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Computer Science

D-Index
31
Citations
11527
World Ranking
13340
National Ranking
5336

Overview

George Tucker is a researcher affiliated with Google in the United States. Their work predominantly focuses on the field of Computer Science, with a strong specialization in Artificial Intelligence.

Their research spans multiple subfields, including:

  • Artificial Intelligence
  • Management Science and Operations Research
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Industrial and Manufacturing Engineering

George Tucker's main topics of research encompass:

  • Reinforcement Learning in Robotics
  • Advanced Bandit Algorithms Research
  • Gaussian Processes and Bayesian Inference
  • Autonomous Vehicle Technology and Safety
  • Machine Learning and Algorithms
  • Machine Learning and Data Classification
  • Generative Adversarial Networks and Image Synthesis

Most of Tucker's scholarly output is published through the venue arXiv (Cornell University), with a total of 20 publications in this repository.

Recent papers by George Tucker include:

  • Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020), published in arXiv (Cornell University)
  • Gemini: A Family of Highly Capable Multimodal Models (2023), published in arXiv (Cornell University)
  • Conservative Q-Learning for Offline Reinforcement Learning (2020), published in arXiv (Cornell University)
  • D4RL: Datasets for Deep Data-Driven Reinforcement Learning (2020), published in arXiv (Cornell University)
  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024), published in arXiv (Cornell University)

Frequent collaborators include:

  • Ofir Nachum
  • Sergey Levine
  • Aviral Kumar
  • Justin Fu
  • Cosmin Păduraru

Best Publications

  • Soft Actor-Critic Algorithms and Applications

    Tuomas Haarnoja;Aurick Zhou;Kristian Hartikainen;George Tucker

  • Efficient Bayesian mixed-model analysis increases association power in large cohorts

    Po-Ru Loh;George Tucker;Brendan K Bulik-Sullivan;Bjarni J Vilhjálmsson;Bjarni J Vilhjálmsson

  • Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

    Sergey Levine;Aviral Kumar;George Tucker;Justin Fu

  • Regularizing Neural Networks by Penalizing Confident Output Distributions

    Gabriel Pereyra;George Tucker;Jan Chorowski;Łukasz Kaiser

  • Conservative Q-Learning for Offline Reinforcement Learning

    Aviral Kumar;Aurick Zhou;George Tucker;Sergey Levine

  • Model-Based Reinforcement Learning for Atari

    Lukasz Kaiser;Mohammad Babaeizadeh;Piotr Milos;Blazej Osinski

  • Learning to Walk via Deep Reinforcement Learning

    Tuomas Haarnoja;Sehoon Ha;Aurick Zhou;Jie Tan

  • On Variational Bounds of Mutual Information

    Ben Poole;Sherjil Ozair;Aaron van den Oord;Alexander A. Alemi

  • D4RL: Datasets for Deep Data-Driven Reinforcement Learning

    Justin Fu;Aviral Kumar;Ofir Nachum;George Tucker

  • Model Based Reinforcement Learning for Atari

    Łukasz Kaiser;Mohammad Babaeizadeh;Piotr Miłos;Błażej Osiński

  • Behavior Regularized Offline Reinforcement Learning

    Yifan Wu;George Tucker;Ofir Nachum

  • Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

    Aviral Kumar;Justin Fu;Matthew Soh;George Tucker

  • REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

    George Tucker;Andriy Mnih;Chris J. Maddison;Dieterich Lawson

  • Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

    Jacob Buckman;Danijar Hafner;George Tucker;Eugene Brevdo

  • Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

    Aviral Kumar;Justin Fu;George Tucker;Sergey Levine

  • Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

    Carlos Riquelme;George Tucker;Jasper Roland Snoek

  • Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting

    Ming Sun;Anirudh Raju;George Tucker;Sankaran Panchapagesan

  • Filtering Variational Objectives

    Chris J Maddison;Dieterich Lawson;George Tucker;Nicolas Heess

  • Methods and devices for ignoring similar audio being received by a system

    Alexander David Rosen;Michael James Rodehorst;George Jay Tucker;Aaron Lee Mathers Challenner

  • Meta-Learning without Memorization

    Mingzhang Yin;George Tucker;Mingyuan Zhou;Sergey Levine

  • Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

    James Lucas;George Tucker;Roger B. Grosse;Mohammad Norouzi

  • Widespread Macromolecular Interaction Perturbations in Human Genetic Disorders

    Jian Peng;George Jay Tucker;Alexander T. Leighton;Bonnie Berger Leighton

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Po-Ru Loh
Po-Ru Loh Harvard Medical School
Alkes L. Price
Alkes L. Price Harvard University
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Jascha Sohl-Dickstein
Jascha Sohl-Dickstein Google (United States)
Shixiang Gu
Shixiang Gu Google (United States)
Benjamin M. Neale
Benjamin M. Neale Harvard University

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