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AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge for AI safety is capturing the flexibility of the human moral mind — the ability to determine when a rule should be broken, especially in novel or unusual situations. In this paper, we present a novel challenge set consisting of moral exception question answering (MoralExceptQA) of cases that involve potentially permissible moral exceptions – inspired by recent moral psychology studies. Using a state-of-the-art large language model (LLM) as a basis, we propose a novel moral chain of thought (MoralCoT) prompting strategy that combines the strengths of LLMs with theories of moral reasoning developed in cognitive science to predict human moral judgments. MoralCoT outperforms seven existing LLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to capture the flexibility of the human moral mind. We also conduct a detailed error analysis to suggest directions for future work to improve AI safety using MoralExceptQA. Our data is open-sourced at https://huggingface.co/datasets/feradauto/MoralExceptQA and code at https://github.com/feradauto/MoralCoT.
Author Information
Zhijing Jin (ETH Zürich)
Sydney Levine (MIT / Allen AI)
Fernando Gonzalez Adauto (ETH Zurich)
Ojasv Kamal (Indian Institute of Technology Kharagpur)
Final year Undergraduate @IIT Kharagpur Works in MoralNLP
Maarten Sap (Carnegie Mellon University)

Maarten Sap is an assistant professor in Carnegie Mellon University's Language Technologies Department (CMU LTI). His research focuses on making NLP systems socially intelligent, and understanding social inequality and bias in language. He has presented his work in top-tier NLP and AI conferences, receiving a best short paper nomination at ACL 2019 and a best paper award at the WeCNLP 2020 summit. His research has been covered in the New York Times, Forbes, Fortune, and Vox. Additionally, he and his team won the inaugural 2017 Amazon Alexa Prize, a social chatbot competition. Before joining CMU, he was a postdoc/young investigator at the Allen Institute for AI (AI2) on project MOSAIC. He received his PhD from the University of Washington's Paul G. Allen School of Computer Science & Engineering where he was advised by Yejin Choi and Noah Smith. In the past, he has interned at the Allen Institute for AI working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.
Mrinmaya Sachan (ETH Zurich)
Rada Mihalcea (University of Michigan)
Josh Tenenbaum (MIT)
Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
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2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning »
Biwei Huang · Sara Magliacane · Kun Zhang · Danielle Belgrave · Elias Bareinboim · Daniel Malinsky · Thomas Richardson · Christopher Meek · Peter Spirtes · Bernhard Schölkopf -
2020 Workshop: Differentiable computer vision, graphics, and physics in machine learning »
Krishna Murthy Jatavallabhula · Kelsey Allen · Victoria Dean · Johanna Hansen · Shuran Song · Florian Shkurti · Liam Paull · Derek Nowrouzezahrai · Josh Tenenbaum -
2020 Poster: Online Bayesian Goal Inference for Boundedly Rational Planning Agents »
Tan Zhi-Xuan · Jordyn Mann · Tom Silver · Josh Tenenbaum · Vikash Mansinghka -
2020 Poster: Program Synthesis with Pragmatic Communication »
Yewen Pu · Kevin Ellis · Marta Kryven · Josh Tenenbaum · Armando Solar-Lezama -
2020 Poster: Learning Compositional Rules via Neural Program Synthesis »
Maxwell Nye · Armando Solar-Lezama · Josh Tenenbaum · Brenden Lake -
2020 Poster: Learning abstract structure for drawing by efficient motor program induction »
Lucas Tian · Kevin Ellis · Marta Kryven · Josh Tenenbaum -
