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ORCIDBernhard Schölkopf
Person information
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- award (2018): Gottfried Wilhelm Leibniz Prize
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2020 – today
- 2023
[j116]Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
Reinforcement learning with model-based feedforward inputs for robotic table tennis. Auton. Robots 47(8): 1387-1403 (2023)- [j115]Amir-Hossein Karimi, Gilles Barthe
, Bernhard Schölkopf, Isabel Valera:
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations. ACM Comput. Surv. 55(5): 95:1-95:29 (2023) - [j114]Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. J. Mach. Learn. Res. 24: 184:1-184:20 (2023) - [j113]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. J. Open Source Softw. 8(87): 5361 (2023) - [j112]Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf:
Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns 4(6): 100739 (2023) - [j111]Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer:
Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases. PLoS Comput. Biol. 19(1) (2023) - [j110]Olga Mineeva, Daniel Danciu, Bernhard Schölkopf, Ruth E. Ley, Gunnar Rätsch, Nicholas D. Youngblut:
ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Comput. Biol. 19(5) (2023) - [j109]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. 2023 (2023) - [c412]Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Schölkopf, Mrinmaya Sachan:
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models. ACL (1) 2023: 545-561 - [c411]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. ACL (Findings) 2023: 11330-11343 - [c410]Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. AISTATS 2023: 1411-1436 - [c409]Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf:
Iterative Teaching by Data Hallucination. AISTATS 2023: 9892-9913 - [c408]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CAIN 2023: 7-12 - [c407]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c406]Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf:
On the Interventional Kullback-Leibler Divergence. CLeaR 2023: 328-349 - [c405]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CLeaR 2023: 553-573 - [c404]Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf, Setareh Maghsudi:
Robustness Implies Fairness in Causal Algorithmic Recourse. FAccT 2023: 984-1001 - [c403]Max-Olivier Van Bastelaer, Heiner Kremer, Valentin Volchkov, Jean-Claude Passy, Bernhard Schölkopf:
Glare Removal for Astronomical Images with High Local Dynamic Range. ICCP 2023: 1-11 - [c402]Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf:
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. ICLR 2023 - [c401]Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius:
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. ICLR 2023 - [c400]Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf:
Structure by Architecture: Structured Representations without Regularization. ICLR 2023 - [c399]Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf:
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. ICLR 2023 - [c398]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. ICLR 2023 - [c397]Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello:
Bridging the Gap to Real-World Object-Centric Learning. ICLR 2023 - [c396]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. ICML 2023: 3038-3062 - [c395]Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf:
On Data Manifolds Entailed by Structural Causal Models. ICML 2023: 8188-8201 - [c394]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023: 14316-14332 - [c393]Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML 2023: 14333-14352 - [c392]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. ICML 2023: 15861-15883 - [c391]Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. ICML 2023: 16190-16215 - [c390]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML 2023: 17745-17783 - [c389]Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou:
Diffusion Based Representation Learning. ICML 2023: 24963-24982 - [c388]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. ICML 2023: 31930-31968 - [c387]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. ICML 2023: 34431-34455 - [c386]Alexander Dittrich, Jan Schneider, Simon Guist, Nico Gürtler, Heiko Ott, Thomas Steinbrenner, Bernhard Schölkopf, Dieter Büchler:
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. ICRA 2023: 3058-3064 - [c385]Majid Khadiv, Avadesh Meduri, Huaijiang Zhu, Ludovic Righetti, Bernhard Schölkopf:
Learning Locomotion Skills from MPC in Sensor Space. L4DC 2023: 1218-1230 - [c384]Simon Guist, Jan Schneider, Vincent Berenz, Alexander Dittrich, Bernhard Schölkopf, Dieter Büchler:
Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning. Robotics: Science and Systems 2023 - [c383]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal effect estimation from observational and interventional data through matrix weighted linear estimators. UAI 2023: 1087-1097 - [d7]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version v1.0.1. Zenodo, 2023 [all versions]
- [d6]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.0. Zenodo, 2023 [all versions] - [d5]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.1. Zenodo, 2023 [all versions] - [d4]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.2. Zenodo, 2023 [all versions] - [i314]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CoRR abs/2301.05169 (2023) - [i313]Flavio Schneider, Zhijing Jin, Bernhard Schölkopf:
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion. CoRR abs/2301.11757 (2023) - [i312]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
The passive symmetries of machine learning. CoRR abs/2301.13724 (2023) - [i311]Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf, Setareh Maghsudi:
Robustness Implies Fairness in Causal Algorithmic Recourse. CoRR abs/2302.03465 (2023) - [i310]Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf:
On the Interventional Kullback-Leibler Divergence. CoRR abs/2302.05380 (2023) - [i309]Uddeshya Upadhyay, Jae-Myung Kim, Cordelia Schmid, Bernhard Schölkopf, Zeynep Akata:
Posterior Annealing: Fast Calibrated Uncertainty for Regression. CoRR abs/2302.11012 (2023) - [i308]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. CoRR abs/2302.12014 (2023) - [i307]Simon Guist, Jan Schneider, Alexander Dittrich, Vincent Berenz, Bernhard Schölkopf, Dieter Büchler:
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning. CoRR abs/2303.02234 (2023) - [i306]Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf:
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. CoRR abs/2303.06484 (2023) - [i305]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CoRR abs/2303.09552 (2023) - [i304]Siyuan Guo, Jonas Wildberger, Bernhard Schölkopf:
Out-of-Variable Generalization. CoRR abs/2304.07896 (2023) - [i303]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. CoRR abs/2304.07939 (2023) - [i302]Zhiheng Lyu, Zhijing Jin, Justus Mattern, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schölkopf:
Psychologically-Inspired Causal Prompts. CoRR abs/2305.01764 (2023) - [i301]Fernando Gonzalez, Zhijing Jin, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachan, Rada Mihalcea:
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good. CoRR abs/2305.05471 (2023) - [i300]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. CoRR abs/2305.09088 (2023) - [i299]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. CoRR abs/2305.10898 (2023) - [i298]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. CoRR abs/2305.14229 (2023) - [i297]Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Ryan Cotterell:
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations. CoRR abs/2305.14555 (2023) - [i296]Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schölkopf, Zhijing Jin, Rada Mihalcea:
Voices of Her: Analyzing Gender Differences in the AI Publication World. CoRR abs/2305.14597 (2023) - [i295]Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet:
A Measure-Theoretic Axiomatisation of Causality. CoRR abs/2305.17139 (2023) - [i294]Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. CoRR abs/2305.17161 (2023) - [i293]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. CoRR abs/2305.17225 (2023) - [i292]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. CoRR abs/2305.18462 (2023) - [i291]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i290]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR abs/2306.01157 (2023) - [i289]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR abs/2306.02235 (2023) - [i288]Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR abs/2306.03968 (2023) - [i287]Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona T. Diab, Bernhard Schölkopf:
Can Large Language Models Infer Causation from Correlation? CoRR abs/2306.05836 (2023) - [i286]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators. CoRR abs/2306.06002 (2023) - [i285]Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf:
Controlling Text-to-Image Diffusion by Orthogonal Finetuning. CoRR abs/2306.07280 (2023) - [i284]Vincent Berenz, Felix Widmaier, Simon Guist, Bernhard Schölkopf, Dieter Büchler:
Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80. CoRR abs/2306.09764 (2023) - [i283]Aaron Spieler, Nasim Rahaman, Georg Martius, Bernhard Schölkopf, Anna Levina:
The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks. CoRR abs/2306.16922 (2023) - [i282]Simon Guist, Jan Schneider, Hao Ma, Vincent Berenz, Julian Martus, Felix Grüninger, Michael Mühlebach, Jonathan Fiene, Bernhard Schölkopf, Dieter Büchler:
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions. CoRR abs/2307.02654 (2023) - [i281]Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf:
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. CoRR abs/2307.09933 (2023) - [i280]Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius:
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. CoRR abs/2307.15690 (2023) - [i279]Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin A. Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius:
