Santi, Manish Prajapat, Andreas Krause:
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods. CoRR abs/2407.09905 (2024) - [i218]Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause:
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction. CoRR abs/2407.13364 (2024) - [i217]Marco Bagatella, Andreas Krause, Georg Martius:
Directed Exploration in Reinforcement Learning from Linear Temporal Logic. CoRR abs/2408.09495 (2024) - [i216]Manish Prajapat, Amon Lahr, Johannes Köhler, Andreas Krause, Melanie N. Zeilinger:
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework. CoRR abs/2409.08616 (2024) - [i215]Melis Ilayda Bal, Pier Giuseppe Sessa, Mojmir Mutny, Andreas Krause:
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design. CoRR abs/2409.18582 (2024) - 2023
- [j40]Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann:
GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems. Artif. Intell. 320: 103922 (2023) - [j39]Omar G. Younis, Matteo Turchetta, Daniel Ariza Suarez, Steven Yates, Bruno Studer, Ioannis N. Athanasiadis, Andreas Krause, Joachim M. Buhmann, Luca Corinzia:
ChromaX: a fast and scalable breeding program simulator. Bioinform. 39(12) (2023) - [j38]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. J. Mach. Learn. Res. 24: 346:1-346:45 (2023) - [j37]Songyan Hou, Parnian Kassraie, Anastasis Kratsios, Andreas Krause, Jonas Rothfuss:
Instance-Dependent Generalization Bounds via Optimal Transport. J. Mach. Learn. Res. 24: 349:1-349:51 (2023) - [j36]Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause:
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. J. Mach. Learn. Res. 24: 386:1-386:62 (2023) - [j35]Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David M. Pennock, Andreas Krause:
Incentive-Compatible Forecasting Competitions. Manag. Sci. 69(3): 1354-1374 (2023) - [j34]Felix Berkenkamp, Andreas Krause, Angela P. Schoellig:
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics. Mach. Learn. 112(10): 3713-3747 (2023) - [j33]Christopher König, Miks Ozols, Anastasia Makarova, Efe C. Balta, Andreas Krause, Alisa Rupenyan:
Safe Risk-Averse Bayesian Optimization for Controller Tuning. IEEE Robotics Autom. Lett. 8(12): 8208-8215 (2023) - [j32]Jonas Gehring, Deepak Gopinath, Jungdam Won, Andreas Krause, Gabriel Synnaeve, Nicolas Usunier:
Leveraging Demonstrations with Latent Space Priors. Trans. Mach. Learn. Res. 2023 (2023) - [j31]Barna Pásztor, Andreas Krause, Ilija Bogunovic:
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c275]Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. AISTATS 2023: 1411-1436 - [c274]Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause:
Isotropic Gaussian Processes on Finite Spaces of Graphs. AISTATS 2023: 4556-4574 - [c273]Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause:
The Schrödinger Bridge between Gaussian Measures has a Closed Form. AISTATS 2023: 5802-5833 - [c272]Mojmir Mutny, Tadeusz Janik, Andreas Krause:
Active Exploration via Experiment Design in Markov Chains. AISTATS 2023: 7349-7374 - [c271]Daniel Widmer, Dongho Kang, Bhavya Sukhija, Jonas Hübotter, Andreas Krause, Stelian Coros:
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization. CoRL 2023: 2444-2464 - [c270]Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas:
Replicable Bandits. ICLR 2023 - [c269]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. ICLR 2023 - [c268]Krunoslav Lehman Pavasovic, Jonas Rothfuss, Andreas Krause:
MARS: Meta-learning as Score Matching in the Function Space. ICLR 2023 - [c267]Scott Sussex, Anastasia Makarova, Andreas Krause:
Model-based Causal Bayesian Optimization. ICLR 2023 - [c266]Bhavya Sukhija, Nathanael Köhler, Miguel Zamora, Simon Zimmermann, Sebastian Curi, Andreas Krause, Stelian Coros:
Gradient-Based Trajectory Optimization With Learned Dynamics. ICRA 2023: 1011-1018 - [c265]Nicolas Emmenegger, Mojmir Mutny, Andreas Krause:
Likelihood Ratio Confidence Sets for Sequential Decision Making. NeurIPS 2023 - [c264]Bernardo Fichera, Slava Borovitskiy, Andreas Krause, Aude Gemma Billard:
Implicit Manifold Gaussian Process Regression. NeurIPS 2023 - [c263]Ya-Ping Hsieh, Mohammad Reza Karimi Jaghargh, Andreas Krause, Panayotis Mertikopoulos:
Riemannian stochastic optimization methods avoid strict saddle points. NeurIPS 2023 - [c262]Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn:
Contextual Stochastic Bilevel Optimization. NeurIPS 2023 - [c261]Mohammad Reza Karimi Jaghargh, Ya-Ping Hsieh, Andreas Krause:
A Dynamical System View of Langevin-Based Non-Convex Sampling. NeurIPS 2023 - [c260]Mohammad Reza Karimi Jaghargh, Ya-Ping Hsieh, Andreas Krause:
Stochastic Approximation Algorithms for Systems of Interacting Particles. NeurIPS 2023 - [c259]Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano:
Anytime Model Selection in Linear Bandits. NeurIPS 2023 - [c258]Max B. Paulus, Andreas Krause:
Learning To Dive In Branch And Bound. NeurIPS 2023 - [c257]Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause:
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning. NeurIPS 2023 - [c256]Bhavya Sukhija, Lenart Treven, Cansu Sancaktar, Sebastian Blaes, Stelian Coros, Andreas Krause:
Optimistic Active Exploration of Dynamical Systems. NeurIPS 2023 - [c255]Lenart Treven, Jonas Hübotter, Bhavya Sukhija, Florian Dörfler, Andreas Krause:
Efficient Exploration in Continuous-time Model-based Reinforcement Learning. NeurIPS 2023 - [c254]Ali Gorji, Andisheh Amrollahi, Andreas Krause:
A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks. UAI 2023: 723-733 - [c253]Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause:
Hallucinated adversarial control for conservative offline policy evaluation. UAI 2023: 1774-1784 - [c252]Felix Schur, Parnian Kassraie, Jonas Rothfuss, Andreas Krause:
Lifelong bandit optimization: no prior and no regret. UAI 2023: 1847-1857 - [c251]Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause, Charlotte Bunne:
Aligned Diffusion Schrödinger Bridges. UAI 2023: 1985-1995 - [e2]Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett:
International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research 202, PMLR 2023 [contents] - [i214]Max B. Paulus, Andreas Krause:
Learning To Dive In Branch And Bound. CoRR abs/2301.09943 (2023) - [i213]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications. CoRR abs/2302.03683 (2023) - [i212]Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, María Rodríguez Martínez, Andreas Krause, Charlotte Bunne:
Aligned Diffusion Schrödinger Bridges. CoRR abs/2302.11419 (2023) - [i211]Jonas Rothfuss, Bhavya Sukhija, Tobias Birchler, Parnian Kassraie, Andreas Krause:
Hallucinated Adversarial Control for Conservative Offline Policy Evaluation. CoRR abs/2303.01076 (2023) - [i210]Yunke Ao, Hooman Esfandiari, Fabio Carrillo, Yarden As, Mazda Farshad, Benjamin F. Grewe, Andreas Krause, Philipp Fürnstahl:
Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement. CoRR abs/2305.05354 (2023) - [i209]Ali Gorji, Andisheh Amrollahi, Andreas Krause:
A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks. CoRR abs/2305.09779 (2023) - [i208]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. CoRR abs/2305.16147 (2023) - [i207]Daniel Widmer, Dongho Kang, Bhavya Sukhija, Jonas Hübotter, Andreas Krause, Stelian Coros:
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization. CoRR abs/2306.07092 (2023) - [i206]Pragnya Alatur, Giorgia Ramponi, Niao He, Andreas Krause:
Provably Learning Nash Policies in Constrained Markov Potential Games. CoRR abs/2306.07749 (2023) - [i205]Matteo Pariset, Ya-Ping Hsieh, Charlotte Bunne, Andreas Krause, Valentin De Bortoli:
Unbalanced Diffusion Schrödinger Bridge. CoRR abs/2306.09099 (2023) - [i204]Bhavya Sukhija, Lenart Treven, Cansu Sancaktar, Sebastian Blaes, Stelian Coros, Andreas Krause:
Optimistic Active Exploration of Dynamical Systems. CoRR abs/2306.12371 (2023) - [i203]Christopher König, Miks Ozols, Anastasia Makarova, Efe C. Balta, Andreas Krause, Alisa Rupenyan:
Safe Risk-averse Bayesian Optimization for Controller Tuning. CoRR abs/2306.13479 (2023) - [i202]Matej Jusup, Barna Pásztor, Tadeusz Janik, Kenan Zhang, Francesco Corman, Andreas Krause, Ilija Bogunovic:
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning. CoRR abs/2306.17052 (2023) - [i201]Parnian Kassraie, Aldo Pacchiano, Nicolas Emmenegger, Andreas Krause:
Anytime Model Selection in Linear Bandits. CoRR abs/2307.12897 (2023) - [i200]Manish Prajapat, Mojmír Mutný, Melanie N. Zeilinger, Andreas Krause:
Submodular Reinforcement Learning. CoRR abs/2307.13372 (2023) - [i199]Scott Sussex, Pier Giuseppe Sessa, Anastasiia Makarova, Andreas Krause:
Model-based Causal Bayesian Optimization. CoRR abs/2307.16625 (2023) - [i198]Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause:
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning. CoRR abs/2308.01744 (2023) - [i197]Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic:
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces. CoRR abs/2309.02236 (2023) - [i196]Vignesh Ram Somnath, Pier Giuseppe Sessa, María Rodríguez Martínez, Andreas Krause:
DockGame: Cooperative Games for Multimeric Rigid Protein Docking. CoRR abs/2310.06177 (2023) - [i195]Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. CoRR abs/2310.17405 (2023) - [i194]Yifan Hu, Jie Wang, Yao Xie, Andreas Krause, Daniel Kuhn:
Contextual Stochastic Bilevel Optimization. CoRR abs/2310.18535 (2023) - [i193]Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy:
Intrinsic Gaussian Vector Fields on Manifolds. CoRR abs/2310.18824 (2023) - [i192]Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause, Aude Billard:
Implicit Manifold Gaussian Process Regression. CoRR abs/2310.19390 (2023) - [i191]Lenart Treven, Jonas Hübotter, Bhavya Sukhija, Florian Dörfler, Andreas Krause:
Efficient Exploration in Continuous-time Model-based Reinforcement Learning. CoRR abs/2310.19848 (2023) - [i190]Ya-Ping Hsieh, Mohammad Reza Karimi, Andreas Krause, Panayotis Mertikopoulos:
Riemannian stochastic optimization methods avoid strict saddle points. CoRR abs/2311.02374 (2023) - [i189]Nicolas Emmenegger, Mojmír Mutný, Andreas Krause:
Likelihood Ratio Confidence Sets for Sequential Decision Making. CoRR abs/2311.04402 (2023) - [i188]Arjun Bhardwaj, Jonas Rothfuss, Bhavya Sukhija, Yarden As, Marco Hutter, Stelian Coros, Andreas Krause:
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning. CoRR abs/2311.07558 (2023) - [i187]Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause:
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm. CoRR abs/2311.16706 (2023) - [i186]Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminboeuf:
EquiReact: An equivariant neural network for chemical reactions. CoRR abs/2312.08307 (2023) - 2022
- [c250]Parnian Kassraie, Andreas Krause:
Neural Contextual Bandits without Regret. AISTATS 2022: 240-278 - [c249]Charlotte Bunne, Laetitia Papaxanthos, Andreas Krause, Marco Cuturi:
Proximal Optimal Transport Modeling of Population Dynamics. AISTATS 2022: 6511-6528 - [c248]Mojmir Mutny, Andreas Krause:
Sensing Cox Processes via Posterior Sampling and Positive Bases. AISTATS 2022: 6968-6989 - [c247]Elvis Nava, Mojmir Mutny, Andreas Krause:
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes. AISTATS 2022: 7031-7054 - [c246]Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau:
Automatic Termination for Hyperparameter Optimization. AutoML 2022: 7/1-21 - [c245]Sebastian Curi, Armin Lederer, Sandra Hirche, Andreas Krause:
Safe Reinforcement Learning via Confidence-Based Filters. CDC 2022: 3409-3415 - [c244]Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos, Andreas Krause:
The Dynamics of Riemannian Robbins-Monro Algorithms. COLT 2022: 3503 - [c243]Jonas Rothfuss, Christopher König, Alisa Rupenyan, Andreas Krause:
Meta-Learning Priors for Safe Bayesian Optimization. CoRL 2022: 237-265 - [c242]Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause:
Constrained Policy Optimization via Bayesian World Models. ICLR 2022 - [c241]Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang:
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning. ICLR 2022 - [c240]Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause:
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking. ICLR 2022 - [c239]Edoardo Caldarelli, Philippe Wenk, Stefan Bauer, Andreas Krause:
Adaptive Gaussian Process Change Point Detection. ICML 2022: 2542-2571 - [c238]Parnian Kassraie, Jonas Rothfuss, Andreas Krause:
Meta-Learning Hypothesis Spaces for Sequential Decision-making. ICML 2022: 10802-10824 - [c237]David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. ICML 2022: 13505-13527 - [c236]Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison:
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. ICML 2022: 17584-17600 - [c235]Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause:
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation. ICML 2022: 19580-19597 - [c234]Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett:
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits. NeurIPS 2022