2020 Oral: Learning abstract structure for drawing by efficient motor program induction »
Lucas Tian · Kevin Ellis · Marta Kryven · Josh Tenenbaum -
2020 Memorial: In Memory of Olivier Chapelle »
Bernhard Schölkopf · Andre Elisseeff · Olivier Bousquet · Vladimir Vapnik · Jason E Weston -
2020 Poster: Learning Kernel Tests Without Data Splitting »
Jonas Kübler · Wittawat Jitkrittum · Bernhard Schölkopf · Krikamol Muandet -
2020 Poster: Algorithmic recourse under imperfect causal knowledge: a probabilistic approach »
Amir-Hossein Karimi · Julius von Kügelgen · Bernhard Schölkopf · Isabel Valera -
2020 Poster: Causal analysis of Covid-19 Spread in Germany »
Atalanti Mastakouri · Bernhard Schölkopf -
2020 Spotlight: Algorithmic recourse under imperfect causal knowledge: a probabilistic approach »
Amir-Hossein Karimi · Julius von Kügelgen · Bernhard Schölkopf · Isabel Valera -
2020 Poster: Relative gradient optimization of the Jacobian term in unsupervised deep learning »
Luigi Gresele · Giancarlo Fissore · Adrián Javaloy · Bernhard Schölkopf · Aapo Hyvarinen -
2020 Poster: Multi-Plane Program Induction with 3D Box Priors »
Yikai Li · Jiayuan Mao · Xiuming Zhang · Bill Freeman · Josh Tenenbaum · Noah Snavely · Jiajun Wu -
2020 Poster: Learning Physical Graph Representations from Visual Scenes »
Daniel Bear · Chaofei Fan · Damian Mrowca · Yunzhu Li · Seth Alter · Aran Nayebi · Jeremy Schwartz · Li Fei-Fei · Jiajun Wu · Josh Tenenbaum · Daniel Yamins -
2020 Oral: Learning Physical Graph Representations from Visual Scenes »
Daniel Bear · Chaofei Fan · Damian Mrowca · Yunzhu Li · Seth Alter · Aran Nayebi · Jeremy Schwartz · Li Fei-Fei · Jiajun Wu · Josh Tenenbaum · Daniel Yamins -
2019 : Bernhard Schölkopf »
Bernhard Schölkopf -
2019 : Panel Discussion »
Linda Smith · Josh Tenenbaum · Lisa Anne Hendricks · James McClelland · Timothy Lillicrap · Jesse Thomason · Jason Baldridge · Louis-Philippe Morency -
2019 : Josh Tenenbaum »
Josh Tenenbaum -
2019 : Panel »
Sanja Fidler · Josh Tenenbaum · Tatiana López-Guevara · Danilo Jimenez Rezende · Niloy Mitra -
2019 : Poster Session »
Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou -
2019 : Josh Tenenbaum »
Josh Tenenbaum -
2019 Poster: Write, Execute, Assess: Program Synthesis with a REPL »
Kevin Ellis · Maxwell Nye · Yewen Pu · Felix Sosa · Josh Tenenbaum · Armando Solar-Lezama -
2019 Poster: ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models »
Andrei Barbu · David Mayo · Julian Alverio · William Luo · Christopher Wang · Dan Gutfreund · Josh Tenenbaum · Boris Katz -
2019 Poster: On the Fairness of Disentangled Representations »
Francesco Locatello · Gabriele Abbati · Thomas Rainforth · Stefan Bauer · Bernhard Schölkopf · Olivier Bachem -
2019 Poster: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset »
Muhammad Waleed Gondal · Manuel Wuethrich · Djordje Miladinovic · Francesco Locatello · Martin Breidt · Valentin Volchkov · Joel Akpo · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations »
Kevin Smith · Lingjie Mei · Shunyu Yao · Jiajun Wu · Elizabeth Spelke · Josh Tenenbaum · Tomer Ullman -
2019 Poster: Visual Concept-Metaconcept Learning »
Chi Han · Jiayuan Mao · Chuang Gan · Josh Tenenbaum · Jiajun Wu -
2019 Poster: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2019 Spotlight: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2019 Poster: Perceiving the arrow of time in autoregressive motion »
Kristof Meding · Dominik Janzing · Bernhard Schölkopf · Felix A. Wichmann -
2019 Poster: Selecting causal brain features with a single conditional independence test per feature »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2019 Poster: Kernel Stein Tests for Multiple Model Comparison »
Jen Ning Lim · Makoto Yamada · Bernhard Schölkopf · Wittawat Jitkrittum -
