MB
Program Committee
Organizers
NIPS 2015
DB
Program Committee
David Balduzzi
Program Committee
Samy Bengio
AB
Program Committee
Alina Beygelzimer
DB
Program Committee
Daniel A Braun
EB
Program Committee
Emma Brunskill
GC
Program Committee
Gal Chechik
Program Committee
Kyunghyun Cho
Kyunghyun Cho - Glen de Vries Professor of Health Statistics, NYU; Executive Director of Frontier Research, Prescient Design, Genentech Cho's work spans machine learning and natural language processing. He co-developed the Gated Recurrent Unit (GRU) architecture and has contributed to neural machine translation and sequence-to-sequence learning. He is a CIFAR Fellow of Learning in Machines & Brains and received the 2021 Samsung Ho-Am Prize in Engineering. He served as program chair for ICLR 2020, NeurIPS 2022, and ICML 2022.
Kyunghyun Cho - Glen de Vries Professor of Health Statistics, NYU; Executive Director of Frontier Research, Prescient Design, Genentech Cho's work spans machine learning and natural language processing. He co-developed the Gated Recurrent Unit (GRU) architecture and has contributed to neural machine translation and sequence-to-sequence learning. He is a CIFAR Fellow of Learning in Machines & Brains and received the 2021 Samsung Ho-Am Prize in Engineering. He served as program chair for ICLR 2020, NeurIPS 2022, and ICML 2022.
SC
Program Committee
Seungjin Choi
AC
Program Committee
Aaron Courville
MC
Program Committee
Marco Cuturi
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
Program Committee
Marc Deisenroth
Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.
Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book Mathematics for Machine Learning published by Cambridge University Press (2020).
Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book Mathematics for Machine Learning published by Cambridge University Press (2020).
LD
Program Committee
Li Deng
ID
Program Committee
Inderjit Dhillon
FD
Program Committee
Francesco Dinuzzo
FD
Program Committee
Florence d'Alche-Buc
EF
Program Committee
Emily Fox
KF
Program Committee
Kenji Fukumizu
TG
Program Committee
Thomas Gaertner
Program Committee
Amir Globerson
Amir Globerson received a BSc in computer science and physics from the Hebrew University, and a PhD in computational neuroscience from the Hebrew University. After his PhD, he was a postdoctoral fellow at the University of Toronto and a Rothschild postdoctoral fellow at MIT. He joined the Hebrew University school of computer science in 2008, and moved to the Tel Aviv University School of Computer Science in 2016. He is also a research scientist at Google and is currently on sabbatical at Google NYC. He served as an Associate Editor in Chief for the IEEE Transactions on Pattern Analysis And Machine Intelligence. His work has received several paper awards (at NeurIPS,UAI, and ICML). In 2018 he served as program co-chair for the UAI conference, and in 2019 he was the general co-chair for UAI in Tel Aviv. In 2019 he received the ERC consolidator grant. He is serving as program co-chair at NeurIPS 2023, and will serve as NeurIPS 2024 general chair.
Amir Globerson received a BSc in computer science and physics from the Hebrew University, and a PhD in computational neuroscience from the Hebrew University. After his PhD, he was a postdoctoral fellow at the University of Toronto and a Rothschild postdoctoral fellow at MIT. He joined the Hebrew University school of computer science in 2008, and moved to the Tel Aviv University School of Computer Science in 2016. He is also a research scientist at Google and is currently on sabbatical at Google NYC. He served as an Associate Editor in Chief for the IEEE Transactions on Pattern Analysis And Machine Intelligence. His work has received several paper awards (at NeurIPS,UAI, and ICML). In 2018 he served as program co-chair for the UAI conference, and in 2019 he was the general co-chair for UAI in Tel Aviv. In 2019 he received the ERC consolidator grant. He is serving as program co-chair at NeurIPS 2023, and will serve as NeurIPS 2024 general chair.
IG
Program Committee
Ian Goodfellow
MG
Program Committee
Moritz Grosse-Wentrup
BH
Program Committee
Bohyung Han
EH
Program Committee
Elad Hazan
XH
Program Committee
Xiaofei He
TI
Program Committee
Tomoharu Iwata
MK
Program Committee
Mohammad Emtiyaz Khan
Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also a visiting professor at the Tokyo University of Agriculture and Technology (TUAT). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For the past 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.
Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also a visiting professor at the Tokyo University of Agriculture and Technology (TUAT). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For the past 10 years, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.
KK
Program Committee
Kee-Eung Kim
SK
Program Committee
Samory Kpotufe
AK
Program Committee
Andreas Krause
JK
Program Committee
James Kwok
Program Committee
Christoph Lampert
Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. In 2010 he joined the Institute of Science and Technology Austria (ISTA) first as an Assistant Professor and since 2015 as a Professor. There, he leads the research group for Machine Learning and Computer Vision, and since 2019 he is also the head of ISTA's ELLIS unit.
Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. In 2010 he joined the Institute of Science and Technology Austria (ISTA) first as an Assistant Professor and since 2015 as a Professor. There, he leads the research group for Machine Learning and Computer Vision, and since 2019 he is also the head of ISTA's ELLIS unit.
JL
Program Committee
John Langford
John Langford is a machine learning research scientist, a field which he says "is shifting from an academic discipline to an industrial tool". He is the author of the weblog hunch.net and the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002. He was the program co-chair for the 2012 International Conference on Machine Learning.
John Langford is a machine learning research scientist, a field which he says "is shifting from an academic discipline to an industrial tool". He is the author of the weblog hunch.net and the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002. He was the program co-chair for the 2012 International Conference on Machine Learning.
HL
Program Committee
Hugo Larochelle
PL
Program Committee
Pavel Laskov
SL
Program Committee
Svetlana Lazebnik
HL
Program Committee
Honglak Lee
WL
Program Committee
Wee Sun Lee
Wee Sun Lee is a professor in the Department of Computer Science, National University of Singapore. He obtained his B.Eng from the University of Queensland in 1992 and his Ph.D. from the Australian National University in 1996. He has been a research fellow at the Australian Defence Force Academy, a fellow of the Singapore-MIT Alliance, and a visiting scientist at MIT.
His research interests include machine learning, planning under uncertainty, and approximate inference. His works have won the Test of Time Award at Robotics: Science and Systems (RSS) 2021, the RoboCup Best Paper Award at International Conference on Intelligent Robots and Systems (IROS) 2015, the Google Best Student Paper Award, Uncertainty in AI (UAI) 2014 (as faculty co-author), as well as several competitions and challenges.
He has been an area chair for machine learning and AI conferences such as the Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the AAAI Conference on Artificial Intelligence (AAAI), and the International Joint Conference on Artificial Intelligence (IJCAI). He was a program, conference and journal track co-chair for the Asian Conference on Machine Learning (ACML), and he is currently the co-chair of the steering committee of ACML.
Wee Sun Lee is a professor in the Department of Computer Science, National University of Singapore. He obtained his B.Eng from the University of Queensland in 1992 and his Ph.D. from the Australian National University in 1996. He has been a research fellow at the Australian Defence Force Academy, a fellow of the Singapore-MIT Alliance, and a visiting scientist at MIT.
His research interests include machine learning, planning under uncertainty, and approximate inference. His works have won the Test of Time Award at Robotics: Science and Systems (RSS) 2021, the RoboCup Best Paper Award at International Conference on Intelligent Robots and Systems (IROS) 2015, the Google Best Student Paper Award, Uncertainty in AI (UAI) 2014 (as faculty co-author), as well as several competitions and challenges.
He has been an area chair for machine learning and AI conferences such as the Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the AAAI Conference on Artificial Intelligence (AAAI), and the International Joint Conference on Artificial Intelligence (IJCAI). He was a program, conference and journal track co-chair for the Asian Conference on Machine Learning (ACML), and he is currently the co-chair of the steering committee of ACML.
HL
Program Committee
Hang Li
CL
Program Committee
Chih-Jen Lin
YL
Program Committee
Yuanqing Lin
Program Committee
Hsuan-Tien Lin
Professor Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008 and has been promoted to full professor in 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier for marketing. Currently, he keeps growing with Appier as its Chief Data Science Consultant. From the university, Prof. Lin received the Distinguished Teaching Awards in 2011 and 2021, the Outstanding Mentoring Award in 2013, and five Outstanding Teaching Awards between 2016 and 2020. He co-authored the introductory machine learning textbook Learning from Data and offered two popular Mandarin-teaching MOOCs Machine Learning Foundations and Machine Learning Techniques based on the textbook. He served in the machine learning community as Progam Co-chair of NeurIPS 2020, Expo Co-chair of ICML 2021, and Workshop Chair of NeurIPS 2022 and 2023. He co-led the teams that won the champion of KDDCup 2010, the double-champion of the two tracks in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013.
Professor Hsuan-Tien Lin received a B.S. in Computer Science and Information Engineering from National Taiwan University in 2001, an M.S. and a Ph.D. in Computer Science from California Institute of Technology in 2005 and 2008, respectively. He joined the Department of Computer Science and Information Engineering at National Taiwan University as an assistant professor in 2008 and has been promoted to full professor in 2017. Between 2016 and 2019, he worked as the Chief Data Scientist of Appier, a startup company that specializes in making AI easier for marketing. Currently, he keeps growing with Appier as its Chief Data Science Consultant. From the university, Prof. Lin received the Distinguished Teaching Awards in 2011 and 2021, the Outstanding Mentoring Award in 2013, and five Outstanding Teaching Awards between 2016 and 2020. He co-authored the introductory machine learning textbook Learning from Data and offered two popular Mandarin-teaching MOOCs Machine Learning Foundations and Machine Learning Techniques based on the textbook. He served in the machine learning community as Progam Co-chair of NeurIPS 2020, Expo Co-chair of ICML 2021, and Workshop Chair of NeurIPS 2022 and 2023. He co-led the teams that won the champion of KDDCup 2010, the double-champion of the two tracks in KDDCup 2011, the champion of track 2 in KDDCup 2012, and the double-champion of the two tracks in KDDCup 2013.
ZL
Program Committee
Zhouchen Lin
TL
Program Committee
Tie-Yan Liu
Tie-Yan Liu is an assistant managing director of Microsoft Research Asia, leading the machine learning research area. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, Windows, Xbox, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. He has also been actively contributing to academic communities. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. He has published 200+ papers in refereed conferences and journals, with over 17000 citations. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ACL, ICTIR, as well as associate editor of ACM Transactions on Information Systems, ACM Transactions on the Web, and Neurocomputing. Tie-Yan Liu is a fellow of the IEEE, and a distinguished member of the ACM.
Tie-Yan Liu is an assistant managing director of Microsoft Research Asia, leading the machine learning research area. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, Windows, Xbox, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. He has also been actively contributing to academic communities. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. He has published 200+ papers in refereed conferences and journals, with over 17000 citations. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ACL, ICTIR, as well as associate editor of ACM Transactions on Information Systems, ACM Transactions on the Web, and Neurocomputing. Tie-Yan Liu is a fellow of the IEEE, and a distinguished member of the ACM.
AL
Program Committee
Aurelie Lozano
DM
Program Committee
David Mcallester
MM
Program Committee
Marina Meila
Program Committee
Shakir Mohamed
Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.
Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.
CM
Program Committee
Claire Monteleoni
Claire Monteleoni is an associate professor of Computer Science at University of Colorado Boulder. Previously, she was an associate professor at George Washington University, and research faculty at the Center for Computational Learning Systems, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Masters in Computer Science, at MIT. She holds a Bachelors in Earth and Planetary Sciences from Harvard. Her research focuses on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and climate informatics: accelerating discovery in climate science with machine learning. Her work on climate informatics received the Best Application Paper Award at NASA CIDU 2010. In 2011, she co-founded the International Workshop on Climate Informatics, which is now in its fourth year, attracting climate scientists and data scientists from over 14 countries and 26 states.
Claire Monteleoni is an associate professor of Computer Science at University of Colorado Boulder. Previously, she was an associate professor at George Washington University, and research faculty at the Center for Computational Learning Systems, at Columbia University. She did a postdoc in Computer Science and Engineering at the University of California, San Diego, and completed her PhD and Masters in Computer Science, at MIT. She holds a Bachelors in Earth and Planetary Sciences from Harvard. Her research focuses on machine learning algorithms and theory for problems including learning from data streams, learning from raw (unlabeled) data, learning from private data, and climate informatics: accelerating discovery in climate science with machine learning. Her work on climate informatics received the Best Application Paper Award at NASA CIDU 2010. In 2011, she co-founded the International Workshop on Climate Informatics, which is now in its fourth year, attracting climate scientists and data scientists from over 14 countries and 26 states.
GM
Program Committee
Greg Mori
RM
Program Committee
Remi Munos
SN
Program Committee
Shinichi Nakajima
SN
Program Committee
Sebastian Nowozin
CO
Program Committee
Cheng Soon Ong
PO
Program Committee
Peter Orbanz
Peter Orbanz is a research fellow at the University of Cambridge. He holds
a PhD degree from ETH Zurich and will join the Statistics Faculty at
Columbia University as an Assistant Professor in 2012. He is interested in
the mathematical and algorithmic aspects of Bayesian nonparametric models
and of related learning technologies.
Peter Orbanz is a research fellow at the University of Cambridge. He holds a PhD degree from ETH Zurich and will join the Statistics Faculty at Columbia University as an Assistant Professor in 2012. He is interested in the mathematical and algorithmic aspects of Bayesian nonparametric models and of related learning technologies.
SP
Program Committee
Sinno Jialin Pan
BP
Program Committee
Barnabas Poczos
MP
Program Committee
Massimiliano Pontil
NQ
Program Committee
Novi Quadrianto
AR
Program Committee
Alain Rakotomamonjy
GR
Program Committee
Gunnar Rätsch
CR
Program Committee
Cynthia Rudin
RS
Program Committee
Russ Salakhutdinov
IS
Program Committee
Issei Sato
CS
Program Committee
Clay Scott
MS
Program Committee
Matthias Seeger
DS
Program Committee
Dino Sejdinovic
Dino Sejdinovic is a Professor at the School of Computer and Mathematical Sciences, University of Adelaide. He was previously a Lecturer and an Associate Professor at the Department of Statistics, University of Oxford (2014-2022). He held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Institute for Statistical Science, University of Bristol (2009-2011). He received a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006).
Dino Sejdinovic is a Professor at the School of Computer and Mathematical Sciences, University of Adelaide. He was previously a Lecturer and an Associate Professor at the Department of Statistics, University of Oxford (2014-2022). He held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Institute for Statistical Science, University of Bristol (2009-2011). He received a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006).
YS
Program Committee
Yevgeny Seldin
JS
Program Committee
Jianbo Shi
AS
Program Committee
Aarti Singh
LS
Program Committee
Le Song
NS
Program Committee
Nati Srebro
BS
Program Committee
Bharath Sriperumbudur
AS
Program Committee
Alan A Stocker
AS
Program Committee
Amos Storkey
IS
Program Committee
Ilya Sutskever
TS
Program Committee
Taiji Suzuki
CS
Program Committee
Csaba Szepesvari
IT
Program Committee
Ichiro Takeuchi
TT
Program Committee
Toshiyuki Tanaka
RT
Program Committee
Ryota Tomioka
IT
Program Committee
Ivor Tsang
KT
Program Committee
Koji Tsuda
NU
Program Committee
Naonori Ueda
LV
Program Committee
Laurens van der Maaten
RV
Program Committee
René Vidal
SV
Program Committee
S.V.N. Vishwanathan
LW
Program Committee
Liwei Wang
SW
Program Committee
Sinead Williamson
EX
Program Committee
Eric Xing
HX
Program Committee
Huan Xu
EY
Program Committee
Eunho Yang
JY
Program Committee
Jieping Ye
JY
Program Committee
Jingyi Yu
BY
Program Committee
Byron M Yu
XZ
Program Committee
Xinhua Zhang
ZZ
Program Committee
Zhi-Hua Zhou
DZ
Program Committee
Denny Zhou
JZ
Program Committee
Jerry Zhu
JZ
Program Committee
Jun Zhu
TS
President
Terrence Sejnowski
Demonstration Chair
Marc'Aurelio Ranzato
YN
Program Chair Assistant
Yung-Kyun Noh
PO
Program Chair Assistant
Pedro Ortega
CC
General Chair
Corinna Cortes
NL
General Chair
Neil D Lawrence
DL
Program Chair
Daniel Lee
MS
Program Chair
Masashi Sugiyama
RH
Tutorial Chair
Ralf Herbrich
TD
Workshop Series Editors
Thomas Dietterich
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois
1979; PhD Stanford University 1984) is Professor and Director of
Intelligent Systems Research at Oregon State University. Among his
contributions to machine learning research are (a) the formalization
of the multiple-instance problem, (b) the development of the
error-correcting output coding method for multi-class prediction, (c)
methods for ensemble learning, (d) the development of the MAXQ
framework for hierarchical reinforcement learning, and (e) the
application of gradient tree boosting to problems of structured
prediction and latent variable models. Dietterich has pursued
application-driven fundamental research in many areas including drug
discovery, computer vision, computational sustainability, and
intelligent user interfaces.
Dietterich has served the machine learning community in a variety of
roles including Executive Editor of the Machine Learning journal,
co-founder of the Journal of Machine Learning Research, editor of the
MIT Press Book Series on Adaptive Computation and Machine Learning,
and editor of the Morgan-Claypool Synthesis series on Artificial
Intelligence and Machine Learning. He was Program Co-Chair of
AAAI-1990, Program Chair of NIPS-2000, and General Chair of
NIPS-2001. He was first President of the International Machine
Learning Society (the parent organization of ICML) and served a term
on the NIPS Board of Trustees and the Council of AAAI.
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor and Director of Intelligent Systems Research at Oregon State University. Among his contributions to machine learning research are (a) the formalization of the multiple-instance problem, (b) the development of the error-correcting output coding method for multi-class prediction, (c) methods for ensemble learning, (d) the development of the MAXQ framework for hierarchical reinforcement learning, and (e) the application of gradient tree boosting to problems of structured prediction and latent variable models. Dietterich has pursued application-driven fundamental research in many areas including drug discovery, computer vision, computational sustainability, and intelligent user interfaces.
Dietterich has served the machine learning community in a variety of roles including Executive Editor of the Machine Learning journal, co-founder of the Journal of Machine Learning Research, editor of the MIT Press Book Series on Adaptive Computation and Machine Learning, and editor of the Morgan-Claypool Synthesis series on Artificial Intelligence and Machine Learning. He was Program Co-Chair of AAAI-1990, Program Chair of NIPS-2000, and General Chair of NIPS-2001. He was first President of the International Machine Learning Society (the parent organization of ICML) and served a term on the NIPS Board of Trustees and the Council of AAAI.
MJ
Workshop Series Editors
Michael Jordan
BB
Workshop Chair
Borja Balle
MC
Workshop Chair
Marco Cuturi
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
Marco Cuturi is a research scientist at Apple, in Paris. He received his Ph.D. in 11/2005 from the Ecole des Mines de Paris in applied mathematics. Before that he graduated from National School of Statistics (ENSAE) with a master degree (MVA) from ENS Cachan. He worked as a post-doctoral researcher at the Institute of Statistical Mathematics, Tokyo, between 11/2005 and 3/2007 and then in the financial industry between 4/2007 and 9/2008. After working at the ORFE department of Princeton University as a lecturer between 2/2009 and 8/2010, he was at the Graduate School of Informatics of Kyoto University between 9/2010 and 9/2016 as a tenured associate professor. He joined ENSAE in 9/2016 as a professor, where he is now working part-time. He was at Google between 10/2018 and 1/2022. His main employment is now with Apple, since 1/2022, as a research scientist working on fundamental aspects of machine learning.
KW
Publications Chair
Kilian Q Weinberger
YN
Program Manager
Yung-Kyun Noh
PO
Program Manager
Pedro Ortega
MP
Executive Director
Mary Ellen Perry
CC
Symposia Chairs
Corinna Cortes
NL
Symposia Chairs
Neil D Lawrence
MB
Treasurer
Marian S Bartlett
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