Tucker, George

Soft Actor-Critic Algorithms and Applications

Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 1,994

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

Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 1,507

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

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

Levine, S., Kumar, A., Tucker, G., & Fu, J. (2020) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 824

Regularizing Neural Networks by Penalizing Confident Output Distributions

Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., & Hinton, G. (2017) · arXiv

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 655

Widespread macromolecular interaction perturbations in human genetic disorders

Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell

Genomics and Rare DiseasesBioinformatics and Genomic Networks

Cited by 594

On Variational Bounds of Mutual Information

Poole, B., Ozair, S., Oord, A. V. D., Alemi, A. A., & Tucker, G. (2019) · arXiv

Stochastic Gradient Optimization TechniquesGaussian Processes and Bayesian Inference

Cited by 480

Model-Based Reinforcement Learning for Atari

Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 467

Learning to Walk Via Deep Reinforcement Learning

Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2019) · Robotics: Science and Systems XV

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 430

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Buckman, J., Hafner, D., Tucker, G., Brevdo, E., & Lee, H. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 161

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

Riquelme, C., Tucker, G., & Snoek, J. (2018) · arXiv

Advanced Bandit Algorithms ResearchReinforcement Learning in Robotics

Cited by 72

Learning to Walk via Deep Reinforcement Learning

Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2018) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 53

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research

Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al. (2023) · arXiv

Autonomous Vehicle Technology and SafetyTraffic control and management

Cited by 48

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Gulcehre, C., Wang, Z., Novikov, A., Paine, T. L., Colmenarejo, S. G., Zolna, K., Agarwal, R., Merel, J., Mankowitz, D., Paduraru, C., et al. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 42

Training Language Models to Self-Correct via Reinforcement Learning

Kumar, A., Zhuang, V., Agarwal, R., Su, Y., Co-Reyes, J. D., Singh, A., Baumli, K., Iqbal, S., Bishop, C., Roelofs, R., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

Kumar, A., Agarwal, R., Geng, X., Tucker, G., & Levine, S. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 4