Posted by Cat Armato and Jaqui Herman, Program Managers
Groups across Google are actively pursuing research across the field of machine learning, ranging from theory to application. With scalable tools and architectures, we build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, and more.
Google is proud to be a Platinum Sponsor of the thirty-eighth International Conference on Machine Learning (ICML 2021), a premier annual event happening this week. As a leader in machine learning research — with over 100 accepted publications and Googlers participating in workshops — we look forward to our continued partnership with the broader machine learning research community.
Registered for ICML 2021? We hope you’ll visit the Google virtual booth to learn more about the exciting work, creativity, and fun that goes into solving a portion of the field’s most interesting challenges. Take a look below to learn more about the Google research being presented at ICML 2021 (Google affiliations in bold).
ICML Board Members include: Corinna Cortes, Hugo Larochelle, Shakir Mohamed
ICML Emeritus Board includes: William Cohen, Andrew McCallum
Tutorial Co-Chair member: Quoc Lee
Attention Is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Scalable Evaluation of Multi-agent Reinforcement Learning with Melting Pot
Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li*, Csaba Szepesvari, Dale Schuurmans
Low-Rank Sinkhorn Factorization
Meyer Scetbon, Marco Cuturi, Gabriel Peyré
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
PID Accelerated Value Iteration Algorithm
Amir-Massoud Farahmand, Mohammad Ghavamzadeh
Dueling Convex Optimization
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