Timezone: »
Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we avoid the discretization schemes of classical approaches. This leads to significant improvements in parameter accuracy and robustness given random initial guesses. On four commonly used benchmark systems, we demonstrate the performance of our algorithms compared to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.
Author Information
Gabriele Abbati (University of Oxford)
Philippe Wenk (ETH Zurich)
Michael A Osborne (U Oxford)
Andreas Krause (ETH Zurich)

Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center and Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Max Planck Fellow at the Max Planck Institute for Intelligent Systems, an ELLIS Fellow, a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received the Rössler Prize, ERC Starting Investigator and ERC Consolidator grants, the German Pattern Recognition Award, an NSF CAREER award as well as the ETH Golden Owl teaching award. His research has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. Andreas Krause served as Program Co-Chair for ICML 2018, and currently serves as General Chair for ICML 2023 and as Action Editor for the Journal of Machine Learning Research.
Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Stefan Bauer (MPI for Intelligent Systems)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Poster: AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs »
Thu. Jun 13th 01:30 -- 04:00 AM Room Pacific Ballroom #216
More from the Same Authors
-
2021 : Attacking Graph Classification via Bayesian Optimisation »
Xingchen Wan · Henry Kenlay · Binxin Ru · Arno Blaas · Michael A Osborne · Xiaowen Dong -
2021 : Towards Principled Disentanglement for Domain Generalization »
Hanlin Zhang · Yi-Fan Zhang · Weiyang Liu · Adrian Weller · Bernhard Schölkopf · Eric Xing -
2021 : Revisiting Design Choices in Offline Model Based Reinforcement Learning »
Cong Lu · Philip Ball · Jack Parker-Holder · Michael A Osborne · Stephen Roberts -
2021 : Variational Causal Networks: Approximate Bayesian Inference over Causal Structures »
Yashas Annadani · Jonas Rothfuss · Alexandre Lacoste · Nino Scherrer · Anirudh Goyal · Yoshua Bengio · Stefan Bauer -
2022 : Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges »
Ya-Ping Hsieh · Charlotte Bunne · Marco Cuturi · Andreas Krause -
2022 : Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges »
Charlotte Bunne · Ya-Ping Hsieh · Marco Cuturi · Andreas Krause -
2022 : Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations »
Cong Lu · Philip Ball · Tim G. J Rudner · Jack Parker-Holder · Michael A Osborne · Yee-Whye Teh -
2023 : Anytime Model Selection in Linear Bandits »
Parnian Kassraie · Aldo Pacchiano · Nicolas Emmenegger · Andreas Krause -
2023 : Unbalanced Diffusion Schrödinger Bridge »
Matteo Pariset · Ya-Ping Hsieh · Charlotte Bunne · Andreas Krause · Valentin De Bortoli -
2023 : Aligned Diffusion Schrödinger Bridges »
Vignesh Ram Somnath · Matteo Pariset · Ya-Ping Hsieh · Maria Rodriguez Martinez · Andreas Krause · Charlotte Bunne -
2023 : Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces »
Miles Wang-Henderson · Bartu Soyuer · Parnian Kassraie · Andreas Krause · Ilija Bogunovic -
2023 : SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces »
Masaki Adachi · Satoshi Hayakawa · Saad Hamid · Martin Jørgensen · Harald Oberhauser · Michael A Osborne -
2023 : Anytime Model Selection in Linear Bandits »
Parnian Kassraie · Aldo Pacchiano · Nicolas Emmenegger · Andreas Krause -
2023 Panel: ICML Education Outreach Panel »
Andreas Krause · Barbara Engelhardt · Emma Brunskill · Kyunghyun Cho -
2022 Workshop: Adaptive Experimental Design and Active Learning in the Real World »
Mojmir Mutny · Willie Neiswanger · Ilija Bogunovic · Stefano Ermon · Yisong Yue · Andreas Krause -
2022 Poster: Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning »
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison -
2022 Spotlight: Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning »
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison -
2022 Poster: Robust Multi-Objective Bayesian Optimization Under Input Noise »
Samuel Daulton · Sait Cakmak · Maximilian Balandat · Michael A Osborne · Enlu Zhou · Eytan Bakshy -
2022 Poster: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Spotlight: Robust Multi-Objective Bayesian Optimization Under Input Noise »
