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Probabilistic models and approximate inference algorithms are powerful, widely-used tools, central to fields ranging from machine learning to robotics to genetics. However, even simple variations on models and algorithms from the standard machine learning toolkit can be difficult and time-consuming to design, specify, analyze, implement, optimize and debug. The emerging field of probabilistic programming aims to address these challenges by developing formal languages and software systems that integrate key ideas from probabilistic modeling and inference with programming languages and Turing-universal computation.
Over the past two years, the field has rapidly grown and begun to mature. Systems have developed enough that they are seeing significant adoption in real-world applications while also highlighting the need for research in profiling, testing, verification and debugging. New academic and industrial languages have been developed, yielding new applications as well as new technical problems. General-purpose probabilistic programming languages have emerged, complementing previous work focused on specific domains; some offer programmable inference so that experts can take over where automatic techniques fail. The field is has also begun to explore new AI architectures that perform probabilistic reasoning or hierarchical Bayesian approaches for inductive learning over rich data structures and software simulators.
This workshop will survey recent progress, emphasizing results from the ongoing DARPA PPAML program on probabilistic programming. A key theme will be articulating formal connections between probabilistic programming and other fields central to the NIPS community.
Author Information
Daniel Roy (Univ of Toronto & Vector)
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).
Thomas Dietterich (Oregon State University)
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces. Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.
Stuart J Russell (UC Berkeley)
YI WU (Shanghai Qi Zhi Institute & Tsinghua University)
Ulrik R Beierholm (University of Birmingham)
Alp Kucukelbir (Fero Labs / Columbia University)
Zenna Tavares (Basis Research Institute & Columbia University)
Yura Perov (Babylon)
Daniel Lee (Columbia University)
Brian Ruttenberg (Charles River Analytics)
Sameer Singh (University of California, Irvine)
Michael Hughes (Tufts University)
Marco Gaboardi (Harvard University and University of Dundee)
Alexey Radul (Massachusetts Institute of Technology)
Vikash Mansinghka (Massachusetts Institute of Technology)
Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project. Vikash holds S.B. degrees in Mathematics and in Computer Science from MIT, as well as an M.Eng. in Computer Science and a PhD in Computation. He also held graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR. He served on DARPA’s Information Science and Technology advisory board from 2010-2012, and currently serves on the editorial boards for the Journal of Machine Learning Research and the journal Statistics and Computation. He was an advisor to Google DeepMind and has co-founded two AI-related startups, one acquired and one currently operational.
Frank Wood (University of British Columbia)
Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that he was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. He holds a PhD from Brown University (â07) and BS from Cornell University (â96), both in computer science. Dr. Wood is the original architect of both the Anglican and Probabilistic-C probabilistic programming systems. He conducts AI-driven research at the boundary of probabilistic programming, Bayesian modeling, and Monte Carlo methods. Dr. Wood holds 6 patents, has authored over 50 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to AOL/Time Warner and served as CEO of Interfolio.
Sebastian Riedel (DeepMind / UCL)
Prakash Panangaden (McGill University, Montreal)
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Kai Xu · Akash Srivastava · Dan Gutfreund · Felix Sosa · Tomer Ullman · Josh Tenenbaum · Charles Sutton -
2021 Poster: Towards a Unified Information-Theoretic Framework for Generalization »
Mahdi Haghifam · Gintare Karolina Dziugaite · Shay Moran · Dan Roy -
2021 Poster: PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning »
Yining Hong · Li Yi · Josh Tenenbaum · Antonio Torralba · Chuang Gan -
2021 Poster: Noether Networks: meta-learning useful conserved quantities »
Ferran Alet · Dylan Doblar · Allan Zhou · Josh Tenenbaum · Kenji Kawaguchi · Chelsea Finn -
2021 Poster: 3DP3: 3D Scene Perception via Probabilistic Programming »
Nishad Gothoskar · Marco Cusumano-Towner · Ben Zinberg · Matin Ghavamizadeh · Falk Pollok · Austin Garrett · Josh Tenenbaum · Dan Gutfreund · Vikash Mansinghka -
2021 Poster: Dynamical Wasserstein Barycenters for Time-series Modeling »
Kevin Cheng · Shuchin Aeron · Michael Hughes · Eric L Miller -
2021 Poster: Counterfactual Explanations Can Be Manipulated »
Dylan Slack · Anna Hilgard · Himabindu Lakkaraju · Sameer Singh -
2021 Poster: MICo: Improved representations via sampling-based state similarity for Markov decision processes »
Pablo Samuel Castro · Tyler Kastner · Prakash Panangaden · Mark Rowland -
2021 : ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation »
Chuang Gan · Jeremy Schwartz · Seth Alter · Damian Mrowca · Martin Schrimpf · James Traer · Julian De Freitas · Jonas Kubilius · Abhishek Bhandwaldar · Nick Haber · Megumi Sano · Kuno Kim · Elias Wang · Michael Lingelbach · Aidan Curtis · Kevin Feigelis · Daniel Bear · Dan Gutfreund · David Cox · Antonio Torralba · James J DiCarlo · Josh Tenenbaum · Josh McDermott · Dan Yamins -
2020 : Mini-panel discussion 3 - Prioritizing Real World RL Challenges »
Chelsea Finn · Thomas Dietterich · Angela Schoellig · Anca Dragan · Anusha Nagabandi · Doina Precup -
2020 : Keynote: Tom Diettrich »
Thomas Dietterich -
2020 Workshop: Workshop on Computer Assisted Programming (CAP) »
Augustus Odena · Charles Sutton · Nadia Polikarpova · Josh Tenenbaum · Armando Solar-Lezama · Isil Dillig -
2020 : Invited Talk: Mike Hughes - The Case for Prediction Constrained Training »
Michael Hughes -
2020 : Invited Talk: Growing into intelligence the human way: What do we start with, and how do we learn the rest? »
Josh Tenenbaum -
2020 : Panel Discussion »
Jessica Hamrick · Klaus Greff · Michelle A. Lee · Irina Higgins · Josh Tenenbaum -
2020 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
Veronika Thost · Kartik Talamadupula · Vivek Srikumar · Chenwei Zhang · Josh Tenenbaum -
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: Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel »
Stanislav Fort · Gintare Karolina Dziugaite · Mansheej Paul · Sepideh Kharaghani · Daniel Roy · Surya Ganguli -
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 Poster: Adaptive Gradient Quantization for Data-Parallel SGD »
Fartash Faghri · Iman Tabrizian · Ilia Markov · Dan Alistarh · Daniel Roy · Ali Ramezani-Kebrya -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh -
2020 Poster: Multi-Task Reinforcement Learning with Soft Modularization »
Ruihan Yang · Huazhe Xu · YI WU · Xiaolong Wang -
2020 Poster: Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms »
Mahdi Haghifam · Jeffrey Negrea · Ashish Khisti · Daniel Roy · Gintare Karolina Dziugaite -
2020 Poster: In search of robust measures of generalization »
Gintare Karolina Dziugaite · Alexandre Drouin · Brady Neal · Nitarshan Rajkumar · Ethan Caballero · Linbo Wang · Ioannis Mitliagkas · Daniel Roy -
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 -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh -
2019 : AI and Sustainable Development »
Fei Fang · Carla Gomes · Miguel Luengo-Oroz · Thomas Dietterich · Julien Cornebise -
2019 : Automated Quality Control for a Weather Sensor Network »
Thomas Dietterich -
2019 : Poster and Coffee Break 2 »
Karol Hausman · Kefan Dong · Ken Goldberg · Lihong Li · Lin Yang · Lingxiao Wang · Lior Shani · Liwei Wang · Loren Amdahl-Culleton · Lucas Cassano · Marc Dymetman · Marc Bellemare · Marcin Tomczak · Margarita Castro · Marius Kloft · Marius-Constantin Dinu · Markus Holzleitner · Martha White · Mengdi Wang · Michael Jordan · Mihailo Jovanovic · Ming Yu · Minshuo Chen · Moonkyung Ryu · Muhammad Zaheer · Naman Agarwal · Nan Jiang · Niao He · Nikolaus Yasui · Nikos Karampatziakis · Nino Vieillard · Ofir Nachum · Olivier Pietquin · Ozan Sener · Pan Xu · Parameswaran Kamalaruban · Paul Mineiro · Paul Rolland · Philip Amortila · Pierre-Luc Bacon · Prakash Panangaden · Qi Cai · Qiang Liu · Quanquan Gu · Raihan Seraj · Richard Sutton · Rick Valenzano · Robert Dadashi · Rodrigo Toro Icarte · Roshan Shariff · Roy Fox · Ruosong Wang · Saeed Ghadimi · Samuel Sokota · Sean Sinclair · Sepp Hochreiter · Sergey Levine · Sergio Valcarcel Macua · Sham Kakade · Shangtong Zhang · Sheila McIlraith · Shie Mannor · Shimon Whiteson · Shuai Li · Shuang Qiu · Wai Lok Li · Siddhartha Banerjee · Sitao Luan · Tamer Basar · Thinh Doan · Tianhe Yu · Tianyi Liu · Tom Zahavy · Toryn Klassen · Tuo Zhao · Vicenç Gómez · Vincent Liu · Volkan Cevher · Wesley Suttle · Xiao-Wen Chang · Xiaohan Wei · Xiaotong Liu · Xingguo Li · Xinyi Chen · Xingyou Song · Yao Liu · YiDing Jiang · Yihao Feng · Yilun Du · Yinlam Chow · Yinyu Ye · Yishay Mansour · · Yonathan Efroni · Yongxin Chen · Yuanhao Wang · Bo Dai · Chen-Yu Wei · Harsh Shrivastava · Hongyang Zhang · Qinqing Zheng · SIDDHARTHA SATPATHI · Xueqing Liu · Andreu Vall -
2019 : Lunch break & Poster session »
Breandan Considine · Michael Innes · Du Phan · Dougal Maclaurin · Robin Manhaeve · Alexey Radul · Shashi Gowda · Ekansh Sharma · Eli Sennesh · Maxim Kochurov · Gordon Plotkin · Thomas Wiecki · Navjot Kukreja · Chung-chieh Shan · Matthew Johnson · Dan Belov · Neeraj Pradhan · Wannes Meert · Angelika Kimmig · Luc De Raedt · Brian Patton · Matthew Hoffman · Rif A. Saurous · Daniel Roy · Eli Bingham · Martin Jankowiak · Colin Carroll · Junpeng Lao · Liam Paull · Martin Abadi · Angel Rojas Jimenez · JP Chen -
2019 : Lunch Break and Posters »
Xingyou Song · Elad Hoffer · Wei-Cheng Chang · Jeremy Cohen · Jyoti Islam · Yaniv Blumenfeld · Andreas Madsen · Jonathan Frankle · Sebastian Goldt · Satrajit Chatterjee · Abhishek Panigrahi · Alex Renda · Brian Bartoldson · Israel Birhane · Aristide Baratin · Niladri Chatterji · Roman Novak · Jessica Forde · YiDing Jiang · Yilun Du · Linara Adilova · Michael Kamp · Berry Weinstein · Itay Hubara · Tal Ben-Nun · Torsten Hoefler · Daniel Soudry · Hsiang-Fu Yu · Kai Zhong · Yiming Yang · Inderjit Dhillon · Jaime Carbonell · Yanqing Zhang · Dar Gilboa · Johannes Brandstetter · Alexander R Johansen · Gintare Karolina Dziugaite · Raghav Somani · Ari Morcos · Freddie Kalaitzis · Hanie Sedghi · Lechao Xiao · John Zech · Muqiao Yang · Simran Kaur · Qianli Ma · Yao-Hung Hubert Tsai · Ruslan Salakhutdinov · Sho Yaida · Zachary Lipton · Daniel Roy · Michael Carbin · Florent Krzakala · Lenka Zdeborová · Guy Gur-Ari · Ethan Dyer · Dilip Krishnan · Hossein Mobahi · Samy Bengio · Behnam Neyshabur · Praneeth Netrapalli · Kris Sankaran · Julien Cornebise · Yoshua Bengio · Vincent Michalski · Samira Ebrahimi Kahou · Md Rifat Arefin · Jiri Hron · Jaehoon Lee · Jascha Sohl-Dickstein · Samuel Schoenholz · David Schwab · Dongyu Li · Sang Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 : Poster Spotlight 2 »
Aaron Sidford · Mengdi Wang · Lin Yang · Yinyu Ye · Zuyue Fu · Zhuoran Yang · Yongxin Chen · Zhaoran Wang · Ofir Nachum · Bo Dai · Ilya Kostrikov · Dale Schuurmans · Ziyang Tang · Yihao Feng · Lihong Li · Denny Zhou · Qiang Liu · Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Simon Du · Sham Kakade · Ruosong Wang · Minshuo Chen · Tianyi Liu · Xingguo Li · Zhaoran Wang · Tuo Zhao · Philip Amortila · Doina Precup · Prakash Panangaden · Marc Bellemare -
2019 Workshop: Machine Learning with Guarantees »
Ben London · Gintare Karolina Dziugaite · Daniel Roy · Thorsten Joachims · Aleksander Madry · John Shawe-Taylor -
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 : Posters »
Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner -
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 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
Veronika Thost · Christian Muise · Kartik Talamadupula · Sameer Singh · Christopher Ré -
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: Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates »
Jeffrey Negrea · Mahdi Haghifam · Gintare Karolina Dziugaite · Ashish Khisti · Daniel Roy -
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: Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes »
Jun Yang · Shengyang Sun · Daniel Roy -
2019 Poster: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2019 Demonstration: AllenNLP Interpret: Explaining Predictions of NLP Models »
Jens Tuyls · Eric Wallace · Matt Gardner · Junlin Wang · Sameer Singh · Sanjay Subramanian -
2019 Spotlight: Finding Friend and Foe in Multi-Agent Games »
Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum -
2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Chen -
2018 : Opening Remarks: Josh Tenenbaum »
Josh Tenenbaum -
2018 : TBC 1 »
Frank Wood -
2018 Workshop: All of Bayesian Nonparametrics (Especially the Useful Bits) »
Diana Cai · Trevor Campbell · Michael Hughes · Tamara Broderick · Nick Foti · Sinead Williamson -
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: 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: Meta-Learning MCMC Proposals »
Tongzhou Wang · YI WU · Dave Moore · Stuart Russell -
2018 Poster: Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making »
Nishant Desai · Andrew Critch · Stuart J Russell -
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 Poster: Data-dependent PAC-Bayes priors via differential privacy »
Gintare Karolina Dziugaite · Daniel Roy -
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 : Daniel Roy - Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes »
Daniel Roy -
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 : Welcome: Josh Tenenbaum »
Josh Tenenbaum -
2017 Workshop: Workshop on Prioritising Online Content »
John Shawe-Taylor · Massimiliano Pontil · Nicolò Cesa-Bianchi · Emine Yilmaz · Chris Watkins · Sebastian Riedel · Marko Grobelnik -
2017 Workshop: Learning Disentangled Features: from Perception to Control »
Emily Denton · Siddharth Narayanaswamy · Tejas Kulkarni · Honglak Lee · Diane Bouchacourt · Josh Tenenbaum · David Pfau -
2017 : Reading and Reasoning with Neural Program Interpreters »
Sebastian Riedel -
2017 : Coffee break and Poster Session I »
Nishith Khandwala · Steve Gallant · Gregory Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · William Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · Chen Fang · Sibt ul Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavier Giro-i-Nieto · David Kale · Michael Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Mayank Srivastava · Amy Jin · Heliodoro Tejeda Lemus · Priyadip Ray · Tamas Madl · Joseph Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros -
2017 Workshop: Deep Learning for Physical Sciences »
Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood -
2017 Workshop: Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? »
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka -
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: Shape and Material from Sound »
Zhoutong Zhang · Qiujia Li · Zhengjia Huang · Jiajun Wu · Josh Tenenbaum · Bill Freeman -
2017 Poster: End-to-End Differentiable Proving »
Tim Rocktäschel · Sebastian Riedel -
2017 Poster: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments »
Ryan Lowe · YI WU · Aviv Tamar · Jean Harb · OpenAI Pieter Abbeel · Igor Mordatch -
2017 Poster: Inverse Reward Design »
Dylan Hadfield-Menell · Smitha Milli · Pieter Abbeel · Stuart J Russell · Anca Dragan -
2017 Oral: Inverse Reward Design »
Dylan Hadfield-Menell · Smitha Milli · Pieter Abbeel · Stuart J Russell · Anca Dragan -
2017 Oral: End-to-end Differentiable Proving »
Tim Rocktäschel · Sebastian Riedel -
2017 Poster: MarrNet: 3D Shape Reconstruction via 2.5D Sketches »
Jiajun Wu · Yifan Wang · Tianfan Xue · Xingyuan Sun · Bill Freeman · Josh Tenenbaum -
2017 Poster: AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms »
Marco Cusumano-Towner · Vikash Mansinghka -
2017 Poster: Self-Supervised Intrinsic Image Decomposition »
Michael Janner · Jiajun Wu · Tejas Kulkarni · Ilker Yildirim · Josh Tenenbaum -
2017 Poster: Learning Disentangled Representations with Semi-Supervised Deep Generative Models »
Siddharth Narayanaswamy · Brooks Paige · Jan-Willem van de Meent · Alban Desmaison · Noah Goodman · Pushmeet Kohli · Frank Wood · Philip Torr -
2017 Tutorial: Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning »
Josh Tenenbaum · Vikash Mansinghka -
2016 : Automated Data Cleaning via Multi-View Anomaly Detection »
Thomas Dietterich -
2016 Workshop: Neural Abstract Machines & Program Induction »
Matko Bošnjak · Nando de Freitas · Tejas Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel -
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 Workshop: Practical Bayesian Nonparametrics »
Nick Foti · Tamara Broderick · Trevor Campbell · Michael Hughes · Jeffrey Miller · Aaron Schein · Sinead Williamson · Yanxun Xu -
2016 Poster: Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation »
Tejas Kulkarni · Karthik Narasimhan · Ardavan Saeedi · Josh Tenenbaum -
2016 Poster: A Probabilistic Programming Approach To Probabilistic Data Analysis »
Feras Saad · Vikash Mansinghka -
2016 Poster: Bayesian Optimization for Probabilistic Programs »
Thomas Rainforth · Tuan Anh Le · Jan-Willem van de Meent · Michael A Osborne · Frank Wood -
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: Measuring the reliability of MCMC inference with bidirectional Monte Carlo »
Roger Grosse · Siddharth Ancha · Daniel Roy -
2016 Poster: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Oral: Value Iteration Networks »
Aviv Tamar · Sergey Levine · Pieter Abbeel · YI WU · Garrett Thomas -
2016 Poster: Cooperative Inverse Reinforcement Learning »
Dylan Hadfield-Menell · Stuart J Russell · Pieter Abbeel · Anca Dragan -
2016 Poster: Sampling for Bayesian Program Learning »
Kevin Ellis · Armando Solar-Lezama · Josh Tenenbaum -
2016 Poster: Probing the Compositionality of Intuitive Functions »
Eric Schulz · Josh Tenenbaum · David Duvenaud · Maarten Speekenbrink · Samuel J Gershman -
2015 : Automatic Differentiation Variational Inference in Stan »
Alp Kucukelbir -
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 Workshop: Machine Learning Systems »
Alex Beutel · Tianqi Chen · Sameer Singh · Elaine Angelino · Markus Weimer · Joseph Gonzalez -
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 Workshop: Advances in Approximate Bayesian Inference »
Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei -
2015 Poster: Softstar: Heuristic-Guided Probabilistic Inference »
Mathew Monfort · Brenden M Lake · Brenden Lake · Brian Ziebart · Patrick Lucey · Josh Tenenbaum -
2015 Poster: Gaussian Process Random Fields »
Dave Moore · Stuart J Russell -
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 -
2015 Poster: Automatic Variational Inference in Stan »
Alp Kucukelbir · Rajesh Ranganath · Andrew Gelman · David Blei -
2015 Spotlight: Automatic Variational Inference in Stan »
Alp Kucukelbir · Rajesh Ranganath · Andrew Gelman · David Blei -
2015 Poster: Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models »
Michael Hughes · William Stephenson · Erik Sudderth -
2015 Poster: Basis refinement strategies for linear value function approximation in MDPs »
Gheorghe Comanici · Doina Precup · Prakash Panangaden -
2015 Tutorial: Probabilistic Programming »
Frank Wood -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Workshop: From Bad Models to Good Policies (Sequential Decision Making under Uncertainty) »
Odalric-Ambrym Maillard · Timothy A Mann · Shie Mannor · Jeremie Mary · Laurent Orseau · Thomas Dietterich · Ronald Ortner · Peter Grünwald · Joelle Pineau · Raphael Fonteneau · Georgios Theocharous · Esteban D Arcaute · Christos Dimitrakakis · Nan Jiang · Doina Precup · Pierre-Luc Bacon · Marek Petrik · Aviv Tamar -
2014 Poster: Algorithm selection by rational metareasoning as a model of human strategy selection »
Falk Lieder · Dillon Plunkett · Jessica B Hamrick · Stuart J Russell · Nicholas Hay · Tom Griffiths -
2014 Poster: Gibbs-type Indian Buffet Processes »
Creighton Heaukulani · Daniel Roy -
2014 Poster: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Demonstration: A Visual and Interactive IDE for Probabilistic Programming »
Sameer Singh · Luke Hewitt · Tim Rocktäschel · Sebastian Riedel -
2014 Oral: Asynchronous Anytime Sequential Monte Carlo »
Brooks Paige · Frank Wood · Arnaud Doucet · Yee Whye Teh -
2014 Poster: Mondrian Forests: Efficient Online Random Forests »
Balaji Lakshminarayanan · Daniel Roy · Yee Whye Teh -
2013 Workshop: Machine Learning for Sustainability »
Edwin Bonilla · Thomas Dietterich · Theodoros Damoulas · Andreas Krause · Daniel Sheldon · Iadine Chades · J. Zico Kolter · Bistra Dilkina · Carla Gomes · Hugo P Simao -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré -
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 Poster: One-shot learning by inverting a compositional causal process »
Brenden M Lake · Russ Salakhutdinov · Josh Tenenbaum -
2013 Poster: Multilinear Dynamical Systems for Tensor Time Series »
Mark Rogers · Lei Li · Stuart J Russell -
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 -
2013 Poster: Memoized Online Variational Inference for Dirichlet Process Mixture Models »
Michael Hughes · Erik Sudderth -
2013 Session: Session Chair »
Daniel Roy -
2013 Session: Tutorial Session B »
Daniel Roy -
2012 Workshop: Big Learning : Algorithms, Systems, and Tools »
Sameer Singh · John Duchi · Yucheng Low · Joseph E Gonzalez -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Workshop: Human Computation for Science and Computational Sustainability »
Theodoros Damoulas · Thomas Dietterich · Edith Law · Serge Belongie -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Poster: Probabilistic Topic Coding for Superset Label Learning »
Liping Liu · Thomas Dietterich -
2012 Poster: Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data »
Michael Hughes · Emily Fox · Erik Sudderth -
2012 Invited Talk: Challenges for Machine Learning in Computational Sustainability »
Thomas Dietterich -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
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: Machine Learning for Sustainability »
Thomas Dietterich · J. Zico Kolter · Matthew A Brown -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Poster: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2011 Poster: Hierarchically Supervised Latent Dirichlet Allocation »
Adler J Perotte · Frank Wood · Noemie Elhadad · Nicholas Bartlett -
2011 Poster: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Spotlight: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2011 Spotlight: Learning to Learn with Compound HD Models »
Russ Salakhutdinov · Josh Tenenbaum · Antonio Torralba -
2011 Poster: Collective Graphical Models »
Daniel Sheldon · Thomas Dietterich -
2011 Poster: Inverting Grice's Maxims to Learn Rules from Natural Language Extractions »
M. Shahed Sorower · Thomas Dietterich · Janardhan Rao Doppa · Walker Orr · Prasad Tadepalli · Xiaoli Fern -
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 Poster: Dynamic Infinite Relational Model for Time-varying Relational Data Analysis »
Katsuhiko Ishiguro · Tomoharu Iwata · Naonori Ueda · Josh Tenenbaum -
2010 Spotlight: Probabilistic Deterministic Infinite Automata »
David Pfau · Nicholas Bartlett · Frank Wood -
2010 Poster: Global seismic monitoring as probabilistic inference »
Nimar Arora · Stuart J Russell · Paul Kidwell · Erik Sudderth -
2010 Poster: Probabilistic Deterministic Infinite Automata »
David Pfau · Nicholas Bartlett · Frank Wood -
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: Analyzing Networks and Learning With Graphs »
Edo M Airoldi · Jure Leskovec · Jon Kleinberg · Josh Tenenbaum -
2009 Mini Symposium: Machine Learning for Sustainability »
J. Zico Kolter · Thomas Dietterich · Andrew Y Ng -
2009 Poster: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs »
Andrew McCallum · Karl Schultz · Sameer Singh -
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 Demonstration: Monte: An Interactive Ssytem for Massively Parallel Probabilistic Programming »
Vikash Mansinghka -
2009 Poster: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum -
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 Spotlight: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference »
Michael Wick · Khashayar Rohanimanesh · Sameer Singh · Andrew McCallum -
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 Demonstration: The IID: A Natively Probabilistic Reconfigurable Computer »
Vikash Mansinghka -
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 Oral: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Poster: Characterizing neural dependencies with Poisson copula models »
Pietro Berkes · Frank Wood · Jonathan W Pillow -
2008 Poster: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Spotlight: Characterizing neural dependencies with Poisson copula models »
Pietro Berkes · Frank Wood · Jonathan W Pillow -
2008 Poster: Probabilistic detection of short events, with application to critical care monitoring »
Norm Aleks · Stuart J Russell · Michael G Madden · Diane Morabito · Geoffrey T Manley · Kristan Staudenmayer · Mitchell Cohen -
2008 Poster: Dependent Dirichlet Process Spike Sorting »
Jan Gasthaus · Frank Wood · Dilan Gorur · Yee Whye Teh -
2008 Poster: Bounding Performance Loss in Approximate MDP Homomorphisms »
Doina Precup · Jonathan Taylor Taylor · Prakash Panangaden -
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 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
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 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2006 Poster: Particle Filtering for Nonparametric Bayesian Matrix Factorization »
Frank Wood · Tom Griffiths -
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 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 Demonstration: Blaise: A System for Interactive Development of High Performance Inference Algorithms »
Keith Bonawitz · Vikash Mansinghka -
2006 Tutorial: Bayesian Models of Human Learning and Inference »
Josh Tenenbaum