Timezone: »
Author Information
Matilde Gargiani (University of Freiburg)
Yochai Zur (Technion – Israel Institute of Technology)
Chaim Baskin (Technion)
Evgenii Zheltonozhskii (Technion)
Liam Li (Carnegie Mellon University)
Ameet Talwalkar (Carnegie Mellon University)
Xuedong Shang (Inria SequeL)
Harkirat Singh Behl (University of Oxford)
Atilim Gunes Baydin (University of Oxford)
Ivo Couckuyt (Ghent University)
Tom Dhaene (Ghent University - imec)
Chieh Lin (National Tsing Hua University (NTHU))
Wei Wei (Google)
Min Sun (National Tsing Hua University)
Orchid Majumder (Amazon Web Services)
I am a research engineer working for Amazon Web Services (AWS) in Seattle. I am currently part of the Computer Vision group.
Michele Donini (Amazon)
Yoshihiko Ozaki (National Institute of Advanced Industrial Science and Technology)
Ryan P. Adams (Princeton University)
Christian Geißler (GT-ARC gGmbH)
Ping Luo (The University of Hong Kong)
zhanglin peng (SenseTime)
Ruimao Zhang (SenseTime Research)
John Langford (Microsoft Research)
Rich Caruana (Microsoft)
Debadeepta Dey (Microsoft)
Charles Weill (Google AI Research NY)
Xavi Gonzalvo (Google Inc.)
Scott Yang (D. E. Shaw & Co.)
Scott Yak (Google Research)
Eugen Hotaj (Google)
Vladimir Macko (Google)
Mehryar Mohri (Google Research and Courant Institute of Mathematical Sciences)
Corinna Cortes (Google Research)
Stefan Webb (University of Oxford)
Jonathan Chen (Uber AI Labs)
Martin Jankowiak (Uber AI Labs)
Noah Goodman (Uber AI Labs)
Aaron Klein (University of Freiburg)
Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence)
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he has been a faculty member since 2013. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on automated machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.
Mojan Javaheripi (University of California, San Diego)
Mohammad Samragh (UC San Diego)
Sungbin Lim (Kakao Brain)
Taesup Kim (Mila, Université de Montréal)
SUNGWOONG KIM (KAKAO BRAIN)
Michael Volpp (Bosch Center for Artificial Intelligence)
Iddo Drori (Columbia University and NYU)
Yamuna Krishnamurthy (New York University)
Kyunghyun Cho (New York University)

Kyunghyun Cho is an associate professor of computer science and data science at New York University and CIFAR Fellow of Learning in Machines & Brains. He is also a senior director of frontier research at the Prescient Design team within Genentech Research & Early Development (gRED). He was a research scientist at Facebook AI Research from June 2017 to May 2020 and a postdoctoral fellow at University of Montreal until Summer 2015 under the supervision of Prof. Yoshua Bengio, after receiving MSc and PhD degrees from Aalto University April 2011 and April 2014, respectively, under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He received the Samsung Ho-Am Prize in Engineering in 2021. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
Stanislaw Jastrzebski (New York University)
Quentin de Laroussilhe (Google Brain)
Mingxing Tan (Google Brain)
Xiao Ma (Google)
Neil Houlsby (Google)
Andrea Gesmundo (Google)
Zalán Borsos (ETH Zurich)
Krzysztof Maziarz (Jagiellonian University)
Felipe Petroski Such (Uber AI Labs)
Joel Lehman (Uber AI Labs)

Joel Lehman is a senior research scientist at Uber AI Labs, and previously was an assistant professor at the IT University of Copenhagen. His research spans AI safety, neuroevolution, reinforcement learning, and deep learning.
Kenneth Stanley (Uber AI Labs & University of Central Florida)
Kenneth O. Stanley is Charles Millican Professor of Computer Science at the University of Central Florida and director there of the Evolutionary Complexity Research Group. He was also a co-founder of Geometric Intelligence Inc., which was acquired by Uber to create Uber AI Labs, where he is now also a senior research science manager and head of Core AI research. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems, coevolution, machine learning for video games, interactive evolution, and open-ended evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, novelty search, and Galactic Arms Race. His original 2002 paper on NEAT also received the 2017 ISAL Award for Outstanding Paper of the Decade 2002 - 2012 from the International Society for Artificial Life. He is a coauthor of the popular science book, "Why Greatness Cannot Be Planned: The Myth of the Objective" (published by Springer), and has spoken widely on its subject.
Jeff Clune (Uber AI Labs)
Jeff Clune is the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming and a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired a startup he was a part of. Jeff focuses on robotics and training deep neural networks via deep learning, including deep reinforcement learning. Prior to becoming a professor, he was a Research Scientist at Cornell University and received degrees from Michigan State University (PhD, master’s) and the University of Michigan (bachelor’s). More on Jeff’s research can be found at JeffClune.com or on Twitter (@jeffclune).
Pieter Gijsbers (Eindhoven University of Technology)
Joaquin Vanschoren (Eindhoven University of Technology)
Felix Mohr (Paderborn University)
Eyke Hüllermeier (Paderborn University)
Zheng Xiong (Tsinghua University)
Wenpeng Zhang (Tsinghua University)
Wenwu Zhu (Tsinghua University)
Wenwu Zhu is currently a Professor of Computer Science Department of Tsinghua University and Vice Dean of National Research Center on Information Science and Technology. Prior to his current post, he was a Senior Researcher and Research Manager at Microsoft Research Asia. He was the Chief Scientist and Director at Intel Research China from 2004 to 2008. He worked at Bell Labs New Jersey as a Member of Technical Staff during 1996-1999. He has been serving as the chair of the steering committee for IEEE T-MM since January 1, 2020. He served as the Editor-in-Chief for the IEEE Transactions on Multimedia (T-MM) from 2017 to 2019. And Vice EiC for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) from 2020-2021 He served as co-Chair for ACM MM 2018 and co-Chair for ACM CIKM 2019. His current research interests are in the areas of multimodal big data and intelligence, and multimedia networking. He received 10 Best Paper Awards. He is a member of Academia Europaea, an IEEE Fellow, AAAS Fellow, and SPIE Fellow.
Weijia Shao (TU-Berlin)
Aleksandra Faust (Google Brain)
Aleksandra Faust is a Staff Research Scientist at Google Brain Robotics, leading Task and Motion planning research group. Previously, Aleksandra led machine learning efforts for self-driving car planning and controls in Waymo, and was a researcher at Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico, a Master's in Computer Science from the University of Illinois at Urbana-Champaign, and a Bachelors in Math with a minor in Computer Science from the University of Belgrade. Her research interests include machine learning for safe, scalable, and socially-aware motion planning, decision-making, and robot behavior. Aleksandra won the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in STEM in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, and was awarded Best Paper in Service Robotics at ICRA 2018.
Michal Valko (DeepMind)
Michal is a research scientist in DeepMind Paris and SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. He also teaches the course Graphs in Machine Learning at l'ENS Cachan. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimising the data that humans need spend inspecting, classifying, or “tuning” the algorithms. Another important feature of machine learning algorithms should be the ability to adapt to changing environments. That is why he is working in domains that are able to deal with minimal feedback, such as bandit algorithms, semi-supervised learning, and anomaly detection. Most recently he has worked on sequential algorithms with structured decisions where exploiting the structure can lead to provably faster learning. In the past the common thread of Michal's work has been adaptive graph-based learning and its application to the real world applications such as recommender systems, medical error detection, and face recognition. His industrial collaborators include Adobe, Intel, Technicolor, and Microsoft Research. He received his PhD in 2011 from University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos.
Michael Y Li (Princeton University)
Hugo Jair Escalante (INAOE)
Marcel Wever (Paderborn University)
Andrey Khorlin (Google Inc)
Tara Javidi (University of California San Diego)
Anthony Francis (Google)
Dr. Anthony G. Francis, Jr. is a Senior Software Engineer at Google Brain Robotics specializing in reinforcement learning for robot navigation. Previously, he worked on emotional long-term memory for robot pets at Georgia Tech's PEPE robot pet project, on models of human memory for information retrieval at Enkia Corporation, and on large-scale metadata search and 3D object visualization at Google. He earned his B.S. (1991), M.S. (1996) and Ph.D. (2000) in Computer Science from Georgia Tech, along with a Certificate in Cognitive Science (1999). He and his colleagues won the ICRA 2018 Best Paper Award for Service Robotics for their paper "PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning". He's the author of over a dozen peer-reviewed publications and is an inventor on over a half-dozen patents. He's published over a dozen short stories and four novels, including the EPIC eBook Award-winning Frost Moon; his popular writing on robotics includes articles in the books Star Trek Psychology and Westworld Psychology. as well as a Google AI blog article titled Maybe your computer just needs a hug. He lives in San Jose with his wife and cats, but his heart will always belong in Atlanta. You can find out more about his writing at his website dresan.com.
Saurajit Mukherjee (Microsoft)
Jungtaek Kim (POSTECH)
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.
Saehoon Kim (AITRICS)
Tackgeun You (POSTECH)
Seungjin Choi (POSTECH)
Nicolas Knudde (Ghent University)
Alexander Tornede (Paderborn University)
Ghassen Jerfel (Duke University)
More from the Same Authors
-
2020 : HAT: Hierarchical Alternative Training for Long Range Policy Transfer »
Min Sun · Wei-Cheng Tseng -
2021 : True Few-Shot Learning with Language Models »
Ethan Perez · Douwe Kiela · Kyunghyun Cho -
2021 : Unsupervised Information Obfuscation for Split Inference of Neural Networks »
Mohammad Samragh · Hossein Hosseini · Aleksei Triastcyn · Kambiz Azarian · Joseph B Soriaga · Farinaz Koushanfar -
2021 : Learning with User-Level Privacy »
Daniel A Levy · Ziteng Sun · Kareem Amin · Satyen Kale · Alex Kulesza · Mehryar Mohri · Ananda Theertha Suresh -
2021 : Ranking Architectures by Feature Extraction Capabilities »
Debadeepta Dey · Shital Shah · Sebastien Bubeck -
2021 : Automated Learning Rate Scheduler for Large-batch Training »
Chiheon Kim · Saehoon Kim · Jongmin Kim · Donghoon Lee · SUNGWOONG KIM -
2021 : Replacing the Ex-Def Baseline in AutoML by Naive AutoML »
Felix Mohr · Marcel Wever -
2021 : Dynamic Pruning of a Neural Network via Gradient Signal-to-Noise Ratio »
Julien N Siems · Aaron Klein · Cedric Archambeau · Maren Mahsereci -
2021 : Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization »
Sergio Izquierdo · Julia Guerrero-Viu · Sven Hauns · Guilherme Miotto · Simon Schrodi · André Biedenkapp · Thomas Elsken · Difan Deng · Marius Lindauer · Frank Hutter -
2021 : Detecting and Quantifying Malicious Activity with Simulation-based Inference »
Andrew Gambardella · Naeemullah Khan · Phil Torr · Atilim Gunes Baydin -
2021 : Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan P. Adams · Sergey Levine -
2021 : Provable RL with Exogenous Distractors via Multistep Inverse Dynamics »
Yonathan Efroni · Dipendra Misra · Akshay Krishnamurthy · Alekh Agarwal · John Langford -
2021 : SparseDice: Imitation Learning for Temporally Sparse Data via Regularization »
Alberto Camacho · Izzeddin Gur · Marcin Moczulski · Ofir Nachum · Aleksandra Faust -
2021 : A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning »
Tianchi Cai · Wenpeng Zhang · Lihong Gu · Xiaodong Zeng · Jinjie Gu -
2021 : Interpretable Machine Learning: Moving From Mythos to Diagnostics »
Valerie Chen · Jeffrey Li · Joon Kim · Gregory Plumb · Ameet Talwalkar -
2022 : Interaction-Grounded Learning with Action-inclusive Feedback »
Tengyang Xie · Akanksha Saran · Dylan Foster · Lekan Molu · Ida Momennejad · Nan Jiang · Paul Mineiro · John Langford -
2022 : SimpleSpot and Evaluating Systemic Errors using Synthetic Image Datasets »
Gregory Plumb · Nari Johnson · Ángel Alexander Cabrera · Marco Ribeiro · Ameet Talwalkar -
2022 : Linear Connectivity Reveals Generalization Strategies »
Jeevesh Juneja · Rachit Bansal · Kyunghyun Cho · João Sedoc · Naomi Saphra -
2022 : SI-Score »
Jessica Yung · Rob Romijnders · Alexander Kolesnikov · Lucas Beyer · Josip Djolonga · Neil Houlsby · Sylvain Gelly · Mario Lucic · Xiaohua Zhai -
2022 : P30: Meta-Learning Real-Time Bayesian AutoML For Small Tabular Data »
Frank Hutter · Katharina Eggensperger -
2022 : On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning »
Diane Wagner · Fabio Ferreira · Danny Stoll · Robin Tibor Schirrmeister · Samuel Gabriel Müller · Frank Hutter -
2022 : Perspectives on Incorporating Expert Feedback into Model Updates »
Valerie Chen · Umang Bhatt · Hoda Heidari · Adrian Weller · Ameet Talwalkar -
2023 : Latent State Transitions in Training Dynamics »
Michael Hu · Angelica Chen · Naomi Saphra · Kyunghyun Cho -
2023 : Classifier Robustness Enhancement Via Test-Time Transformation »
Tsachi Blau · Roy Ganz · Chaim Baskin · Michael Elad · Alex Bronstein -
2023 : Where Does My Model Underperform?: A Human Evaluation of Slice Discovery Algorithms »
Nari Johnson · Ángel Alexander Cabrera · Gregory Plumb · Ameet Talwalkar -
2023 : Separating multimodal modeling from multidimensional modeling for multimodal learning »
Divyam Madaan · Taro Makino · Sumit Chopra · Kyunghyun Cho -
2023 : Antibody DomainBed: Towards robust predictions using invariant representations of biological sequences carrying complex distribution shifts »
Natasa Tagasovska · Ji Won Park · Stephen Ra · Kyunghyun Cho -
2023 : Generative Marginalization Models »
Sulin Liu · Peter Ramadge · Ryan P. Adams -
2023 : Deep Fusion: Efficient Network Training via Pre-trained Initializations »
Hanna Mazzawi · Xavi Gonzalvo · Michael Wunder -
2023 : CAAFE: Combining Large Language Models with Tabular Predictors for Semi-Automated Data Science »
Noah Hollmann · Samuel Gabriel Müller · Frank Hutter -
2023 : JAX FDM: A differentiable solver for inverse form-finding »
Rafael Pastrana · Deniz Oktay · Ryan P. Adams · Sigrid Adriaenssens -
2023 : Concept Bottleneck Generative Models »
Aya Ismail · Julius Adebayo · Hector Corrada Bravo · Stephen Ra · Kyunghyun Cho -
2023 : Ranking with Abstention »
Anqi Mao · Mehryar Mohri · Yutao Zhong -
2023 : Protein Design with Guided Discrete Diffusion »
Nate Gruver · Samuel Stanton · Nathan Frey · Tim G. J. Rudner · Isidro Hotzel · Julien Lafrance-Vanasse · Arvind Rajpal · Kyunghyun Cho · Andrew Wilson -
2023 : Guided Evolution with Binary Predictors for ML Program Search »
John Co-Reyes · Yingjie Miao · George Tucker · Aleksandra Faust · Esteban Real -
2023 Workshop: Interactive Learning with Implicit Human Feedback »
Andi Peng · Akanksha Saran · Andreea Bobu · Tengyang Xie · Pierre-Yves Oudeyer · Anca Dragan · John Langford -
2023 : A margin-based multiclass generalization bound via geometric complexity »
Michael Munn · Benoit Dherin · Xavi Gonzalvo -
2023 Oral: Cross-Modal Fine-Tuning: Align then Refine »
Junhong Shen · Liam Li · Lucio Dery · Corey Staten · Mikhail Khodak · Graham Neubig · Ameet Talwalkar -
2023 Poster: Cross-Modal Fine-Tuning: Align then Refine »
Junhong Shen · Liam Li · Lucio Dery · Corey Staten · Mikhail Khodak · Graham Neubig · Ameet Talwalkar -
2023 Poster: $\pi$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation »
CHENGYUE WU · Teng Wang · Yixiao Ge · Zeyu Lu · Ruisong Zhou · Ying Shan · Ping Luo -
2023 Poster: Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation »
Xin Wang · Zirui Pan · Yuwei Zhou · Hong Chen · Chendi Ge · Wenwu Zhu -
2023 Poster: PFNs4BO: In-Context Learning for Bayesian Optimization »
Samuel Gabriel Müller · Matthias Feurer · Noah Hollmann · Frank Hutter -
2023 Poster: AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners »
Zhixuan Liang · Yao Mu · Mingyu Ding · Fei Ni · Masayoshi Tomizuka · Ping Luo -
2023 Poster: Underspecification Presents Challenges for Credibility in Modern Machine Learning »
Alexander D'Amour · Katherine Heller · Dan Moldovan · Ben Adlam · Babak Alipanahi · Alex Beutel · Christina Chen · Jonathan Deaton · Jacob Eisenstein · Matthew Hoffman · Farhad Hormozdiari · Neil Houlsby · Shaobo Hou · Ghassen Jerfel · Alan Karthikesalingam · Mario Lucic · Yian Ma · Cory McLean · Diana Mincu · Akinori Mitani · Andrea Montanari · Zachary Nado · Vivek Natarajan · Christopher Nielson · Thomas F. Osborne · Rajiv Raman · Kim Ramasamy · Rory sayres · Jessica Schrouff · Martin Seneviratne · Shannon Sequeira · Harini Suresh · Victor Veitch · Maksym Vladymyrov · Xuezhi Wang · Kellie Webster · Steve Yadlowsky · Taedong Yun · Xiaohua Zhai · D. Sculley -
2023 Poster: $H$-Consistency Bounds for Pairwise Misranking Loss Surrogates »
Anqi Mao · Mehryar Mohri · Yutao Zhong -
2023 Poster: Reinforcement Learning Can Be More Efficient with Multiple Rewards »
Christoph Dann · Yishay Mansour · Mehryar Mohri -
2023 Poster: On Second-Order Scoring Rules for Epistemic Uncertainty Quantification »
Viktor Bengs · Eyke Hüllermeier · Willem Waegeman -
2023 Poster: Adaptive Computation with Elastic Input Sequence »
Fuzhao Xue · Valerii Likhosherstov · Anurag Arnab · Neil Houlsby · Mostafa Dehghani · Yang You -
2023 Poster: Scaling Vision Transformers to 22 Billion Parameters »
Mostafa Dehghani · Josip Djolonga · Basil Mustafa · Piotr Padlewski · Jonathan Heek · Justin Gilmer · Andreas Steiner · Mathilde Caron · Robert Geirhos · Ibrahim Alabdulmohsin · Rodolphe Jenatton · Lucas Beyer · Michael Tschannen · Anurag Arnab · Xiao Wang · Carlos Riquelme · Matthias Minderer · Joan Puigcerver · Utku Evci · Manoj Kumar · Sjoerd van Steenkiste · Gamaleldin Elsayed · Aravindh Mahendran · Fisher Yu · Avital Oliver · Fantine Huot · Jasmijn Bastings · Mark Collier · Alexey Gritsenko · Vighnesh N Birodkar · Cristina Vasconcelos · Yi Tay · Thomas Mensink · Alexander Kolesnikov · Filip Pavetic · Dustin Tran · Thomas Kipf · Mario Lucic · Xiaohua Zhai · Daniel Keysers · Jeremiah Harmsen · Neil Houlsby -
2023 Poster: Cross-Entropy Loss Functions: Theoretical Analysis and Applications »
Anqi Mao · Mehryar Mohri · Yutao Zhong -
2023 Poster: Towards Understanding and Improving GFlowNet Training »
Max Shen · Emmanuel Bengio · Ehsan Hajiramezanali · Andreas Loukas · Kyunghyun Cho · Tommaso Biancalani -
2023 Poster: ChiPFormer: Transferable Chip Placement via Offline Decision Transformer »
Yao LAI · Jinxin Liu · Zhentao Tang · Bin Wang · Jianye Hao · Ping Luo -
2023 Oral: Scaling Vision Transformers to 22 Billion Parameters »
Mostafa Dehghani · Josip Djolonga · Basil Mustafa · Piotr Padlewski · Jonathan Heek · Justin Gilmer · Andreas Steiner · Mathilde Caron · Robert Geirhos · Ibrahim Alabdulmohsin · Rodolphe Jenatton · Lucas Beyer · Michael Tschannen · Anurag Arnab · Xiao Wang · Carlos Riquelme · Matthias Minderer · Joan Puigcerver · Utku Evci · Manoj Kumar · Sjoerd van Steenkiste · Gamaleldin Elsayed · Aravindh Mahendran · Fisher Yu · Avital Oliver · Fantine Huot · Jasmijn Bastings · Mark Collier · Alexey Gritsenko · Vighnesh N Birodkar · Cristina Vasconcelos · Yi Tay · Thomas Mensink · Alexander Kolesnikov · Filip Pavetic · Dustin Tran · Thomas Kipf · Mario Lucic · Xiaohua Zhai · Daniel Keysers · Jeremiah Harmsen · Neil Houlsby -
2023 Oral: AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners »
Zhixuan Liang · Yao Mu · Mingyu Ding · Fei Ni · Masayoshi Tomizuka · Ping Luo -
2023 Poster: CLUTR: Curriculum Learning via Unsupervised Task Representation Learning »
Abdus Salam Azad · Izzeddin Gur · Jasper Emhoff · Nathaniel Alexis · Aleksandra Faust · Pieter Abbeel · Ion Stoica -
2023 Poster: Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features »
Chieh Lin · Hung-Yu Tseng · Hsin-Ying Lee · Maneesh Singh · Ming-Hsuan Yang -
2023 Poster: Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks »
Xu Chu · Yujie Jin · Xin Wang · Shanghang Zhang · Yasha Wang · Wenwu Zhu · Hong Mei -
2023 Poster: Universal Morphology Control via Contextual Modulation »
Zheng Xiong · Jacob Beck · Shimon Whiteson -
2023 Poster: Optimizing Hyperparameters with Conformal Quantile Regression »
David Salinas · Jacek Golebiowski · Aaron Klein · Matthias Seeger · Cedric Archambeau -
2023 Tutorial: Discovering Agent-Centric Latent States in Theory and in Practice »
John Langford · Alex Lamb -
2023 Panel: ICML Education Outreach Panel »
Andreas Krause · Barbara Engelhardt · Emma Brunskill · Kyunghyun Cho -
2023 Expo Talk Panel: Vowpal Wabbit: year in review and looking ahead in an LLM world »
John Langford · Byron Xu · Cheng Tan · Jack Gerrits · Lili Wu · Mark Rucker · Olga Vrousgou -
2022 : Invited talk #3 Rich Caruana. Talk Title: Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning for Healthcare »
Rich Caruana -
2022 : A simple and universal rotation equivariant point-cloud network »
Ben Finkelshtein · Chaim Baskin · Haggai Maron · Nadav Dym -
2022 : SI-Score »
Jessica Yung · Rob Romijnders · Alexander Kolesnikov · Lucas Beyer · Josip Djolonga · Neil Houlsby · Sylvain Gelly · Mario Lucic · Xiaohua Zhai -
2022 : Dynamic neural networks: Present and Future »
Neil Houlsby -
2022 Poster: Instance Dependent Regret Analysis of Kernelized Bandits »
Shubhanshu Shekhar · Tara Javidi -
2022 Poster: Flow-based Recurrent Belief State Learning for POMDPs »
Xiaoyu Chen · Yao Mu · Ping Luo · Shengbo Li · Jianyu Chen -
2022 Poster: Graph Neural Architecture Search Under Distribution Shifts »
Yijian Qin · Xin Wang · Ziwei Zhang · Pengtao Xie · Wenwu Zhu -
2022 Spotlight: Flow-based Recurrent Belief State Learning for POMDPs »
Xiaoyu Chen · Yao Mu · Ping Luo · Shengbo Li · Jianyu Chen -
2022 Spotlight: Instance Dependent Regret Analysis of Kernelized Bandits »
Shubhanshu Shekhar · Tara Javidi -
2022 Spotlight: Graph Neural Architecture Search Under Distribution Shifts »
Yijian Qin · Xin Wang · Ziwei Zhang · Pengtao Xie · Wenwu Zhu -
2022 Poster: Sanity Simulations for Saliency Methods »
Joon Kim · Gregory Plumb · Ameet Talwalkar -
2022 Poster: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning »
Alberto Bietti · Chen-Yu Wei · Miroslav Dudik · John Langford · Steven Wu -
2022 Poster: Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks »
Nan Wu · Stanislaw Jastrzebski · Kyunghyun Cho · Krzysztof J Geras -
2022 Poster: Contextual Bandits with Large Action Spaces: Made Practical »
Yinglun Zhu · Dylan Foster · John Langford · Paul Mineiro -
2022 Poster: Auxiliary Learning with Joint Task and Data Scheduling »
Hong Chen · Xin Wang · Chaoyu Guan · Yue Liu · Wenwu Zhu -
2022 Poster: Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation »
Chris Dann · Yishay Mansour · Mehryar Mohri · Ayush Sekhari · Karthik Sridharan -
2022 Spotlight: Auxiliary Learning with Joint Task and Data Scheduling »
Hong Chen · Xin Wang · Chaoyu Guan · Yue Liu · Wenwu Zhu -
2022 Spotlight: Sanity Simulations for Saliency Methods »
Joon Kim · Gregory Plumb · Ameet Talwalkar -
2022 Spotlight: Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks »
Nan Wu · Stanislaw Jastrzebski · Kyunghyun Cho · Krzysztof J Geras -
2022 Spotlight: Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation »
Chris Dann · Yishay Mansour · Mehryar Mohri · Ayush Sekhari · Karthik Sridharan -
2022 Spotlight: Contextual Bandits with Large Action Spaces: Made Practical »
Yinglun Zhu · Dylan Foster · John Langford · Paul Mineiro -
2022 Spotlight: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning »
Alberto Bietti · Chen-Yu Wei · Miroslav Dudik · John Langford · Steven Wu -
2022 Poster: H-Consistency Bounds for Surrogate Loss Minimizers »
Pranjal Awasthi · Anqi Mao · Mehryar Mohri · Yutao Zhong -
2022 Poster: DNA: Domain Generalization with Diversified Neural Averaging »
Xu Chu · Yujie Jin · Wenwu Zhu · Yasha Wang · Xin Wang · Shanghang Zhang · Hong Mei -
2022 Poster: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
Viktor Bengs · Aadirupa Saha · Eyke Hüllermeier -
2022 Poster: Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty »
Jixiang Qing · Tom Dhaene · Ivo Couckuyt -
2022 Poster: Parametric Visual Program Induction with Function Modularization »
Xuguang Duan · Xin Wang · Ziwei Zhang · Wenwu Zhu -
2022 Poster: Zero-shot AutoML with Pretrained Models »
Ekrem Öztürk · Fabio Ferreira · Hadi S Jomaa · Lars Schmidt-Thieme · Josif Grabocka · Frank Hutter -
2022 Poster: Large-Scale Graph Neural Architecture Search »
Chaoyu Guan · Xin Wang · Hong Chen · Ziwei Zhang · Wenwu Zhu -
2022 Oral: H-Consistency Bounds for Surrogate Loss Minimizers »
Pranjal Awasthi · Anqi Mao · Mehryar Mohri · Yutao Zhong -
2022 Spotlight: Large-Scale Graph Neural Architecture Search »
Chaoyu Guan · Xin Wang · Hong Chen · Ziwei Zhang · Wenwu Zhu -
2022 Spotlight: Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty »
Jixiang Qing · Tom Dhaene · Ivo Couckuyt -
2022 Spotlight: Parametric Visual Program Induction with Function Modularization »
Xuguang Duan · Xin Wang · Ziwei Zhang · Wenwu Zhu -
2022 Spotlight: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
Viktor Bengs · Aadirupa Saha · Eyke Hüllermeier -
2022 Spotlight: Zero-shot AutoML with Pretrained Models »
Ekrem Öztürk · Fabio Ferreira · Hadi S Jomaa · Lars Schmidt-Thieme · Josif Grabocka · Frank Hutter -
2022 Spotlight: DNA: Domain Generalization with Diversified Neural Averaging »
Xu Chu · Yujie Jin · Wenwu Zhu · Yasha Wang · Xin Wang · Shanghang Zhang · Hong Mei -
2022 : Introduction »
John Langford -
2021 : Contributed Talk #4 »
Mohammad Samragh · Hossein Hosseini · Kambiz Azarian · Farinaz Koushanfar -
2021 : Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing (Q&A) »
Ameet Talwalkar -
2021 : Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing »
Ameet Talwalkar -
2021 : Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (Spotlight #4) »
Eyke Hüllermeier -
2021 Workshop: Time Series Workshop »
Yian Ma · Ehi Nosakhare · Yuyang Wang · Scott Yang · Rose Yu -
2021 Workshop: ICML Workshop on Human in the Loop Learning (HILL) »
Trevor Darrell · Xin Wang · Li Erran Li · Fisher Yu · Zeynep Akata · Wenwu Zhu · Pradeep Ravikumar · Shiji Zhou · Shanghang Zhang · Kalesha Bullard -
2021 : Ranking Architectures by their Feature Extraction Capabilities »
Debadeepta Dey -
2021 : RL Foundation Panel »
Matthew Botvinick · Thomas Dietterich · Leslie Kaelbling · John Langford · Warrren B Powell · Csaba Szepesvari · Lihong Li · Yuxi Li -
2021 Workshop: 8th ICML Workshop on Automated Machine Learning (AutoML 2021) »
Gresa Shala · Frank Hutter · Joaquin Vanschoren · Marius Lindauer · Katharina Eggensperger · Colin White · Erin LeDell -
2021 Poster: Overcoming Catastrophic Forgetting by Bayesian Generative Regularization »
PEI-HUNG Chen · Wei Wei · Cho-Jui Hsieh · Bo Dai -
2021 Spotlight: Overcoming Catastrophic Forgetting by Bayesian Generative Regularization »
PEI-HUNG Chen · Wei Wei · Cho-Jui Hsieh · Bo Dai -
2021 Spotlight: A Discriminative Technique for Multiple-Source Adaptation »
Corinna Cortes · Mehryar Mohri · Ananda Theertha Suresh · Ningshan Zhang -
2021 Poster: Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design »
Gustavo Malkomes · Bolong Cheng · Eric Lee · Michael McCourt -
2021 Poster: Accuracy, Interpretability, and Differential Privacy via Explainable Boosting »
Harsha Nori · Rich Caruana · Zhiqi Bu · Judy Hanwen Shen · Janardhan Kulkarni -
2021 Poster: A Discriminative Technique for Multiple-Source Adaptation »
Corinna Cortes · Mehryar Mohri · Ananda Theertha Suresh · Ningshan Zhang -
2021 Poster: Rissanen Data Analysis: Examining Dataset Characteristics via Description Length »
Ethan Perez · Douwe Kiela · Kyunghyun Cho -
2021 Spotlight: Rissanen Data Analysis: Examining Dataset Characteristics via Description Length »
Ethan Perez · Douwe Kiela · Kyunghyun Cho -
2021 Spotlight: Accuracy, Interpretability, and Differential Privacy via Explainable Boosting »
Harsha Nori · Rich Caruana · Zhiqi Bu · Judy Hanwen Shen · Janardhan Kulkarni -
2021 Spotlight: Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design »
Gustavo Malkomes · Bolong Cheng · Eric Lee · Michael McCourt -
2021 Poster: Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization »
Stanislaw Jastrzebski · Devansh Arpit · Oliver Astrand · Giancarlo Kerg · Huan Wang · Caiming Xiong · Richard Socher · Kyunghyun Cho · Krzysztof J Geras -
2021 Poster: Interaction-Grounded Learning »
Tengyang Xie · John Langford · Paul Mineiro · Ida Momennejad -
2021 Spotlight: Relative Deviation Margin Bounds »
Corinna Cortes · Mehryar Mohri · Ananda Theertha Suresh -
2021 Spotlight: Interaction-Grounded Learning »
Tengyang Xie · John Langford · Paul Mineiro · Ida Momennejad -
2021 Spotlight: Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization »
Stanislaw Jastrzebski · Devansh Arpit · Oliver Astrand · Giancarlo Kerg · Huan Wang · Caiming Xiong · Richard Socher · Kyunghyun Cho · Krzysztof J Geras -
2021 Poster: Self-Paced Context Evaluation for Contextual Reinforcement Learning »
Theresa Eimer · André Biedenkapp · Frank Hutter · Marius Lindauer -
2021 Poster: TempoRL: Learning When to Act »
André Biedenkapp · Raghu Rajan · Frank Hutter · Marius Lindauer -
2021 Poster: ChaCha for Online AutoML »
Qingyun Wu · Chi Wang · John Langford · Paul Mineiro · Marco Rossi -
2021 Poster: Relative Deviation Margin Bounds »
Corinna Cortes · Mehryar Mohri · Ananda Theertha Suresh -
2021 Spotlight: TempoRL: Learning When to Act »
André Biedenkapp · Raghu Rajan · Frank Hutter · Marius Lindauer -
2021 Spotlight: ChaCha for Online AutoML »
Qingyun Wu · Chi Wang · John Langford · Paul Mineiro · Marco Rossi -
2021 Spotlight: Self-Paced Context Evaluation for Contextual Reinforcement Learning »
Theresa Eimer · André Biedenkapp · Frank Hutter · Marius Lindauer -
2021 Poster: AutoAttend: Automated Attention Representation Search »
Chaoyu Guan · Xin Wang · Wenwu Zhu -
2021 Poster: EfficientNetV2: Smaller Models and Faster Training »
Mingxing Tan · Quoc Le -
2021 Spotlight: AutoAttend: Automated Attention Representation Search »
Chaoyu Guan · Xin Wang · Wenwu Zhu -
2021 Spotlight: EfficientNetV2: Smaller Models and Faster Training »
Mingxing Tan · Quoc Le -
2021 Town Hall: Town Hall »
John Langford · Marina Meila · Tong Zhang · Le Song · Stefanie Jegelka · Csaba Szepesvari -
2021 Poster: Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size »
Jack Kosaian · Amar Phanishayee · Matthai Philipose · Debadeepta Dey · Rashmi Vinayak -
2021 Poster: Explainable Automated Graph Representation Learning with Hyperparameter Importance »
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu -
2021 Poster: BORE: Bayesian Optimization by Density-Ratio Estimation »
Louis Chi-Chun Tiao · Aaron Klein · Matthias W Seeger · Edwin V. Bonilla · Cedric Archambeau · Fabio Ramos -
2021 Spotlight: Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size »
Jack Kosaian · Amar Phanishayee · Matthai Philipose · Debadeepta Dey · Rashmi Vinayak -
2021 Spotlight: Explainable Automated Graph Representation Learning with Hyperparameter Importance »
Xin Wang · Shuyi Fan · Kun Kuang · Wenwu Zhu -
2021 Oral: BORE: Bayesian Optimization by Density-Ratio Estimation »
Louis Chi-Chun Tiao · Aaron Klein · Matthias W Seeger · Edwin V. Bonilla · Cedric Archambeau · Fabio Ramos -
2021 Expo Workshop: Real World RL: Azure Personalizer & Vowpal Wabbit »
Sheetal Lahabar · Etienne Kintzler · Mark Rucker · Bogdan Mazoure · Qingyun Wu · Pavithra Srinath · Jack Gerrits · Olga Vrousgou · John Langford · Eduardo Salinas -
2020 : Invited Talk 10: Prof. Wenwu Zhu from Tsinghua University »
Wenwu Zhu -
2020 Workshop: 7th ICML Workshop on Automated Machine Learning (AutoML 2020) »
Frank Hutter · Joaquin Vanschoren · Marius Lindauer · Charles Weill · Katharina Eggensperger · Matthias Feurer · Matthias Feurer -
2020 : Welcome »
Frank Hutter -
2020 : Discussion Panel »
Krzysztof Dembczynski · Prateek Jain · Alina Beygelzimer · Inderjit Dhillon · Anna Choromanska · Maryam Majzoubi · Yashoteja Prabhu · John Langford -
2020 : Invited Talk: What Interpretable Machine Learning Can Tell Us About Missing Values »
Rich Caruana -