Samuel Daulton · Sait Cakmak · Maximilian Balandat · Michael A Osborne · Enlu Zhou · Eytan Bakshy -
2022 Spotlight: Interactively Learning Preference Constraints in Linear Bandits »
David Lindner · Sebastian Tschiatschek · Katja Hofmann · Andreas Krause -
2022 Poster: Adaptive Gaussian Process Change Point Detection »
Edoardo Caldarelli · Philippe Wenk · Stefan Bauer · Andreas Krause -
2022 Poster: Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation »
Pier Giuseppe Sessa · Maryam Kamgarpour · Andreas Krause -
2022 Poster: Meta-Learning Hypothesis Spaces for Sequential Decision-making »
Parnian Kassraie · Jonas Rothfuss · Andreas Krause -
2022 Spotlight: Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation »
Pier Giuseppe Sessa · Maryam Kamgarpour · Andreas Krause -
2022 Spotlight: Meta-Learning Hypothesis Spaces for Sequential Decision-making »
Parnian Kassraie · Jonas Rothfuss · Andreas Krause -
2022 Spotlight: Adaptive Gaussian Process Change Point Detection »
Edoardo Caldarelli · Philippe Wenk · Stefan Bauer · Andreas Krause -
2021 : Data Summarization via Bilevel Coresets »
Andreas Krause -
2021 Workshop: Challenges in Deploying and monitoring Machine Learning Systems »
Alessandra Tosi · Nathan Korda · Michael A Osborne · Stephen Roberts · Andrei Paleyes · Fariba Yousefi -
2021 Poster: PopSkipJump: Decision-Based Attack for Probabilistic Classifiers »
Carl-Johann Simon-Gabriel · Noman Ahmed Sheikh · Andreas Krause -
2021 Spotlight: PopSkipJump: Decision-Based Attack for Probabilistic Classifiers »
Carl-Johann Simon-Gabriel · Noman Ahmed Sheikh · Andreas Krause -
2021 Poster: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Poster: PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees »
Jonas Rothfuss · Vincent Fortuin · Martin Josifoski · Andreas Krause -
2021 Oral: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Spotlight: PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees »
Jonas Rothfuss · Vincent Fortuin · Martin Josifoski · Andreas Krause -
2021 Poster: Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems »
Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour -
2021 Spotlight: Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems »
Pier Giuseppe Sessa · Ilija Bogunovic · Andreas Krause · Maryam Kamgarpour -
2021 Poster: No-regret Algorithms for Capturing Events in Poisson Point Processes »
Mojmir Mutny · Andreas Krause -
2021 Poster: Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning »
Sebastian Curi · Ilija Bogunovic · Andreas Krause -
2021 Spotlight: No-regret Algorithms for Capturing Events in Poisson Point Processes »
Mojmir Mutny · Andreas Krause -
2021 Spotlight: Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning »
Sebastian Curi · Ilija Bogunovic · Andreas Krause -
2021 Poster: Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces »
Xingchen Wan · Vu Nguyen · Huong Ha · Binxin Ru · Cong Lu · Michael A Osborne -
2021 Poster: Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search »
Vu Nguyen · Tam Le · Makoto Yamada · Michael A Osborne -
2021 Spotlight: Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces »
Xingchen Wan · Vu Nguyen · Huong Ha · Binxin Ru · Cong Lu · Michael A Osborne -
2021 Spotlight: Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search »
Vu Nguyen · Tam Le · Makoto Yamada · Michael A Osborne -
2021 Poster: Bias-Robust Bayesian Optimization via Dueling Bandits »
Johannes Kirschner · Andreas Krause -
2021 Poster: Fast Projection Onto Convex Smooth Constraints »
Ilnura Usmanova · Maryam Kamgarpour · Andreas Krause · Kfir Levy -
2021 Spotlight: Fast Projection Onto Convex Smooth Constraints »
Ilnura Usmanova · Maryam Kamgarpour · Andreas Krause · Kfir Levy -
2021 Spotlight: Bias-Robust Bayesian Optimization via Dueling Bandits »
Johannes Kirschner · Andreas Krause -
2020 : Constrained Maximization of Lattice Submodular Functions »
Aytunc Sahin · Joachim Buhmann · Andreas Krause -
2020 : Q&A: Bernhard Scholkopf »
Bernhard Schölkopf · Mayoore Jaiswal -
2020 : Invited Talk: Bernhard Scholkopf »
Bernhard Schölkopf -
2020 Poster: From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models »
Aytunc Sahin · Yatao Bian · Joachim Buhmann · Andreas Krause -
2020 Poster: Knowing The What But Not The Where in Bayesian Optimization »
Vu Nguyen · Michael A Osborne -
2020 Poster: Bayesian Optimisation over Multiple Continuous and Categorical Inputs »
Binxin Ru · Ahsan Alvi · Vu Nguyen · Michael A Osborne · Stephen Roberts -
2020 Test Of Time: Test of Time: Gaussian Process Optimization in the Bandit Settings: No Regret and Experimental Design »
Niranjan Srinivas · Andreas Krause · Sham Kakade · Matthias Seeger -
2019 Poster: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Poster: Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces »
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause -
2019 Poster: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: On the Limitations of Representing Functions on Sets »
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne -
2019 Oral: Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness »
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer -
2019 Oral: Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces »
Johannes Kirschner · Mojmir Mutny · Nicole Hiller · Rasmus Ischebeck · Andreas Krause -
2019 Oral: On the Limitations of Representing Functions on Sets »
Edward Wagstaff · Fabian Fuchs · Martin Engelcke · Ingmar Posner · Michael A Osborne -
2019 Oral: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Poster: Learning Generative Models across Incomparable Spaces »
Charlotte Bunne · David Alvarez-Melis · Andreas Krause · Stefanie Jegelka -
2019 Poster: Automated Model Selection with Bayesian Quadrature »
Henry Chai · Jean-Francois Ton · Michael A Osborne · Roman Garnett -
2019 Poster: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Oral: Learning Generative Models across Incomparable Spaces »
Charlotte Bunne · David Alvarez-Melis · Andreas Krause · Stefanie Jegelka -
2019 Oral: Automated Model Selection with Bayesian Quadrature »
Henry Chai · Jean-Francois Ton · Michael A Osborne · Roman Garnett -
2019 Oral: Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation »
Ahsan Alvi · Binxin Ru · Jan-Peter Calliess · Stephen Roberts · Michael A Osborne -
2019 Poster: Fingerprint Policy Optimisation for Robust Reinforcement Learning »
Supratik Paul · Michael A Osborne · Shimon Whiteson -
2019 Poster: Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference »
Yatao Bian · Joachim Buhmann · Andreas Krause -
2019 Poster: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2019 Oral: Fingerprint Policy Optimisation for Robust Reinforcement Learning »
Supratik Paul · Michael A Osborne · Shimon Whiteson -
2019 Oral: Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference »
Yatao Bian · Joachim Buhmann · Andreas Krause -
2019 Oral: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2018 Poster: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol -
2018 Poster: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne -
2018 Oral: Optimization, fast and slow: optimally switching between local and Bayesian optimization »
Mark McLeod · Stephen Roberts · Michael A Osborne -
2018 Oral: Fast Information-theoretic Bayesian Optimisation »
Binxin Ru · Michael A Osborne · Mark Mcleod · Diego Granziol -
2017 Poster: Guarantees for Greedy Maximization of Non-submodular Functions with Applications »
Yatao Bian · Joachim Buhmann · Andreas Krause · Sebastian Tschiatschek -
2017 Poster: Differentially Private Submodular Maximization: Data Summarization in Disguise »
Marko Mitrovic · Mark Bun · Andreas Krause · Amin Karbasi -
2017 Poster: Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" »
Baharan Mirzasoleiman · Amin Karbasi · Andreas Krause -
2017 Poster: Probabilistic Submodular Maximization in Sub-Linear Time »
Serban A Stan · Morteza Zadimoghaddam · Andreas Krause · Amin Karbasi -
2017 Talk: Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" »
Baharan Mirzasoleiman · Amin Karbasi · Andreas Krause -
2017 Talk: Probabilistic Submodular Maximization in Sub-Linear Time »
Serban A Stan · Morteza Zadimoghaddam · Andreas Krause · Amin Karbasi -
2017 Talk: Guarantees for Greedy Maximization of Non-submodular Functions with Applications »
Yatao Bian · Joachim Buhmann · Andreas Krause · Sebastian Tschiatschek -
2017 Talk: Differentially Private Submodular Maximization: Data Summarization in Disguise »
Marko Mitrovic · Mark Bun · Andreas Krause · Amin Karbasi -
2017 Poster: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause -
2017 Poster: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Talk: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Talk: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause