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Author Information
Abraham Stanway (Amperon)
Alex Robson (Invenia Labs)
Aneesh Rangnekar (Rochester Institute of Technology)
Ashesh Chattopadhyay (Rice University)
Ashley Pilipiszyn (Stanford University)
Benjamin LeRoy (Carnegie Mellon University)
I am a third year PhD student in the Statistics & Data Science Department at Carnegie Mellon University. My work involves leveraging structure information in diverse application fields to improve algorithms and develop better tools to quantify uncertainty in these settings. My interests include high dimensional statistics, statistical machine learning, and data visualization.
Bolong Cheng (SigOpt)
I am a research engineer at [SigOpt](www.sigopt.com), an Intel company. Currently, I work on productionizing Bayesian optimization, and more broadly, sequential decision making problems. Prior to SigOpt, my research focused on [approximate dynamic programming](http://adp.princeton.edu) and stochastic optimization, with an application in controlling grid-level battery storage.
Ce Zhang (ETH Zurich)
Chaopeng Shen (Pennsylvania State University)
Solving water resources and climate resilience problem with AI and process based models. Study streams, soil moisture, groundwater, etc.
Christian Schroeder (University of Oxford)
Christian Clough (Planet)
Clement DUHART (MIT Medialab)
Clement Fung (University of British Columbia)
Cozmin Ududec (Invenia Labs)
Dali Wang (Oak Ridge National Lab)
David Dao (ETH Zurich / UC Berkeley)
I'm a PhD student at ETH Zurich building AI and Data Systems for Sustainable Development. I'm leading the Climate + AI initiative at DS3Lab, mapping the ethical use of AI, and directing the Kara research project with Stanford and UC Berkeley. I'm also the founder of GainForest, a non-profit grantee of Microsoft’s AI for Earth program, which leverages decentralized technology to prevent deforestation. Previously, I was an engineer in Silicon Valley and a research fellow at Berkeley AI Research (BAIR), Stanford University and Broad Institute of MIT and Harvard. I'm a Global Shaper at World Economic Forum, a Core Member of Climate Change AI, a Climate Leader at Climate Reality Project and organized conferences with thousands of attendees in Germany, Silicon Valley, and at Harvard.
di wu (McGill University)
Dimitrios Giannakis (New York University)
Dino Sejdinovic (University of Oxford)
Doina Precup (McGill University / DeepMind)
Duncan Watson-Parris (University of Oxford)
Gege Wen (Stanford University)
George Chen (Carnegie Mellon University)
Gopal Erinjippurath (Planet Labs Inc)
Haifeng Li (Central South University)
Han Zou (University of California, Berkeley)
Herke van Hoof (University of Amsterdam)
Hillary A Scannell (University of Washington)
Hiroshi Mamitsuka (Kyoto University / Aalto University)
Hongbao Zhang (Petuum Inc)
Jaegul Choo (Korea University)
James Wang (Penn State University)
James Requeima (University of Cambridge)
Jessica Hwang (Stanford University)
Jinfan Xu (Zhejiang University)
Johan Mathe (Froglabs)
Jonathan Binas (Mila, Montreal)
Joonseok Lee (Google Research)
Kalai Ramea (Palo Alto Research Center)
Kate Duffy (Northeastern University)
Kevin McCloskey (Google)
Kris Sankaran (Mila)
Lester Mackey (Microsoft Research)

Lester Mackey is a machine learning researcher at Microsoft Research, where he develops new tools, models, and theory for large-scale learning tasks driven by applications from healthcare, climate, recommender systems, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second place team in the \$1M. Netflix Prize competition for collaborative filtering, won the \$50K Prise4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time \$800K Subseasonal Climate Forecast Rodeo, and received a best student paper award at the International Conference on Machine Learning.
Letif Mones (Invenia Labs)
Loubna Benabbou (University of Quebec UQAR)
Lynn Kaack (ETH Zurich)
Matthew Hoffman (Google)
Mayur Mudigonda (UC Berkeley)
Mehrdad Mahdavi (Pennsylvania State University)
Michael McCourt (SigOpt)

My research focuses on reproducing kernel Hilbert spaces as applied within meshfree approximation theory. Currently, I am working at SigOpt to adapt theory and strategies from functional/numerical analysis to be used in Bayesian optimization.
