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ORCIDAndreas 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
![[0000-0002-4509-9775]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-4509-9775")
— 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
- 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 - [i230]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) - [i229]Manish Prajapat, Johannes Köhler, Matteo Turchetta, Andreas Krause, Melanie N. Zeilinger:
Safe Guaranteed Exploration for Non-linear Systems. CoRR abs/2402.06562 (2024) - [i228]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) - [i227]Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause:
Active Few-Shot Fine-Tuning. CoRR abs/2402.15441 (2024) - [i226]Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause:
Information-based Transductive Active Learning. CoRR abs/2402.15898 (2024) - [i225]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) - [i224]Yarden As, Bhavya Sukhija, Andreas Krause:
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization. CoRR abs/2405.05890 (2024) - [i223]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) - [i222]Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause:
NeoRL: Efficient Exploration for Nonepisodic RL. CoRR abs/2406.01175 (2024) - [i221]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) - [i220]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) - [i219]Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause:
Standardizing Structural Causal Models. CoRR abs/2406.11601 (2024) - [i218]Barna Pásztor, Parnian Kassraie, Andreas Krause:
Bandits with Preference Feedback: A Stackelberg Game Perspective. CoRR abs/2406.16745 (2024) - [i217]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) - [i216]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) - [i215]Marco Bagatella, Andreas Krause, Georg Martius:
Directed Exploration in Reinforcement Learning from Linear Temporal Logic. CoRR abs/2408.09495 (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 - [c233]Charlotte Bunne, Andreas Krause, Marco Cuturi:
Supervised Training of Conditional Monge Maps. NeurIPS 2022 - [c232]Parnian Kassraie, Andreas Krause, Ilija Bogunovic:
Graph Neural Network Bandits. NeurIPS 2022 - [c231]David Lindner, Andreas Krause, Giorgia Ramponi:
Active Exploration for Inverse Reinforcement Learning. NeurIPS 2022 - [c230]Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf:
Amortized Inference for Causal Structure Learning. NeurIPS 2022 - [c229]Mojmir Mutny, Andreas Krause:
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces. NeurIPS 2022 - [c228]Manish Prajapat, Matteo Turchetta, Melanie N. Zeilinger, Andreas Krause:
Near-Optimal Multi-Agent Learning for Safe Coverage Control. NeurIPS 2022 - [c227]Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Andreas Krause, Ilija Bogunovic:
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems. NeurIPS 2022 - [c226]Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen:
Active Bayesian Causal Inference. NeurIPS 2022 - [c225]