2020 Workshop: Workshop on eXtreme Classification: Theory and Applications »
Anna Choromanska · John Langford · Maryam Majzoubi · Yashoteja Prabhu -
2020 Poster: Preselection Bandits »
Viktor Bengs · Eyke Hüllermeier -
2020 Poster: Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning »
Dipendra Kumar Misra · Mikael Henaff · Akshay Krishnamurthy · John Langford -
2020 Poster: Go Wide, Then Narrow: Efficient Training of Deep Thin Networks »
Denny Zhou · Mao Ye · Chen Chen · Tianjian Meng · Mingxing Tan · Xiaodan Song · Quoc Le · Qiang Liu · Dale Schuurmans -
2020 Poster: Adaptive Region-Based Active Learning »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Ningshan Zhang -
2020 Poster: Online Learning with Dependent Stochastic Feedback Graphs »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Ningshan Zhang -
2020 Poster: Amortized Finite Element Analysis for Fast PDE-Constrained Optimization »
Tianju Xue · Alex Beatson · Sigrid Adriaenssens · Ryan P. Adams -
2020 Poster: FACT: A Diagnostic for Group Fairness Trade-offs »
Joon Kim · Jiahao Chen · Ameet Talwalkar -
2020 Poster: No-Regret Exploration in Goal-Oriented Reinforcement Learning »
Jean Tarbouriech · Evrard Garcelon · Michal Valko · Matteo Pirotta · Alessandro Lazaric -
2020 Poster: Gamification of Pure Exploration for Linear Bandits »
Rémy Degenne · Pierre Menard · Xuedong Shang · Michal Valko -
2020 Poster: Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors »
Mike Dusenberry · Ghassen Jerfel · Yeming Wen · Yian Ma · Jasper Snoek · Katherine Heller · Balaji Lakshminarayanan · Dustin Tran -
2020 Poster: Channel Equilibrium Networks for Learning Deep Representation »
Wenqi Shao · Shitao Tang · Xingang Pan · Ping Tan · Xiaogang Wang · Ping Luo -
2020 Poster: Adaptive Sampling for Estimating Probability Distributions »
Shubhanshu Shekhar · Tara Javidi · Mohammad Ghavamzadeh -
2020 Poster: SCAFFOLD: Stochastic Controlled Averaging for Federated Learning »
Sai Praneeth Reddy Karimireddy · Satyen Kale · Mehryar Mohri · Sashank Jakkam Reddi · Sebastian Stich · Ananda Theertha Suresh -
2020 Poster: Automatic Shortcut Removal for Self-Supervised Representation Learning »
Matthias Minderer · Olivier Bachem · Neil Houlsby · Michael Tschannen -
2020 Poster: Explaining Groups of Points in Low-Dimensional Representations »
Gregory Plumb · Jonathan Terhorst · Sriram Sankararaman · Ameet Talwalkar -
2020 Poster: Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks »
Pranjal Awasthi · Natalie Frank · Mehryar Mohri -
2020 Poster: FedBoost: A Communication-Efficient Algorithm for Federated Learning »
Jenny Hamer · Mehryar Mohri · Ananda Theertha Suresh -
2019 : Jeff Clune: Towards Solving Catastrophic Forgetting with Neuromodulation & Learning Curricula by Generating Environments »
Jeff Clune -
2019 : Tricks of Trade 2 (Rich Caruana) »
Rich Caruana -
2019 : ARUBA: Efficient and Adaptive Meta-Learning with Provable Guarantees (Ameet Talwalkar) »
Ameet Talwalkar -
2019 : Tricks of the Trade 1 (Rich Caruana) »
Rich Caruana -
2019 Workshop: Adaptive and Multitask Learning: Algorithms & Systems »
Maruan Al-Shedivat · Anthony Platanios · Otilia Stretcu · Jacob Andreas · Ameet Talwalkar · Rich Caruana · Tom Mitchell · Eric Xing -
2019 Workshop: Workshop on Multi-Task and Lifelong Reinforcement Learning »
Sarath Chandar · Shagun Sodhani · Khimya Khetarpal · Tom Zahavy · Daniel J. Mankowitz · Shie Mannor · Balaraman Ravindran · Doina Precup · Chelsea Finn · Abhishek Gupta · Amy Zhang · Kyunghyun Cho · Andrei A Rusu · Facebook Rob Fergus -
2019 : Closing Remarks »
Frank Hutter -
2019 : Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret »
Michal Valko -
2019 : Panel Discussion »
Wenpeng Zhang · Charles Sutton · Liam Li · Rachel Thomas · Erin LeDell -
2019 : Poster discussion »
Roman Novak · Maxime Gabella · Frederic Dreyer · Siavash Golkar · Anh Tong · Irina Higgins · Mirco Milletari · Joe Antognini · Sebastian Goldt · Adín Ramírez Rivera · Roberto Bondesan · Ryo Karakida · Remi Tachet des Combes · Michael Mahoney · Nicholas Walker · Stanislav Fort · Samuel Smith · Rohan Ghosh · Aristide Baratin · Diego Granziol · Stephen Roberts · Dmitry Vetrov · Andrew Wilson · César Laurent · Valentin Thomas · Simon Lacoste-Julien · Dar Gilboa · Daniel Soudry · Anupam Gupta · Anirudh Goyal · Yoshua Bengio · Erich Elsen · Soham De · Stanislaw Jastrzebski · Charles H Martin · Samira Shabanian · Aaron Courville · Shorato Akaho · Lenka Zdeborova · Ethan Dyer · Maurice Weiler · Pim de Haan · Taco Cohen · Max Welling · Ping Luo · zhanglin peng · Nasim Rahaman · Loic Matthey · Danilo J. Rezende · Jaesik Choi · Kyle Cranmer · Lechao Xiao · Jaehoon Lee · Yasaman Bahri · Jeffrey Pennington · Greg Yang · Jiri Hron · Jascha Sohl-Dickstein · Guy Gur-Ari -
2019 : Contributed Talk 3: Random Search and Reproducibility for Neural Architecture Search »
Liam Li -
2019 : Contributed Talk 2: Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents »
Zalán Borsos -
2019 : Michal Valko: How Negative Dependence Broke the Quadratic Barrier for Learning with Graphs and Kernels »
Michal Valko -
2019 : Networking Lunch (provided) + Poster Session »
Abraham Stanway · Alex Robson · Aneesh Rangnekar · Ashesh Chattopadhyay · Ashley Pilipiszyn · Benjamin LeRoy · Bolong Cheng · Ce Zhang · Chaopeng Shen · Christian Schroeder · Christian Clough · Clement DUHART · Clement Fung · Cozmin Ududec · Dali Wang · David Dao · di wu · Dimitrios Giannakis · Dino Sejdinovic · Doina Precup · Duncan Watson-Parris · Gege Wen · George Chen · Gopal Erinjippurath · Haifeng Li · Han Zou · Herke van Hoof · Hillary A Scannell · Hiroshi Mamitsuka · Hongbao Zhang · Jaegul Choo · James Wang · James Requeima · Jessica Hwang · Jinfan Xu · Johan Mathe · Jonathan Binas · Joonseok Lee · Kalai Ramea · Kate Duffy · Kevin McCloskey · Kris Sankaran · Lester Mackey · Letif Mones · Loubna Benabbou · Lynn Kaack · Matthew Hoffman · Mayur Mudigonda · Mehrdad Mahdavi · Michael McCourt · Mingchao Jiang · Mohammad Mahdi Kamani · Neel Guha · Niccolo Dalmasso · Nick Pawlowski · Nikola Milojevic-Dupont · Paulo Orenstein · Pedram Hassanzadeh · Pekka Marttinen · Ramesh Nair · Sadegh Farhang · Samuel Kaski · Sandeep Manjanna · Sasha Luccioni · Shuby Deshpande · Soo Kim · Soukayna Mouatadid · Sunghyun Park · Tao Lin · Telmo Felgueira · Thomas Hornigold · Tianle Yuan · Tom Beucler · Tracy Cui · Volodymyr Kuleshov · Wei Yu · yang song · Ydo Wexler · Yoshua Bengio · Zhecheng Wang · Zhuangfang Yi · Zouheir Malki -
2019 : Contributed Talk 1: A Boosting Tree Based AutoML System for Lifelong Machine Learning »
Zheng Xiong -
2019 : panel discussion with Craig Boutilier (Google Research), Emma Brunskill (Stanford), Chelsea Finn (Google Brain, Stanford, UC Berkeley), Mohammad Ghavamzadeh (Facebook AI), John Langford (Microsoft Research) and David Silver (Deepmind) »
Peter Stone · Craig Boutilier · Emma Brunskill · Chelsea Finn · John Langford · David Silver · Mohammad Ghavamzadeh -
2019 : invited talk by John Langford (Microsoft Research): How do we make Real World Reinforcement Learning revolution? »
John Langford -
2019 : Welcome »
Frank Hutter -
2019 Workshop: 6th ICML Workshop on Automated Machine Learning (AutoML 2019) »
Frank Hutter · Joaquin Vanschoren · Katharina Eggensperger · Matthias Feurer · Matthias Feurer -
2019 Workshop: ICML 2019 Time Series Workshop »
Vitaly Kuznetsov · Scott Yang · Rose Yu · Cheng Tang · Yuyang Wang -
2019 Poster: Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback »
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban -
2019 Poster: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Poster: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Poster: Non-Monotonic Sequential Text Generation »
Sean Welleck · Kiante Brantley · Hal Daumé III · Kyunghyun Cho -
2019 Poster: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Poster: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2019 Poster: Online Learning with Sleeping Experts and Feedback Graphs »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang -
2019 Oral: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Oral: Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback »
Chicheng Zhang · Alekh Agarwal · Hal Daumé III · John Langford · Sahand Negahban -
2019 Oral: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2019 Oral: Non-Monotonic Sequential Text Generation »
Sean Welleck · Kiante Brantley · Hal Daumé III · Kyunghyun Cho -
2019 Oral: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Oral: Online Variance Reduction with Mixtures »
Zalán Borsos · Sebastian Curi · Yehuda Levy · Andreas Krause -
2019 Oral: Online Learning with Sleeping Experts and Feedback Graphs »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang -
2019 Poster: Differentiable Dynamic Normalization for Learning Deep Representation »
Ping Luo · Peng Zhanglin · Shao Wenqi · Zhang ruimao · Ren jiamin · Wu lingyun -
2019 Poster: Provable Guarantees for Gradient-Based Meta-Learning »
Nina Balcan · Mikhail Khodak · Ameet Talwalkar -
2019 Poster: Disentangled Graph Convolutional Networks »
Jianxin Ma · Peng Cui · Kun Kuang · Xin Wang · Wenwu Zhu -
2019 Poster: NAS-Bench-101: Towards Reproducible Neural Architecture Search »
Chris Ying · Aaron Klein · Eric Christiansen · Esteban Real · Kevin Murphy · Frank Hutter -
2019 Poster: Active Learning with Disagreement Graphs »
Corinna Cortes · Giulia DeSalvo · Mehryar Mohri · Ningshan Zhang · Claudio Gentile -
2019 Poster: Trajectory-Based Off-Policy Deep Reinforcement Learning »
Andreas Doerr · Michael Volpp · Marc Toussaint · Sebastian Trimpe · Christian Daniel -
2019 Poster: Tensor Variable Elimination for Plated Factor Graphs »
Fritz Obermeyer · Elias Bingham · Martin Jankowiak · Neeraj Pradhan · Justin Chiu · Alexander Rush · Noah Goodman -
2019 Oral: Active Learning with Disagreement Graphs »
Corinna Cortes · Giulia DeSalvo · Mehryar Mohri · Ningshan Zhang · Claudio Gentile -
2019 Oral: Disentangled Graph Convolutional Networks »
Jianxin Ma · Peng Cui · Kun Kuang · Xin Wang · Wenwu Zhu -
2019 Oral: Differentiable Dynamic Normalization for Learning Deep Representation »
Ping Luo · Peng Zhanglin · Shao Wenqi · Zhang ruimao · Ren jiamin · Wu lingyun -
2019 Oral: Tensor Variable Elimination for Plated Factor Graphs »
Fritz Obermeyer · Elias Bingham · Martin Jankowiak · Neeraj Pradhan · Justin Chiu · Alexander Rush · Noah Goodman -
2019 Oral: Provable Guarantees for Gradient-Based Meta-Learning »
Nina Balcan · Mikhail Khodak · Ameet Talwalkar -
2019 Oral: Trajectory-Based Off-Policy Deep Reinforcement Learning »
Andreas Doerr · Michael Volpp · Marc Toussaint · Sebastian Trimpe · Christian Daniel -
2019 Oral: NAS-Bench-101: Towards Reproducible Neural Architecture Search »
Chris Ying · Aaron Klein · Eric Christiansen · Esteban Real · Kevin Murphy · Frank Hutter -
2019 Poster: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks »
Mingxing Tan · Quoc Le -
2019 Poster: Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks »
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee-Whye Teh -
2019 Poster: Policy Certificates: Towards Accountable Reinforcement Learning »
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill -
2019 Poster: Provably efficient RL with Rich Observations via Latent State Decoding »
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford -
2019 Poster: Contextual Memory Trees »
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro -
2019 Oral: Provably efficient RL with Rich Observations via Latent State Decoding »
Simon Du · Akshay Krishnamurthy · Nan Jiang · Alekh Agarwal · Miroslav Dudik · John Langford -
2019 Oral: Policy Certificates: Towards Accountable Reinforcement Learning »
Christoph Dann · Lihong Li · Wei Wei · Emma Brunskill -
2019 Oral: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks »
Mingxing Tan · Quoc Le -
2019 Oral: Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks »
Juho Lee · Yoonho Lee · Jungtaek Kim · Adam Kosiorek · Seungjin Choi · Yee-Whye Teh -
2019 Oral: Contextual Memory Trees »
Wen Sun · Alina Beygelzimer · Hal Daumé III · John Langford · Paul Mineiro -
2019 Tutorial: Algorithm configuration: learning in the space of algorithm designs »
Kevin Leyton-Brown · Frank Hutter -
2019 Tutorial: Active Hypothesis Testing: An Information Theoretic (re)View »
Tara Javidi -
2019 Tutorial: Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms »
Jeff Clune · Joel Lehman · Kenneth Stanley -
2018 Poster: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Poster: Online Learning with Abstention »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang -
2018 Poster: Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace »
Yoonho Lee · Seungjin Choi -
2018 Poster: Pathwise Derivatives Beyond the Reparameterization Trick »
Martin Jankowiak · Fritz Obermeyer -
2018 Oral: Pathwise Derivatives Beyond the Reparameterization Trick »
Martin Jankowiak · Fritz Obermeyer -
2018 Oral: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Oral: Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace »
Yoonho Lee · Seungjin Choi -
2018 Oral: Online Learning with Abstention »
Corinna Cortes · Giulia DeSalvo · Claudio Gentile · Mehryar Mohri · Scott Yang -
2018 Poster: Ranking Distributions based on Noisy Sorting »
Adil El Mesaoudi-Paul · Eyke Hüllermeier · Robert Busa-Fekete -
2018 Poster: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Poster: A Distributed Second-Order Algorithm You Can Trust »
Celestine Mendler-Dünner · Aurelien Lucchi · Matilde Gargiani · Yatao Bian · Thomas Hofmann · Martin Jaggi -
2018 Oral: Ranking Distributions based on Noisy Sorting »
Adil El Mesaoudi-Paul · Eyke Hüllermeier · Robert Busa-Fekete -
2018 Oral: A Distributed Second-Order Algorithm You Can Trust »
Celestine Mendler-Dünner · Aurelien Lucchi · Matilde Gargiani · Yatao Bian · Thomas Hofmann · Martin Jaggi -
2018 Oral: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Poster: Differentiable plasticity: training plastic neural networks with backpropagation »
Thomas Miconi · Kenneth Stanley · Jeff Clune -
2018 Poster: Learning Deep ResNet Blocks Sequentially using Boosting Theory »
Furong Huang · Jordan Ash · John Langford · Robert Schapire -
2018 Poster: BOHB: Robust and Efficient Hyperparameter Optimization at Scale »
Stefan Falkner · Aaron Klein · Frank Hutter -
2018 Oral: Learning Deep ResNet Blocks Sequentially using Boosting Theory »
Furong Huang · Jordan Ash · John Langford · Robert Schapire -
2018 Oral: BOHB: Robust and Efficient Hyperparameter Optimization at Scale »
Stefan Falkner · Aaron Klein · Frank Hutter -
2018 Oral: Differentiable plasticity: training plastic neural networks with backpropagation »
Thomas Miconi · Kenneth Stanley · Jeff Clune -
2017 : Faster graph bandit learning using information about the neighbors »
Michal Valko -
2017 Workshop: Picky Learners: Choosing Alternative Ways to Process Data. »
Corinna Cortes · Kamalika Chaudhuri · Giulia DeSalvo · Ningshan Zhang · Chicheng Zhang -
2017 Workshop: Time Series Workshop »
Vitaly Kuznetsov · Yan Liu · Scott Yang · Rose Yu -
2017 Poster: Projection-free Distributed Online Learning in Networks »
Wenpeng Zhang · Peilin Zhao · Wenwu Zhu · Steven Hoi · Tong Zhang -
2017 Talk: Projection-free Distributed Online Learning in Networks »
Wenpeng Zhang · Peilin Zhao · Wenwu Zhu · Steven Hoi · Tong Zhang -
2017 Poster: Learning Deep Architectures via Generalized Whitened Neural Networks »
Ping Luo -
2017 Poster: Variational Boosting: Iteratively Refining Posterior Approximations »
Andrew Miller · Nicholas J Foti · Ryan P. Adams -
2017 Poster: Bayesian inference on random simple graphs with power law degree distributions »
Juho Lee · Creighton Heaukulani · Zoubin Ghahramani · Lancelot F. James · Seungjin Choi -
2017 Poster: Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening »
Mohsen Ahmadi Fahandar · Eyke Hüllermeier · Ines Couso -
2017 Poster: Contextual Decision Processes with low Bellman rank are PAC-Learnable »
Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2017 Poster: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
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: Variational Boosting: Iteratively Refining Posterior Approximations »
Andrew Miller · Nicholas J Foti · Ryan P. Adams -
2017 Talk: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2017 Talk: Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening »
Mohsen Ahmadi Fahandar · Eyke Hüllermeier · Ines Couso -
2017 Talk: Contextual Decision Processes with low Bellman rank are PAC-Learnable »
Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire -
2017 Talk: Learning Deep Architectures via Generalized Whitened Neural Networks »
Ping Luo -
2017 Talk: Bayesian inference on random simple graphs with power law degree distributions »
Juho Lee · Creighton Heaukulani · Zoubin Ghahramani · Lancelot F. James · Seungjin Choi -
2017 Poster: Logarithmic Time One-Against-Some »
Hal Daumé · Nikos Karampatziakis · John Langford · Paul Mineiro -
2017 Poster: Safety-Aware Algorithms for Adversarial Contextual Bandit »
Wen Sun · Debadeepta Dey · Ashish Kapoor -
2017 Poster: AdaNet: Adaptive Structural Learning of Artificial Neural Networks »
Corinna Cortes · Xavi Gonzalvo · Vitaly Kuznetsov · Mehryar Mohri · Scott Yang -
2017 Poster: Active Learning for Cost-Sensitive Classification »
Akshay Krishnamurthy · Alekh Agarwal · Tzu-Kuo Huang · Hal Daumé III · John Langford -
2017 Poster: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Talk: Active Learning for Cost-Sensitive Classification »
Akshay Krishnamurthy · Alekh Agarwal · Tzu-Kuo Huang · Hal Daumé III · John Langford -
2017 Talk: AdaNet: Adaptive Structural Learning of Artificial Neural Networks »
Corinna Cortes · Xavi Gonzalvo · Vitaly Kuznetsov · Mehryar Mohri · Scott Yang -
2017 Talk: Safety-Aware Algorithms for Adversarial Contextual Bandit »
Wen Sun · Debadeepta Dey · Ashish Kapoor -
2017 Talk: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Daniele Calandriello · Alessandro Lazaric · Michal Valko -
2017 Talk: Logarithmic Time One-Against-Some »
Hal Daumé · Nikos Karampatziakis · John Langford · Paul Mineiro -
2017 Tutorial: Real World Interactive Learning »
Alekh Agarwal · John Langford