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Author Information
Breandan Considine (McGill University)
Michael Innes (Julia Computing)
Du Phan (Independent Researcher)
Dougal Maclaurin (Google)
Robin Manhaeve (KU Leuven)
Alexey Radul (Google)
Shashi Gowda (Massachusetts Institute of Technology)
Ekansh Sharma (University of Toronto)
Eli Sennesh (Northeastern University)
I work in the Probabilistic Modeling Lab at Northeastern University’s CCIS, as well as the Interdisciplinary Affective Science Laboratory. We use the tools of machine learning, statistics, and computation to study the deep questions at the heart of neuroscience, cognition, and agency. We’re making the world a better place through probabilistic programming!
Maxim Kochurov (Samsung)
Gordon Plotkin (Google)
Thomas Wiecki (Quantopian Inc.)
Navjot Kukreja (Imperial College London)
Chung-chieh Shan (Indiana University)
Matthew Johnson (Google Brain)
Matt Johnson is a research scientist at Google Brain interested in software systems powering machine learning research. He is the tech lead for JAX, a system for composable function transformations in Python. He was a postdoc at Harvard University with Ryan Adams, working on composing graphical models with neural networks and applications in neurobiology. His Ph.D. is from MIT, where he worked with Alan Willsky on Bayesian nonparametrics, time series models, and scalable inference.
Dan Belov (DeepMind)
Neeraj Pradhan (Uber AI Labs)
Wannes Meert (K.U.Leuven)
Angelika Kimmig (Cardiff University)
Luc De Raedt (KU Leuven)
Brian Patton (Google)
Matthew Hoffman (Google)
Rif A. Saurous (Google)
Daniel Roy (Univ of Toronto & Vector)
Eli Bingham (Uber AI Labs)
Martin Jankowiak (Uber AI Labs)
Colin Carroll (PyMC3)
Colin Carroll is a data scientist in Cambridge, MA interested in statistical computing, particularly as related to Bayesian methods. He is a core contributor to PyMC3, a Python library for Bayesian modelling and inference, as well as ArviZ, a Bayesian visualization and diagnostic library. He received his PhD in mathematics from Rice University, where he researched geometric measure theory.
Junpeng Lao (Google)
Liam Paull (Université de Montréal)
Martin Abadi (Google)
Angel Rojas Jimenez (Yachay Tech)
I obtained my B.Sc. in Mathematics at Yachay Tech in Imbabura, Ecuador. In the last semester of my undergraduate career prof. Griewank and I developed SALGO - Successive Abs-Linearized Global Optimization algorithm and its application to Neural Network (NN) training. Nowadays, we obtain three different optimization strategies for SALGO called TOAST, MILOP, and CGD. Also, we obtained a 92% accuracy for the MNIST digit recognition problem implementing SALGO-TOAST for a single-layer NN. Moreover, we have generalized the prediction model of a NN with hinge (or ReLU) activation functions with what we call GALL - Generalized Abs-Linear Learning.
JP Chen (Uber AI)
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2020 : Conclusions and Wrap up »
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2020 : Interviews with winners »
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2020 : Live robot competition (LF, LFP, lFVM) »
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2020 Poster: Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel »
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2020 Poster: Neural Topographic Factor Analysis for fMRI Data »
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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 Keun Choe · Henning Petzka · Ashish Verma · Zhichao Lin · Cristian Sminchisescu -
2019 : Coffee + Posters »
Changhao Chen · Nils Gählert · Edouard Leurent · Johannes Lehner · Apratim Bhattacharyya · Harkirat Singh Behl · Teck Yian Lim · Shiho Kim · Jelena Novosel · Błażej Osiński · Arindam Das · Ruobing Shen · Jeffrey Hawke · Joachim Sicking · Babak Shahian Jahromi · Theja Tulabandhula · Claudio Michaelis · Evgenia Rusak · WENHANG BAO · Hazem Rashed · JP Chen · Amin Ansari · Jaekwang Cha · Mohamed Zahran · Daniele Reda · Jinhyuk Kim · Kim Dohyun · Ho Suk · Junekyo Jhung · Alexander Kister · Matthias Fahrland · Adam Jakubowski · Piotr Miłoś · Jean Mercat · Bruno Arsenali · Silviu Homoceanu · Xiao-Yang Liu · Philip Torr · Ahmad El Sallab · Ibrahim Sobh · Anurag Arnab · Krzysztof Galias -
2019 Workshop: Machine Learning with Guarantees »
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2019 : Phenotype »
Nir HaCohen · David Reshef · Matthew Johnson · Sam Morris · Aurel Nagy · Gokcen Eraslan · Meromit Singer · Eliezer Van Allen · Smita Krishnaswamy · Casey Greene · Scott Linderman · Alexander Wiltschko · Dylan Kotliar · James Zou · Brendan Bulik-Sullivan -
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2019 : Poster Spotlights A (23 posters) »
DongHa Bahn · Xiaoran Xu · Shih-Chieh Su · Daniel Cunnington · Wonseok Hwang · Sarthak Dash · Alberto Camacho · Theodoros Salonidis · Shiyang Li · Yuyu Zhang · Habibeh Naderi · Zhe Zeng · Pasha Khosravi · Pedro Colon-Hernandez · Dimitris Diochnos · David Windridge · Robin Manhaeve · Vaishak Belle · Brendan Juba · Naveen Sundar Govindarajulu · Joe Bockhorst -
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2018 : Live competition The AI Driving Olympics: Containerization »
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2018 : Posters (all accepted papers) + Break »
Jianyu Wang · Denis Gudovskiy · Ziheng Jiang · Michael Kaufmann · Andreea Anghel · James Bradbury · Nikolas Ioannou · Nitin Agrawal · Emma Tosch · Gyeongin Yu · Keno Fischer · Jarrett Revels · Giuseppe Siracusano · Yaoqing Yang · Jeff Johnson · Yang You · Hector Yuen · Chris Ying · Honglei Liu · Nikoli Dryden · Xiangxi Mo · Yangzihao Wang · Amit Juneja · Micah Smith · Qian Yu · pramod gupta · Deepak Narayanan · Keshav Santhanam · Tim Capes · Abdul Dakkak · Norman Mu · Ke Deng · Liam Li · Joao Carreira · Luis Remis · Deepti Raghavan · Una-May O'Reilly · Amanpreet Singh · Mahmoud (Mido) Assran · Eugene Wu · Eytan Bakshy · Jinliang Wei · Michael Innes · Viral Shah · Haibin Lin · Conrad Sanderson · Ryan Curtin · Marcus Edel -
2018 : Poster Session »
Lorenzo Masoero · Tammo Rukat · Runjing Liu · Sayak Ray Chowdhury · Daniel Coelho de Castro · Claudia Wehrhahn · Feras Saad · Archit Verma · Kelvin Hsu · Irineo Cabreros · Sandhya Prabhakaran · Yiming Sun · Maxime Rischard · Linfeng Liu · Adam Farooq · Jeremiah Liu · Melanie F. Pradier · Diego Romeres · Neill Campbell · Kai Xu · Mehmet M Dundar · Tucker Keuter · Prashnna Gyawali · Eli Sennesh · Alessandro De Palma · Daniel Flam-Shepherd · Takatomi Kubo -
2018 Poster: DeepProbLog: Neural Probabilistic Logic Programming »
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2018 Poster: Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language »
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2018 Poster: Simple, Distributed, and Accelerated Probabilistic Programming »
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2018 Poster: Data-dependent PAC-Bayes priors via differential privacy »
Gintare Karolina Dziugaite · Daniel Roy -
2017 : Daniel Roy - Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes »
Daniel Roy -
2017 : KFAC and Natural Gradients »
Matthew Johnson · Daniel Duckworth -
2017 Tutorial: Statistical Relational Artificial Intelligence: Logic, Probability and Computation »
Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan -
2016 Poster: Measuring the reliability of MCMC inference with bidirectional Monte Carlo »
Roger Grosse · Siddharth Ancha · Daniel Roy -
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: Gibbs-type Indian Buffet Processes »
Creighton Heaukulani · Daniel Roy -
2014 Poster: Mondrian Forests: Efficient Online Random Forests »
Balaji Lakshminarayanan · Daniel Roy · Yee Whye Teh -
2013 Session: Session Chair »
Daniel Roy -
2013 Session: Tutorial Session B »
Daniel Roy -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Workshop: Probabilistic Programming: Foundations and Applications (2 day) »
Vikash Mansinghka · Daniel Roy · Noah Goodman -
2012 Poster: Random function priors for exchangeable graphs and arrays »
James R Lloyd · Daniel Roy · Peter Orbanz · Zoubin Ghahramani -
2011 Workshop: The 4th International Workshop on Music and Machine Learning: Learning from Musical Structure »
Rafael Ramirez · Darrell Conklin · Douglas Eck · Rif A. Saurous -
2011 Poster: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2011 Spotlight: Complexity of Inference in Latent Dirichlet Allocation »
David Sontag · Daniel Roy -
2008 Workshop: Probabilistic Programming: Universal Languages, Systems and Applications »
Daniel Roy · John Winn · David A McAllester · Vikash Mansinghka · Josh Tenenbaum -
2008 Oral: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2008 Poster: The Mondrian Process »
Daniel Roy · Yee Whye Teh -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
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