2019 Spotlight: Perceiving the arrow of time in autoregressive motion »
Kristof Meding · Dominik Janzing · Bernhard Schölkopf · Felix A. Wichmann -
2018 : Datasets and Benchmarks for Causal Learning »
Csaba Szepesvari · Isabelle Guyon · Nicolai Meinshausen · David Blei · Elias Bareinboim · Bernhard Schölkopf · Pietro Perona -
2018 : Learning Independent Mechanisms »
Bernhard Schölkopf -
2018 : Opening Remarks: Josh Tenenbaum »
Josh Tenenbaum -
2018 Workshop: Modeling the Physical World: Learning, Perception, and Control »
Jiajun Wu · Kelsey Allen · Kevin Smith · Jessica Hamrick · Emmanuel Dupoux · Marc Toussaint · Josh Tenenbaum -
2018 Poster: Informative Features for Model Comparison »
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton -
2018 Poster: Learning to Reconstruct Shapes from Unseen Classes »
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu -
2018 Poster: Learning to Infer Graphics Programs from Hand-Drawn Images »
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum -
2018 Poster: Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction »
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum -
2018 Oral: Learning to Reconstruct Shapes from Unseen Classes »
Xiuming Zhang · Zhoutong Zhang · Chengkai Zhang · Josh Tenenbaum · Bill Freeman · Jiajun Wu -
2018 Spotlight: Learning to Infer Graphics Programs from Hand-Drawn Images »
Kevin Ellis · Daniel Ritchie · Armando Solar-Lezama · Josh Tenenbaum -
2018 Spotlight: Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction »
Kevin Ellis · Lucas Morales · Mathias Sablé-Meyer · Armando Solar-Lezama · Josh Tenenbaum -
2018 Poster: Visual Object Networks: Image Generation with Disentangled 3D Representations »
Jun-Yan Zhu · Zhoutong Zhang · Chengkai Zhang · Jiajun Wu · Antonio Torralba · Josh Tenenbaum · Bill Freeman -
2018 Poster: Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models »
Alexander Neitz · Giambattista Parascandolo · Stefan Bauer · Bernhard Schölkopf -
2018 Poster: Learning to Share and Hide Intentions using Information Regularization »
DJ Strouse · Max Kleiman-Weiner · Josh Tenenbaum · Matt Botvinick · David Schwab -
2018 Poster: Learning to Exploit Stability for 3D Scene Parsing »
Yilun Du · Zhijian Liu · Hector Basevi · Ales Leonardis · Bill Freeman · Josh Tenenbaum · Jiajun Wu -
2018 Poster: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Spotlight: End-to-End Differentiable Physics for Learning and Control »
Filipe de Avila Belbute Peres · Kevin Smith · Kelsey Allen · Josh Tenenbaum · J. Zico Kolter -
2018 Poster: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding »
Kexin Yi · Jiajun Wu · Chuang Gan · Antonio Torralba · Pushmeet Kohli · Josh Tenenbaum -
2018 Poster: 3D-Aware Scene Manipulation via Inverse Graphics »
Shunyu Yao · Tzu Ming Hsu · Jun-Yan Zhu · Jiajun Wu · Antonio Torralba · Bill Freeman · Josh Tenenbaum -
2018 Spotlight: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding »
Kexin Yi · Jiajun Wu · Chuang Gan · Antonio Torralba · Pushmeet Kohli · Josh Tenenbaum -
2018 Poster: Flexible neural representation for physics prediction »
Damian Mrowca · Chengxu Zhuang · Elias Wang · Nick Haber · Li Fei-Fei · Josh Tenenbaum · Daniel Yamins -
2017 : Panel Discussion »
Matt Botvinick · Emma Brunskill · Marcos Campos · Jan Peters · Doina Precup · David Silver · Josh Tenenbaum · Roy Fox -
2017 : Learn to learn high-dimensional models from few examples »
Josh Tenenbaum -
2017 : Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation »
Alice Oh · Bernhard Schölkopf -
2017 : Welcome: Josh Tenenbaum »
Josh Tenenbaum -
2017 Workshop: Learning Disentangled Features: from Perception to Control »
Emily Denton · Siddharth Narayanaswamy · Tejas Kulkarni · Honglak Lee · Diane Bouchacourt · Josh Tenenbaum · David Pfau -
2017 : Panel: "How can we characterise the landscape of intelligent systems and locate human-like intelligence in it?" »
Josh Tenenbaum · Gary Marcus · Katja Hofmann -
2017 : Joshua Tenenbaum: 'Types of intelligence: why human-like AI is important' »
Josh Tenenbaum -
2017 Spotlight: Shape and Material from Sound »
Zhoutong Zhang · Qiujia Li · Zhengjia Huang · Jiajun Wu · Josh Tenenbaum · Bill Freeman -
2017 Spotlight: Scene Physics Acquisition via Visual De-animation »
Jiajun Wu · Erika Lu · Pushmeet Kohli · Bill Freeman · Josh Tenenbaum -
2017 Poster: Learning to See Physics via Visual De-animation »
Jiajun Wu · Erika Lu · Pushmeet Kohli · Bill Freeman · Josh Tenenbaum -
2017 Poster: Avoiding Discrimination through Causal Reasoning »
Niki Kilbertus · Mateo Rojas Carulla · Giambattista Parascandolo · Moritz Hardt · Dominik Janzing · Bernhard Schölkopf -
2017 Poster: Shape and Material from Sound »
Zhoutong Zhang · Qiujia Li · Zhengjia Huang · Jiajun Wu · Josh Tenenbaum · Bill Freeman -
2017 Poster: MarrNet: 3D Shape Reconstruction via 2.5D Sketches »
Jiajun Wu · Yifan Wang · Tianfan Xue · Xingyuan Sun · Bill Freeman · Josh Tenenbaum -
2017 Poster: Self-Supervised Intrinsic Image Decomposition »
Michael Janner · Jiajun Wu · Tejas Kulkarni · Ilker Yildirim · Josh Tenenbaum -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: AdaGAN: Boosting Generative Models »
Ilya Tolstikhin · Sylvain Gelly · Olivier Bousquet · Carl-Johann SIMON-GABRIEL · Bernhard Schölkopf -
2017 Tutorial: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning »
Josh Tenenbaum · Vikash Mansinghka -
2016 : Datasets, Methodology, and Challenges in Intuitive Physics »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Josh Tenenbaum »
Josh Tenenbaum -
2016 : Reverse engineering human cooperation (or, How to build machines that treat people like people) »
Josh Tenenbaum · Max Kleiman-Weiner -
2016 : Naive Physics 101: A Tutorial »
Emmanuel Dupoux · Josh Tenenbaum -
2016 : Opening Remarks »
Josh Tenenbaum -
2016 Workshop: Intuitive Physics »
Adam Lerer · Jiajun Wu · Josh Tenenbaum · Emmanuel Dupoux · Rob Fergus -
2016 Poster: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation »
Tejas Kulkarni · Karthik Narasimhan · Ardavan Saeedi · Josh Tenenbaum -
2016 Poster: Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels »
Ilya Tolstikhin · Bharath Sriperumbudur · Bernhard Schölkopf -
2016 Poster: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling »
Jiajun Wu · Chengkai Zhang · Tianfan Xue · Bill Freeman · Josh Tenenbaum -
2016 Poster: Sampling for Bayesian Program Learning »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2016 Poster: Consistent Kernel Mean Estimation for Functions of Random Variables »
Carl-Johann Simon-Gabriel · Adam Scibior · Ilya Tolstikhin · Bernhard Schölkopf -
2016 Poster: Probing the Compositionality of Intuitive Functions »
Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman -
2015 Workshop: Black box learning and inference »
Josh Tenenbaum · Jan-Willem van de Meent · Tejas Kulkarni · S. M. Ali Eslami · Brooks Paige · Frank Wood · Zoubin Ghahramani -
2015 : Discussion Panel with Morning Speakers (Day 1) »
Pedro Domingos · Stephen H Muggleton · Rina Dechter · Josh Tenenbaum -
2015 : Cognitive Foundations for Common-Sense Knowledge Representation and Reasoning »
Josh Tenenbaum -
2015 Poster: Softstar: Heuristic-Guided Probabilistic Inference »
Mathew Monfort · Brenden M Lake · Brenden Lake · Brian Ziebart · Patrick Lucey · Josh Tenenbaum -
2015 Poster: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Spotlight: Deep Convolutional Inverse Graphics Network »
Tejas Kulkarni · William Whitney · Pushmeet Kohli · Josh Tenenbaum -
2015 Poster: Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning »
Jiajun Wu · Ilker Yildirim · Joseph Lim · Bill Freeman · Josh Tenenbaum -
2015 Poster: Unsupervised Learning by Program Synthesis »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2014 Workshop: 3rd NIPS Workshop on Probabilistic Programming »
Daniel Roy · Josh Tenenbaum · Thomas Dietterich · Stuart J Russell · YI WU · Ulrik R Beierholm · Alp Kucukelbir · Zenna Tavares · Yura Perov · Daniel Lee · Brian Ruttenberg · Sameer Singh · Michael Hughes · Marco Gaboardi · Alexey Radul · Vikash Mansinghka · Frank Wood · Sebastian Riedel · Prakash Panangaden -
2014 Poster: Kernel Mean Estimation via Spectral Filtering »
Krikamol Muandet · Bharath Sriperumbudur · Bernhard Schölkopf -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2013 Poster: One-shot learning by inverting a compositional causal process »
Brenden M Lake · Russ Salakhutdinov · Josh Tenenbaum -
2013 Poster: Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. »
Michel Besserve · Nikos K Logothetis · Bernhard Schölkopf -
2013 Poster: Causal Inference on Time Series using Restricted Structural Equation Models »
Jonas Peters · Dominik Janzing · Bernhard Schölkopf -
2013 Poster: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2013 Oral: Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs »
Vikash Mansinghka · Tejas D Kulkarni · Yura N Perov · Josh Tenenbaum -
2012 Poster: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Spotlight: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Poster: The representer theorem for Hilbert spaces: a necessary and sufficient condition »
Francesco Dinuzzo · Bernhard Schölkopf -
2011 Workshop: Challenges in Learning Hierarchical Models: Transfer Learning and Optimization »
Quoc V. Le · Marc'Aurelio Ranzato · Russ Salakhutdinov · Josh Tenenbaum · Andrew Y Ng -
2011 Workshop: Philosophy and Machine Learning »
Marcello Pelillo · Joachim M Buhmann · Tiberio Caetano · Bernhard Schölkopf · Larry Wasserman -
2011 Workshop: Cosmology meets Machine Learning »
Michael Hirsch · Sarah Bridle · Bernhard Schölkopf · Phil Marshall · Stefan Harmeling · Mark Girolami -
2011 Poster: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Spotlight: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Invited Talk: From kernels to causal inference »
Bernhard Schölkopf -
2011 Poster: Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance »
Peter Gehler · Carsten Rother · Martin Kiefel · Lumin Zhang · Bernhard Schölkopf -
2011 Poster: Causal Discovery with Cyclic Additive Noise Models »
Joris M Mooij · Dominik Janzing · Tom Heskes · Bernhard Schölkopf -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Invited Talk: How to Grow a Mind: Statistics, Structure and Abstraction »
Josh Tenenbaum -
2010 Spotlight: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Poster: Dynamic Infinite Relational Model for Time-varying Relational Data Analysis »
Katsuhiko Ishiguro · Tomoharu Iwata · Naonori Ueda · Josh Tenenbaum -
2010 Poster: Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake »
Stefan Harmeling · Michael Hirsch · Bernhard Schölkopf -
2010 Poster: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Poster: Probabilistic latent variable models for distinguishing between cause and effect »
Joris M Mooij · Oliver Stegle · Dominik Janzing · Kun Zhang · Bernhard Schölkopf -
2010 Poster: Nonparametric Bayesian Policy Priors for Reinforcement Learning »
Finale P Doshi-Velez · David Wingate · Nicholas Roy · Josh Tenenbaum -
2009 Workshop: Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain »
Noah Goodman · Edward Vul · Tom Griffiths · Josh Tenenbaum -
2009 Workshop: Connectivity Inference in Neuroimaging »
Karl Friston · Moritz Grosse-Wentrup · Uta Noppeney · Bernhard Schölkopf -
2009 Workshop: Analyzing Networks and Learning With Graphs »
Edo M Airoldi · Jure Leskovec · Jon Kleinberg · Josh Tenenbaum -
2009 Poster: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Poster: Help or Hinder: Bayesian Models of Social Goal Inference »
Tomer D Ullman · Chris L Baker · Owen Macindoe · Owain Evans · Noah Goodman · Josh Tenenbaum -
2009 Spotlight: Perceptual Multistability as Markov Chain Monte Carlo Inference »
Samuel J Gershman · Edward Vul · Josh Tenenbaum -
2009 Oral: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Oral: Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model »
Edward Vul · Michael C Frank · George Alvarez · Josh Tenenbaum -
2009 Poster: Modelling Relational Data using Bayesian Clustered Tensor Factorization »
Ilya Sutskever · Russ Salakhutdinov · Josh Tenenbaum -
2008 Workshop: Probabilistic Programming: Universal Languages, Systems and Applications »
Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Workshop: Causality: objectives and assessment »
Isabelle Guyon · Dominik Janzing · Bernhard Schölkopf -
2008 Mini Symposium: Computational Photography »
Bill Freeman · Bernhard Schölkopf -
2008 Poster: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Oral: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Poster: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Poster: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Spotlight: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Spotlight: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Spotlight: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis »
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch -
2008 Poster: Diffeomorphic Dimensionality Reduction »
Christian Walder · Bernhard Schölkopf -
2007 Workshop: The Grammar of Vision: Probabilistic Grammar-Based Models for Visual Scene Understanding and Object Categorization »
Virginia Savova · Josh Tenenbaum · Leslie Kaelbling · Alan Yuille -
2007 Spotlight: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Spotlight: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Poster: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: A Bayesian Framework for Cross-Situational Word-Learning »
Michael C Frank · Noah Goodman · Josh Tenenbaum -
2007 Poster: A complexity measure for intuitive theories »
Charles Kemp · Noah Goodman · Josh Tenenbaum -
2007 Spotlight: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions »
Christian Walder · Bernhard Schölkopf · Olivier Chapelle -
2006 Poster: Learning Dense 3D Correspondence »
Florian Steinke · Bernhard Schölkopf · Volker Blanz -
2006 Poster: A Local Learning Approach for Clustering »
Mingrui Wu · Bernhard Schölkopf -
2006 Poster: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Poster: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Talk: Learning annotated hierarchies from relational data »
Daniel Roy · Charles Kemp · Vikash Mansinghka · Josh Tenenbaum -
2006 Spotlight: Multiple timescales and uncertainty in motor adaptation »
Konrad P Kording · Josh Tenenbaum · Reza Shadmehr -
2006 Talk: Combining causal and similarity-based reasoning »
Charles Kemp · Patrick Shafto · Allison Berke · Josh Tenenbaum -
2006 Poster: Causal inference in sensorimotor integration »
Konrad P Kording · Josh Tenenbaum -
2006 Poster: A Nonparametric Approach to Bottom-Up Visual Saliency »
Wolf Kienzle · Felix A Wichmann · Bernhard Schölkopf · Matthias Franz -
2006 Poster: Learning with Hypergraphs: Clustering, Classification, and Embedding »
Denny Zhou · Jiayuan Huang · Bernhard Schölkopf -
2006 Tutorial: Bayesian Models of Human Learning and Inference »
Josh Tenenbaum