Mingchao Jiang (Rice University)
Mohammad Mahdi Kamani (The Pennsylvania State University)
Neel Guha (Carnegie Mellon University)
Niccolo Dalmasso (Carnegie Mellon University)
Nick Pawlowski (Imperial College London)
Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change)
Paulo Orenstein (Stanford University)
Pedram Hassanzadeh (Rice University)
Pekka Marttinen (Aalto University)
Ramesh Nair (Planet Labs Inc)
Sadegh Farhang (Pennsylvania State University)
Samuel Kaski (Aalto University)
Sandeep Manjanna (McGill University)
Sasha Luccioni (Mila)
I am currently a postdoctoral researcher working with Yoshua Bengio on the Climate Change AI (CCAI) project. The goal of the project is to develop an interactive website to depict accurate, vivid, and personalized outcomes of climate change, which will bring the future closer in the mind of the viewer and will demonstrate specific actions they can take to improve the environment. In my previous studies and research, I have worked on various domains, namely Natural Language Processing and AI in Education (AIED).
Shuby Deshpande (Carnegie Mellon University)
Soo Kim (Lawrence Livermore National Laboratory)
Soukayna Mouatadid (University of Toronto)
Sunghyun Park (Aamzon)
Tao Lin (Zhejiang University)
Telmo Felgueira (Instituto Superior Técnico)
Thomas Hornigold (University of Oxford)
Tianle Yuan (NASA GSFC)
Tom Beucler (Columbia University & UCI)
Tracy Cui (Google NYC)
Volodymyr Kuleshov (Stanford University / Afresh)
Wei Yu (University of Toronto)
yang song (oak ridge national lab)
I am an earth system modeler which great interest in studying the interactions between environmental change and terrestrial biosphere processes, including land surface energy balance, carbon, nitrogen, and phosphorus cycles, and hydrological cycle. In particular, I am interested in (1) using machine learning approaches to identify metagenomics-informed soil enzyme functional groups and their climate response at the regional and global scale; (2)Using metagenomic information to improve the representation of microbially-mediated soil carbon, nitrogen, and phosphorus biogeochemical cycles in the Earth system models; and Developing big data and decision support tools for agriculture and bioenergy industry.
Ydo Wexler (Amperon)
Yoshua Bengio (Mila / U. Montreal)
Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. He is the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world’s largest university-based research group in deep learning. He is a member of the NeurIPS board and co-founder and general chair for the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains and is Fellow of the same institution. In 2018, Yoshua Bengio ranked as the computer scientist with the most new citations, worldwide, thanks to his many publications. In 2019, he received the ACM A.M. Turing Award, “the Nobel Prize of Computing”, jointly with Geoffrey Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. In 2020 he was nominated Fellow of the Royal Society of London.
Zhecheng Wang (Stanford University)
Zhuangfang Yi (Development Seed)
Zhuangfang Yi is a machine learning engineer at Development Seed. As a machine learning engineer and a formerly trained research scientist Zhuangfang can quickly script algorithms and tools that help translate scientific methodologies to solve real-world problems. She has over 10 years of experience conducting geospatial analysis and image processing. Zhuangfang has led and developed multiple open-source tools for applying machine learning at scale either for object-based detection or pixel decoding.
Zouheir Malki (Polytechnique Montreal)
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Zhengxing Chen · Juan Jose Garau Luis · Ignacio Albert Smet · Aditya Modi · Sabina Tomkins · Riley Simmons-Edler · Hongzi Mao · Alexander Irpan · Hao Lu · Rose Wang · Subhojyoti Mukherjee · Aniruddh Raghu · Syed Arbab Mohd Shihab · Byung Hoon Ahn · Rasool Fakoor · Pratik Chaudhari · Elena Smirnova · Min-hwan Oh · Xiaocheng Tang · Tony Qin · Qingyang Li · Marc Brittain · Ian Fox · Supratik Paul · Xiaofeng Gao · Yinlam Chow · Gabriel Dulac-Arnold · Ofir Nachum · Nikos Karampatziakis · Bharathan Balaji · Supratik Paul · Ali Davody · Djallel Bouneffouf · Himanshu Sahni · Soo Kim · Andrey Kolobov · Alexander Amini · Yao Liu · Xinshi Chen · · Craig Boutilier -
2019 : Poster Session 1 (all papers) »
Matilde Gargiani · Yochai Zur · Chaim Baskin · Evgenii Zheltonozhskii · Liam Li · Ameet Talwalkar · Xuedong Shang · Harkirat Singh Behl · Atilim Gunes Baydin · Ivo Couckuyt · Tom Dhaene · Chieh Lin · Wei Wei · Min Sun · Orchid Majumder · Michele Donini · Yoshihiko Ozaki · Ryan P. Adams · Christian Geißler · Ping Luo · zhanglin peng · · Ruimao Zhang · John Langford · Rich Caruana · Debadeepta Dey · Charles Weill · Xavi Gonzalvo · Scott Yang · Scott Yak · Eugen Hotaj · Vladimir Macko · Mehryar Mohri · Corinna Cortes · Stefan Webb · Jonathan Chen · Martin Jankowiak · Noah Goodman · Aaron Klein · Frank Hutter · Mojan Javaheripi · Mohammad Samragh · Sungbin Lim · Taesup Kim · SUNGWOONG KIM · Michael Volpp · Iddo Drori · Yamuna Krishnamurthy · Kyunghyun Cho · Stanislaw Jastrzebski · Quentin de Laroussilhe · Mingxing Tan · Xiao Ma · Neil Houlsby · Andrea Gesmundo · Zalán Borsos · Krzysztof Maziarz · Felipe Petroski Such · Joel Lehman · Kenneth Stanley · Jeff Clune · Pieter Gijsbers · Joaquin Vanschoren · Felix Mohr · Eyke Hüllermeier · Zheng Xiong · Wenpeng Zhang · Wenwu Zhu · Weijia Shao · Aleksandra Faust · Michal Valko · Michael Y Li · Hugo Jair Escalante · Marcel Wever · Andrey Khorlin · Tara Javidi · Anthony Francis · Saurajit Mukherjee · Jungtaek Kim · Michael McCourt · Saehoon Kim · Tackgeun You · Seungjin Choi · Nicolas Knudde · Alexander Tornede · Ghassen Jerfel -
2019 Poster: Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement »
Wouter Kool · Herke van Hoof · Max Welling -
2019 Poster: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: Calibrated Model-Based Deep Reinforcement Learning »
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon -
2019 Poster: Learning Context-dependent Label Permutations for Multi-label Classification »
Jinseok Nam · Young-Bum Kim · Eneldo Loza Mencia · Sunghyun Park · Ruhi Sarikaya · Johannes Fürnkranz -
2019 Poster: Distributed Learning over Unreliable Networks »
Chen Yu · Hanlin Tang · Cedric Renggli · Simon Kassing · Ankit Singla · Dan Alistarh · Ce Zhang · Ji Liu -
2019 Poster: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Poster: Hierarchical Importance Weighted Autoencoders »
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville -
2019 Oral: Hierarchical Importance Weighted Autoencoders »
Chin-Wei Huang · Kris Sankaran · Eeshan Dhekane · Alexandre Lacoste · Aaron Courville -
2019 Oral: Learning Context-dependent Label Permutations for Multi-label Classification »
Jinseok Nam · Young-Bum Kim · Eneldo Loza Mencia · Sunghyun Park · Ruhi Sarikaya · Johannes Fürnkranz -
2019 Oral: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Oral: Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement »
Wouter Kool · Herke van Hoof · Max Welling -
2019 Oral: Distributed Learning over Unreliable Networks »
Chen Yu · Hanlin Tang · Cedric Renggli · Simon Kassing · Ankit Singla · Dan Alistarh · Ce Zhang · Ji Liu -
2019 Oral: Calibrated Model-Based Deep Reinforcement Learning »
Ali Malik · Volodymyr Kuleshov · Jiaming Song · Danny Nemer · Harlan Seymour · Stefano Ermon -
2019 Oral: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization »
Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe -
2019 Oral: Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization »
Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe -
2019 Poster: Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates »
George Chen -
2019 Poster: Off-Policy Deep Reinforcement Learning without Exploration »
Scott Fujimoto · David Meger · Doina Precup -
2019 Poster: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2019 Poster: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Poster: Active Learning for Decision-Making from Imbalanced Observational Data »
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski -
2019 Poster: Towards a Unified Analysis of Random Fourier Features »
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic -
2019 Poster: DL2: Training and Querying Neural Networks with Logic »
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev -
2019 Oral: Active Learning for Decision-Making from Imbalanced Observational Data »
Iiris Sundin · Peter Schulam · Eero Siivola · Aki Vehtari · Suchi Saria · Samuel Kaski -
2019 Oral: DL2: Training and Querying Neural Networks with Logic »
Marc Fischer · Mislav Balunovic · Dana Drachsler-Cohen · Timon Gehr · Ce Zhang · Martin Vechev -
2019 Oral: Towards a Unified Analysis of Random Fourier Features »
Zhu Li · Jean-Francois Ton · Dino Oglic · Dino Sejdinovic -
2019 Oral: Off-Policy Deep Reinforcement Learning without Exploration »
Scott Fujimoto · David Meger · Doina Precup -
2019 Oral: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Oral: Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates »
George Chen -
2019 Oral: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2018 Poster: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
2018 Oral: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
2018 Poster: Focused Hierarchical RNNs for Conditional Sequence Processing »
Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Poster: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning »
Tabish Rashid · Mikayel Samvelyan · Christian Schroeder · Gregory Farquhar · Jakob Foerster · Shimon Whiteson -
2018 Poster: Convergent Tree Backup and Retrace with Function Approximation »
Ahmed Touati · Pierre-Luc Bacon · Doina Precup · Pascal Vincent -
2018 Poster: Accurate Uncertainties for Deep Learning Using Calibrated Regression »
Volodymyr Kuleshov · Nathan Fenner · Stefano Ermon -
2018 Oral: Accurate Uncertainties for Deep Learning Using Calibrated Regression »
Volodymyr Kuleshov · Nathan Fenner · Stefano Ermon -
2018 Oral: Focused Hierarchical RNNs for Conditional Sequence Processing »
Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Oral: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning »
Tabish Rashid · Mikayel Samvelyan · Christian Schroeder · Gregory Farquhar · Jakob Foerster · Shimon Whiteson -
2018 Oral: Convergent Tree Backup and Retrace with Function Approximation »
Ahmed Touati · Pierre-Luc Bacon · Doina Precup · Pascal Vincent -
2018 Poster: Nonoverlap-Promoting Variable Selection »
Pengtao Xie · Hongbao Zhang · Yichen Zhu · Eric Xing -
2018 Poster: Asynchronous Decentralized Parallel Stochastic Gradient Descent »
Xiangru Lian · Wei Zhang · Ce Zhang · Ji Liu -
2018 Poster: $D^2$: Decentralized Training over Decentralized Data »
Hanlin Tang · Xiangru Lian · Ming Yan · Ce Zhang · Ji Liu -
2018 Oral: $D^2$: Decentralized Training over Decentralized Data »
Hanlin Tang · Xiangru Lian · Ming Yan · Ce Zhang · Ji Liu -
2018 Oral: Asynchronous Decentralized Parallel Stochastic Gradient Descent »
Xiangru Lian · Wei Zhang · Ce Zhang · Ji Liu -
2018 Oral: Nonoverlap-Promoting Variable Selection »
Pengtao Xie · Hongbao Zhang · Yichen Zhu · Eric Xing -
2017 Workshop: Private and Secure Machine Learning »
Antti Honkela · Kana Shimizu · Samuel Kaski -
2017 Workshop: Reproducibility in Machine Learning Research »
Rosemary Nan Ke · Anirudh Goyal · Alex Lamb · Joelle Pineau · Samy Bengio · Yoshua Bengio -
2017 Workshop: Reinforcement Learning Workshop »
Doina Precup · Balaraman Ravindran · Pierre-Luc Bacon -
2017 Poster: A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization »
Jianbo Ye · James Wang · Jia Li -
2017 Poster: ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning »
Hantian Zhang · Jerry Li · Kaan Kara · Dan Alistarh · Ji Liu · Ce Zhang -
2017 Poster: Improving Gibbs Sampler Scan Quality with DoGS »
Ioannis Mitliagkas · Lester Mackey -
2017 Poster: Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space »
Jose Miguel Hernandez-Lobato · James Requeima · Edward Pyzer-Knapp · Alan Aspuru-Guzik -
2017 Talk: ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning »
Hantian Zhang · Jerry Li · Kaan Kara · Dan Alistarh · Ji Liu · Ce Zhang -
2017 Talk: Improving Gibbs Sampler Scan Quality with DoGS »
Ioannis Mitliagkas · Lester Mackey -
2017 Talk: Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space »
Jose Miguel Hernandez-Lobato · James Requeima · Edward Pyzer-Knapp · Alan Aspuru-Guzik -
2017 Talk: A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization »
Jianbo Ye · James Wang · Jia Li -
2017 Poster: Measuring Sample Quality with Kernels »
Jackson Gorham · Lester Mackey -
2017 Poster: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio -
2017 Poster: A Closer Look at Memorization in Deep Networks »
David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: A Closer Look at Memorization in Deep Networks »
David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: Measuring Sample Quality with Kernels »
Jackson Gorham · Lester Mackey -
2017 Poster: Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo »
Matthew Hoffman -
2017 Talk: Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo »
Matthew Hoffman -
2017 Talk: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio