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Author Information
Glen Cowan (Royal Holloway, University of London)
Balázs Kégl (Université Paris Saclay/CNRS)
Kyle Cranmer (New York University)
Gábor Melis (Google Deepmind)
Tim Salimans (Algoritmica)
Vladimir Vava Gligorov (CERN)
Daniel Whiteson (University of California Irvine)
Lester Mackey (Microsoft Research)
Wojciech Kotlowski (Poznan University of Technology, Poland)
Roberto Díaz Morales (University Carlos III de Madrid)
Pierre Baldi (UC Irvine)
Cecile Germain (Universite Paris Sud)
David Rousseau (LAL-Orsay)
Particle physicist, studying the Higgs Boson on the ATLAS experiment at the LHC at CERN, passionate about applying ML algorithms to fundamental research. Organized the HiggsML challenge in 2014 and now the [TrackML challenge] (https://sites.google.com/site/trackmlparticle/home)
Isabelle Guyon (U. Paris-Saclay & ChaLearn)
Isabelle Guyon recently joined Google Brain as a research scientist. She is also professor of artificial intelligence at Université Paris-Saclay (Orsay). Her areas of expertise include computer vision, bioinformatics, and power systems. She is best known for being a co-inventor of Support Vector Machines. Her recent interests are in automated machine learning, meta-learning, and data-centric AI. She has been a strong promoter of challenges and benchmarks, and is president of ChaLearn, a non-profit dedicated to organizing machine learning challenges. She is community lead of Codalab competitions, a challenge platform used both in academia and industry. She co-organized the “Challenges in Machine Learning Workshop” @ NeurIPS between 2014 and 2019, launched the "NeurIPS challenge track" in 2017 while she was general chair, and pushed the creation of the "NeurIPS datasets and benchmark track" in 2021, as a NeurIPS board member.
Tianqi Chen (OctoML)
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2023 Oral: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate models »
Sungduk Yu · Walter Hannah · Liran Peng · Jerry Lin · Mohamed Aziz Bhouri · Ritwik Gupta · Björn Lütjens · Justus Will · Gunnar Behrens · Nora Loose · Charles Stern · Tom Beucler · Bryce Harrop · Benjamin Hillman · Andrea Jenney · Savannah Ferretti · Nana Liu · Animashree Anandkumar · Noah Brenowitz · Veronika Eyring · Nicholas Geneva · Pierre Gentine · Stephan Mandt · Jaideep Pathak · Akshay Subramaniam · Carl Vondrick · Rose Yu · Laure Zanna · Ryan Abernathey · Fiaz Ahmed · David Bader · Pierre Baldi · Elizabeth Barnes · Christopher Bretherton · Julius Busecke · Peter Caldwell · Wayne Chuang · Yilun Han · YU HUANG · Fernando Iglesias-Suarez · Sanket Jantre · Karthik Kashinath · Marat Khairoutdinov · Thorsten Kurth · Nicholas Lutsko · Po-Lun Ma · Griffin Mooers · J. David Neelin · David Randall · Sara Shamekh · Mark Taylor · Nathan Urban · Janni Yuval · Guang Zhang · Tian Zheng · Mike Pritchard -
2023 Competition: NeurIPS 2023 Machine Unlearning Competition »
Eleni Triantafillou · Fabian Pedregosa · Meghdad Kurmanji · Kairan ZHAO · Gintare Karolina Dziugaite · Peter Triantafillou · Ioannis Mitliagkas · Vincent Dumoulin · Lisheng Sun · Peter Kairouz · Julio C Jacques Junior · Jun Wan · Sergio Escalera · Isabelle Guyon -
2022 : Adaptive Bias Correction for Improved Subseasonal Forecast »
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2022 Competition: Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Practical Domains »
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2022 : Foundations of Attention Mechanisms in Deep Neural Network Architectures »
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2022 Poster: Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification »
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2022 : Isabelle Guyon »
Isabelle Guyon -
2022 Invited Talk: The Data-Centric Era: How ML is Becoming an Experimental Science »
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2021 Panel: The Role of Benchmarks in the Scientific Progress of Machine Learning »
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2021 Poster: XDO: A Double Oracle Algorithm for Extensive-Form Games »
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2020 Poster: Deep Statistical Solvers »
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2020 Poster: Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games »
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2019 : Lester Mackey (Microsoft Research and Stanford) »
Lester Mackey -
2019 : Climate Change: A Grand Challenge for ML »
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2019 : Conclusion on TrackML, a Particle Physics Tracking Machine Learning Challenge Combining Accuracy and Inference Speed »
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2019 : Welcome and Opening Remarks »
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2019 Poster: Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes »
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2018 : Poster Session (All Posters) »
Stephen Macke · Hongzi Mao · Caroline Lemieux · Saim Salman · Rishikesh Jha · Hanrui Wang · Shoumik P Palkar · Tianqi Chen · Thomas Pumir · Vaishnav Janardhan · adit bhardwaj · Ed Chi -
2018 : Afternoon Welcome - Isabelle Guyon and Evelyne Viegas »
Isabelle Guyon -
2018 Workshop: CiML 2018 - Machine Learning competitions "in the wild": Playing in the real world or in real time »
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2018 : TrackML Conclusion and Outlook on the on-going Throughput phase »
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2018 : Datasets and Benchmarks for Causal Learning »
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2018 : AutoML3 - LifeLong ML with concept drift Challenge: Overview and award ceremony »
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2018 : Evaluating Causation Coefficients »
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2018 Poster: Learning to Optimize Tensor Programs »
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2018 Spotlight: Learning to Optimize Tensor Programs »
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2018 Poster: On Neuronal Capacity »
Pierre Baldi · Roman Vershynin -
2018 Oral: On Neuronal Capacity »
Pierre Baldi · Roman Vershynin -
2017 : Evolutionary Strategies »
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2017 : Baázs Kégl, RAMP platform »
Balázs Kégl -
2017 Workshop: Machine Learning Challenges as a Research Tool »
Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy -
2017 : Introduction - Isabelle Guyon and Evelyne Viegas »
Isabelle Guyon -
2017 : Poster session 2 and coffee break »
Sean McGregor · Tobias Hagge · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 : Updates from Current ML Systems (TensorFlow, PyTorch, Caffe2, CNTK, MXNet, TVM, Clipper, MacroBase, ModelDB) »
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2017 : Poster session »
Abbas Zaidi · Christoph Kurz · David Heckerman · YiJyun Lin · Stefan Riezler · Ilya Shpitser · Songbai Yan · Olivier Goudet · Yash Deshpande · Judea Pearl · Jovana Mitrovic · Brian Vegetabile · Tae Hwy Lee · Karen Sachs · Karthika Mohan · Reagan Rose · Julius Ramakers · Negar Hassanpour · Pierre Baldi · Razieh Nabi · Noah Hammarlund · Eli Sherman · Carolin Lawrence · Fattaneh Jabbari · Vira Semenova · Maria Dimakopoulou · Pratik Gajane · Russell Greiner · Ilias Zadik · Alexander Blocker · Hao Xu · Tal EL HAY · Tony Jebara · Benoit Rostykus -
2017 : Poster session 1 and coffee break »
Tobias Hagge · Sean McGregor · Markus Stoye · Trang Thi Minh Pham · Seungkyun Hong · Amir Farbin · Sungyong Seo · Susana Zoghbi · Daniel George · Stanislav Fort · Steven Farrell · Arthur Pajot · Kyle Pearson · Adam McCarthy · Cecile Germain · Dustin Anderson · Mario Lezcano Casado · Mayur Mudigonda · Benjamin Nachman · Luke de Oliveira · Li Jing · Lingge Li · Soo Kyung Kim · Timothy Gebhard · Tom Zahavy -
2017 Poster: Random Permutation Online Isotonic Regression »
Wojciech Kotlowski · Wouter Koolen · Alan Malek -
2016 : Out-of-class novelty generation: an experimental foundation »
Balázs Kégl -
2016 Workshop: Machine Learning for Spatiotemporal Forecasting »
Florin Popescu · Sergio Escalera · Xavier Baró · Stephane Ayache · Isabelle Guyon -
2016 : Gaming challenges and encouraging collaborations »
Sergio Escalera · Isabelle Guyon -
2016 : Challenges in education »
Balázs Kégl · Ben Hamner -
2016 Workshop: Challenges in Machine Learning: Gaming and Education »
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2016 Demonstration: Biometric applications of CNNs: get a job at "Impending Technologies"! »
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2016 Poster: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks »
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2016 Oral: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks »
Tim Salimans · Diederik Kingma -
2016 Poster: Improving Variational Autoencoders with Inverse Autoregressive Flow »
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2016 Poster: Improved Techniques for Training GANs »
Tim Salimans · Ian Goodfellow · Wojciech Zaremba · Vicki Cheung · Alec Radford · Peter Chen · Xi Chen -
2015 Workshop: Challenges in Machine Learning (CiML 2015): "Open Innovation" and "Coopetitions" »
Isabelle Guyon · Evelyne Viegas · Ben Hamner · Balázs Kégl -
2015 Workshop: Machine Learning Systems »
Alex Beutel · Tianqi Chen · Sameer Singh · Elaine Angelino · Markus Weimer · Joseph Gonzalez -
2015 : The HiggsML Story »
Balázs Kégl -
2015 : Open ML Problems in High Energy Physics »
Daniel Whiteson -
2015 Poster: A Complete Recipe for Stochastic Gradient MCMC »
Yi-An Ma · Tianqi Chen · Emily Fox -
2015 Poster: Measuring Sample Quality with Stein's Method »
Jackson Gorham · Lester Mackey -
2015 Spotlight: Measuring Sample Quality with Stein's Method »
Jackson Gorham · Lester Mackey -
2015 Poster: Variational Dropout and the Local Reparameterization Trick »
Diederik Kingma · Tim Salimans · Max Welling -
2014 Workshop: Challenges in Machine Learning workshop (CiML 2014) »
Isabelle Guyon · Evelyne Viegas · Percy Liang · Olga Russakovsky · Rinat Sergeev · Gábor Melis · Michele Sebag · Gustavo Stolovitzky · Jaume Bacardit · Michael S Kim · Ben Hamner -
2014 Poster: Searching for Higgs Boson Decay Modes with Deep Learning »
Peter Sadowski · Daniel Whiteson · Pierre Baldi -
2014 Spotlight: Searching for Higgs Boson Decay Modes with Deep Learning »
Peter Sadowski · Daniel Whiteson · Pierre Baldi -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Poster: Understanding Dropout »
Pierre Baldi · Peter Sadowski -
2013 Oral: Understanding Dropout »
Pierre Baldi · Peter Sadowski -
2012 Poster: Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction »
Pietro Di Lena · Pierre Baldi · Ken Nagata -
2012 Spotlight: Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction »
Pietro Di Lena · Pierre Baldi · Ken Nagata -
2012 Demonstration: Gesture recognition with Kinect »
Isabelle Guyon -
2011 Poster: A Machine Learning Approach to Predict Chemical Reactions »
Matthew A Kayala · Pierre Baldi -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2010 Workshop: Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning »
Pierre Baldi · Klaus-Robert Müller · Gisbert Schneider -
2009 Workshop: Clustering: Science or art? Towards principled approaches »
Margareta Ackerman · Shai Ben-David · Avrim Blum · Isabelle Guyon · Ulrike von Luxburg · Robert Williamson · Reza Zadeh -
2009 Mini Symposium: Causality and Time Series Analysis »
Florin Popescu · Isabelle Guyon · Guido Nolte -
2009 Demonstration: Causality Workbench »
Isabelle Guyon -
2008 Workshop: Causality: objectives and assessment »
Isabelle Guyon · Dominik Janzing · Bernhard Schölkopf -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Demonstration: CLOP: a Matlab Learning Object Package »
Amir Reza Saffari Azar Alamdari · Isabelle Guyon · Hugo Jair Escalante · Gökhan H Bakir · Gavin Cawley -
2007 Poster: Mining Internet-Scale Software Repositories »
Erik Linstead · Paul Rigor · sushil bajracharya · cristina lopes · Pierre Baldi -
2006 Workshop: Multi-level Inference Workshop and Model Selection Game »
Isabelle Guyon -
2006 Poster: A Scalable Machine Learning Approach to Go »
Lin Wu · Pierre Baldi -
2006 Talk: A Scalable Machine Learning Approach to Go »
Lin Wu · Pierre Baldi