Google
Google Scholar
Semantic Scholar
MS Academic
CiteSeerX
ORCIDBernhard Schölkopf
Person information
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- award (2018): Gottfried Wilhelm Leibniz Prize
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2020
[j100]Olga Mineeva, Mateo Rojas-Carulla, Ruth Ley, Bernhard Schölkopf, Nicholas Youngblut
:
DeepMAsED: evaluating the quality of metagenomic assemblies. Bioinform. 36(10): 3011-3017 (2020)- [j99]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020) - [j98]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21: 209:1-209:62 (2020) - [j97]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. IEEE Robotics Autom. Lett. 5(2): 970-976 (2020) - [j96]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Adaptation and Robust Learning of Probabilistic Movement Primitives. IEEE Trans. Robotics 36(2): 366-379 (2020) - [c295]Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. AAAI 2020: 6364-6371 - [c294]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020: 13681-13684 - [c293]Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Fair Decisions Despite Imperfect Predictions. AISTATS 2020: 277-287 - [c292]Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf:
Counterfactuals uncover the modular structure of deep generative models. ICLR 2020 - [c291]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. ICLR 2020 - [c290]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c289]Diego Agudelo-España, Andrii Zadaianchuk, Philippe Wenk, Aditya Garg, Joel Akpo, Felix Grimminger, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Georg Martius, Andreas Krause, Bernhard Schölkopf, Stefan Bauer, Manuel Wüthrich:
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models. ICRA 2020: 8151-8157 - [c288]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. L4DC 2020: 915-923 - [c287]Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf:
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020: 11 - [c286]Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez:
On the design of consequential ranking algorithms. UAI 2020: 95 - [c285]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. UAI 2020: 109 - [c284]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Prediction of Change Points. UAI 2020: 137 - [c283]Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware. UIST 2020: 1071-1084 - [i169]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. CoRR abs/2001.10398 (2020) - [i168]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i167]Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera:
Algorithmic Recourse: from Counterfactual Explanations to Interventions. CoRR abs/2002.06278 (2020) - [i166]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. CoRR abs/2002.10271 (2020) - [i165]Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware. CoRR abs/2002.11754 (2020) - [i164]Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause:
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives. CoRR abs/2003.02658 (2020) - [i163]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR abs/2004.00166 (2020) - [i162]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A theory of independent mechanisms for extrapolation in generative models. CoRR abs/2004.00184 (2020) - [i161]Lars Lorch, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment. CoRR abs/2004.07641 (2020) - [i160]Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf:
Towards causal generative scene models via competition of experts. CoRR abs/2004.12906 (2020) - [i159]Louis Abraham, Gary Bécigneul, Bernhard Schölkopf:
Crackovid: Optimizing Group Testing. CoRR abs/2005.06413 (2020) - [i158]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. CoRR abs/2006.02286 (2020) - [i157]Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems. CoRR abs/2006.03947 (2020) - [i156]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch using Muscular Robots. CoRR abs/2006.05935 (2020) - [i155]Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. CoRR abs/2006.06831 (2020) - [i154]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization. CoRR abs/2006.06981 (2020) - [i153]Felix Leeb, Yashas Annadani, Stefan Bauer, Bernhard Schölkopf:
Structural Autoencoders Improve Representations for Generation and Transfer. CoRR abs/2006.07796 (2020) - [i152]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i151]Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. CoRR abs/2006.15090 (2020) - [i150]Anant Raj, Stefan Bauer, Ashkan Soleymani, Michel Besserve, Bernhard Schölkopf:
Causal Feature Selection via Orthogonal Search. CoRR abs/2007.02938 (2020) - [i149]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
S2RMs: Spatially Structured Recurrent Modules. CoRR abs/2007.06533 (2020) - [i148]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i147]Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf:
Geometrically Enriched Latent Spaces. CoRR abs/2008.00565 (2020) - [i146]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020) - [i145]Manuel Wüthrich, Felix Widmaier, Felix Grimminger, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer:
TriFinger: An Open-Source Robot for Learning Dexterity. CoRR abs/2008.03596 (2020) - [i144]Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf:
Learning Dynamical Systems using Local Stability Priors. CoRR abs/2008.10053 (2020) - [i143]Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer:
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records. CoRR abs/2008.13412 (2020) - [i142]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. CoRR abs/2009.00329 (2020) - [i141]Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning. CoRR abs/2010.03110 (2020) - [i140]Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera:
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects. CoRR abs/2010.04050 (2020) - [i139]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. CoRR abs/2010.04296 (2020) - [i138]Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf:
Physically constrained causal noise models for high-contrast imaging of exoplanets. CoRR abs/2010.05591 (2020) - [i137]Julius von Kügelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. CoRR abs/2010.06529 (2020) - [i136]Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wüthrich, Bernhard Schölkopf:
Function Contrastive Learning of Transferable Representations. CoRR abs/2010.07093 (2020) - [i135]Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet:
Maximum Moment Restriction for Instrumental Variable Regression. CoRR abs/2010.07684 (2020) - [i134]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B. Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David Buckeridge, Gaétan Marceau Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. CoRR abs/2010.12536 (2020) - [i133]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. CoRR abs/2010.14407 (2020) - [i132]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR abs/2010.14766 (2020) - [i131]Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St-Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Joseph Pal, Joanna Merckx, Eilif B. Müller, Yoshua Bengio:
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. CoRR abs/2010.16004 (2020) - [i130]Gilles Barthe, Roberta De Viti, Peter Druschel, Deepak Garg, Manuel Gomez-Rodriguez, Pierfrancesco Ingo, Matthew Lentz, Aastha Mehta, Bernhard Schölkopf:
PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation. CoRR abs/2011.08069 (2020)
2010 – 2019
- 2019
- [j95]Vincent Stimper, Stefan Bauer, Ralph Ernstorfer, Bernhard Schölkopf, Rui Patrick Xian:
Multidimensional Contrast Limited Adaptive Histogram Equalization. IEEE Access 7: 165437-165447 (2019) - [j94]Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. J. Mach. Learn. Res. 20: 147:1-147:50 (2019) - [j93]Rohit Babbar, Bernhard Schölkopf:
Data scarcity, robustness and extreme multi-label classification. Mach. Learn. 108(8-9): 1329-1351 (2019) - [j92]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5: e169 (2019) - [j91]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real-Time Ball Tracking for Robot Table Tennis. Robotics 8(4): 90 (2019) - [c282]Matthias R. Hohmann, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: A Platform for Large-scale Neuroscientific Studies. CHI Extended Abstracts 2019 - [c281]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Beta Power May Meditate the Effect of Gamma-TACS on Motor Performance. EMBC 2019: 5902-5908 - [c280]Bernhard Schölkopf:
Learning causal mechanisms. GI-Jahrestagung 2019: 21 - [c279]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR 2019 - [c278]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments. DGS@ICLR 2019 - [c277]Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer:
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. ICML 2019: 1-10 - [c276]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. ICML 2019: 3140-3151 - [c275]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019: 4114-4124 - [c274]Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz:
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML 2019: 5809-5817 - [c273]Raphael Suter, Ðorðe Miladinovic, Bernhard Schölkopf, Stefan Bauer:
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. ICML 2019: 6056-6065 - [c272]Jiachen Xu, Vinay Jayaram, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Feature extraction from the Hermitian manifold for Brain-Computer Interfaces. NER 2019: 965-968 - [c271]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. NeurIPS 2019: 2240-2250 - [c270]Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann:
Perceiving the arrow of time in autoregressive motion. NeurIPS 2019: 2303-2314 - [c269]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Selecting causal brain features with a single conditional independence test per feature. NeurIPS 2019: 12532-12543 - [c268]Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. NeurIPS 2019: 14584-14597 - [c267]Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NeurIPS 2019: 15714-15725 - [c266]Ozan Özdenizci, Timm Meyer, Felix A. Wichmann, Jan Peters, Bernhard Schölkopf, Müjdat Çetin, Moritz Grosse-Wentrup:
Neural Signatures of Motor Skill in the Resting Brain. SMC 2019: 4387-4394 - [c265]Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf:
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. UAI 2019: 49 - [c264]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. UAI 2019: 53 - [i129]Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. CoRR abs/1901.09206 (2019) - [i128]Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas Youngblut, Ruth Ley, Bernhard Schölkopf:
GeNet: Deep Representations for Metagenomics. CoRR abs/1901.11015 (2019) - [i127]Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Improving Consequential Decision Making under Imperfect Predictions. CoRR abs/1902.02979 (2019) - [i126]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Detection and Prediction of Change Points. CoRR abs/1902.04524 (2019) - [i125]Philippe Wenk, Gabriele Abbati, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. CoRR abs/1902.06278 (2019) - [i124]Gabriele Abbati, Philippe Wenk, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs. CoRR abs/1902.08480 (2019) - [i123]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i122]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. CoRR abs/1903.12436 (2019) - [i121]Timothy D. Gebhard, Niki Kilbertus, Ian Harry, Bernhard Schölkopf:
Convolutional neural networks: a magic bullet for gravitational-wave detection? CoRR abs/1904.08693 (2019) - [i120]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i119]Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez:
Consequential Ranking Algorithms and Long-term Welfare. CoRR abs/1905.05305 (2019) - [i118]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. CoRR abs/1905.05882 (2019) - [i117]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA. CoRR abs/1905.06642 (2019) - [i116]Moein Khajehnejad, Behzad Tabibian, Bernhard Schölkopf, Adish Singla, Manuel Gomez-Rodriguez:
Optimal Decision Making Under Strategic Behavior. CoRR abs/1905.09239 (2019) - [i115]Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf:
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR abs/1905.12081 (2019) - [i114]Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf:
Quantum Mean Embedding of Probability Distributions. CoRR abs/1905.13526 (2019) - [i113]Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. CoRR abs/1905.13662 (2019) - [i112]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Representations. CoRR abs/1906.03255 (2019) - [i111]Muhammad Waleed Gondal, Manuel Wüthrich, Ðorðe Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. CoRR abs/1906.03292 (2019) - [i110]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real Time Ball Tracking for Robot Table Tennis. CoRR abs/1908.07332 (2019) - [i109]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. CoRR abs/1909.03895 (2019) - [i108]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. CoRR abs/1909.10893 (2019) - [i107]Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller:
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. CoRR abs/1910.03962 (2019) - [i106]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. CoRR abs/1910.12252 (2019) - [i105]Bernhard Schölkopf:
Causality for Machine Learning. CoRR abs/1911.10500 (2019) - [i104]Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf:
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR abs/1911.11082 (2019) - 2018
- [j90]Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters:
Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. 19: 36:1-36:34 (2018) - [j89]Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions. J. Mach. Learn. Res. 19: 44:1-44:29 (2018) - [j88]Dieter Buchler, Roberto Calandra, Bernhard Schölkopf, Jan Peters:
Control of Musculoskeletal Systems Using Learned Dynamics Models. IEEE Robotics Autom. Lett. 3(4): 3161-3168 (2018) - [j87]Lei Xiao, Felix Heide, Wolfgang Heidrich, Bernhard Schölkopf, Michael Hirsch:
Discriminative Transfer Learning for General Image Restoration. IEEE Trans. Image Process. 27(8): 4091-4104 (2018) - [c263]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. AISTATS 2018: 557-565 - [c262]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. AISTATS 2018: 900-909 - [c261]Friedrich Solowjow, Arash Mehrjou, Bernhard Schölkopf, Sebastian Trimpe:
Efficient Encoding of Dynamical Systems Through Local Approximations. CDC 2018: 6073-6079 - [c260]Muhammad Waleed Gondal, Bernhard Schölkopf, Michael Hirsch:
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-Resolution. ECCV Workshops (5) 2018: 80-97 - [c259]Tae Hyun Kim, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf:
Spatio-Temporal Transformer Network for Video Restoration. ECCV (3) 2018: 111-127 - [c258]Matthias Bauer, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf:
Automatic estimation of modulation transfer functions. ICCP 2018: 1-12 - [c257]Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf:
Fidelity-Weighted Learning. ICLR (Poster) 2018 - [c256]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c255]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Learning Disentangled Representations with Wasserstein Auto-Encoders. ICLR (Workshop) 2018 - [c254]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. ICLR (Workshop) 2018 - [c253]