Timezone: »

 
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

Fri Jun 14 09:40 AM -- 11:00 AM (PDT) @ None

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

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)
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

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 Stanley

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

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