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World. CoRR abs/2308.07741 (2023) - [i278]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. CoRR abs/2308.10364 (2023) - [i277]Philip Tobuschat, Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis. CoRR abs/2308.14562 (2023) - [i276]Timothy D. Gebhard, Daniel Angerhausen, Björn S. Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf:
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks. CoRR abs/2309.03075 (2023) - [i275]Jan Schneider, Pierre Schumacher, Daniel F. B. Häufle, Bernhard Schölkopf, Dieter Büchler:
Investigating the Impact of Action Representations in Policy Gradient Algorithms. CoRR abs/2309.06921 (2023) - [i274]Léon Bottou, Bernhard Schölkopf:
Borges and AI. CoRR abs/2310.01425 (2023) - [i273]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations. CoRR abs/2310.07665 (2023) - [i272]Open X.-Embodiment Collaboration, Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Raj, Anikait Singh, Anthony Brohan, Antonin Raffin, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Brian Ichter, Cewu Lu, Charles Xu, Chelsea Finn, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Chuer Pan, Chuyuan Fu, Coline Devin, Danny Driess, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Federico Ceola, Fei Xia, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Giulio Schiavi, Gregory Kahn, Hao Su, Haoshu Fang, Haochen Shi, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Igor Mordatch, Ilija Radosavovic, et al.:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models. CoRR abs/2310.08864 (2023) - [i271]Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf:
Pairwise Similarity Learning is SimPLE. CoRR abs/2310.09449 (2023) - [i270]Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Schölkopf:
Ghost on the Shell: An Expressive Representation of General 3D Shapes. CoRR abs/2310.15168 (2023) - [i269]Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. CoRR abs/2310.17405 (2023) - [i268]Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Negar Kiyavash, Mrinmaya Sachan, Bernhard Schölkopf:
CausalCite: A Causal Formulation of Paper Citations. CoRR abs/2311.02790 (2023) - [i267]Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf:
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. CoRR abs/2311.06243 (2023) - [i266]David F. Jenny, Yann Billeter, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin:
Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures. CoRR abs/2311.08605 (2023) - [i265]Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim:
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. CoRR abs/2311.08815 (2023) - [i264]Armin Kekic, Bernhard Schölkopf, Michel Besserve:
Targeted Reduction of Causal Models. CoRR abs/2311.18639 (2023) - 2022
- [j108]Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Schölkopf, Laura Leal-Taixé, Xiao Xiang Zhu:
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. IEEE Trans. Geosci. Remote. Sens. 60: 1-15 (2022) - [j107]Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve:
Causal Feature Selection via Orthogonal Search. Trans. Mach. Learn. Res. 2022 (2022) - [j106]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch Using Muscular Robots. IEEE Trans. Robotics 38(6): 3850-3860 (2022) - [j105]Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots. ACM Trans. Spatial Algorithms Syst. 8(4): 25:1-25:28 (2022) - [c382]Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. AAAI 2022: 9584-9594 - [c381]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
A Witness Two-Sample Test. AISTATS 2022: 1403-1419 - [c380]Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf:
A prior-based approximate latent Riemannian metric. AISTATS 2022: 4634-4658 - [c379]Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. AISTATS 2022: 4915-4936 - [c378]Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf:
Adversarially Robust Kernel Smoothing. AISTATS 2022: 4972-4994 - [c377]Sumedh A. Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. AISTATS 2022: 7518-7530 - [c376]Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Learning Random Feature Dynamics for Uncertainty Quantification. CDC 2022: 4937-4944 - [c375]Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu:
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CDC 2022: 5660-5667 - [c374]Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf:
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR 2022: 110-143 - [c373]Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing:
Towards Principled Disentanglement for Domain Generalization. CVPR 2022: 8014-8024 - [c372]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR 2022: 10400-10411 - [c371]Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter V. Gehler:
Towards Total Recall in Industrial Anomaly Detection. CVPR 2022: 14298-14308 - [c370]Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller, Bernhard Schölkopf:
Structural Causal 3D Reconstruction. ECCV (1) 2022: 140-159 - [c369]