Yuandong Tian

RESEARCH SCIENTIST | MENLO PARK, UNITED STATES

Yuandong is a Research Scientist working on deep reinforcement learning and its applications on games, and theoretical analysis of deep models. He is the Lead Scientist and Engineer for ELF OpenGo and DarkForest Go project. Prior to that, he was a researcher and engineer on the Google Self-driving Car team. Yann received a Ph.D. in Robotics Institute, Carnegie Mellon University, and a Bachelor's and Master's degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of ICCV 2013 Marr Prize Honorable Mentions.

Research Areas

Computer Vision

Core Machine Learning

Reinforcement Learning

Systems Research

Theory

Yuandong's Work

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

darkforestGO Github Repository

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Yuandong's Publications

October 14, 2022

CORE MACHINE LEARNING

SYSTEMS RESEARCH

Latent Execution for Neural Program Synthesis

Yuandong Tian, Dawn Song, Xinyun Chen

October 14, 2022

Yuandong Tian

RESEARCH SCIENTIST | MENLO PARK, UNITED STATES

Research Areas

Computer Vision

Core Machine Learning

Reinforcement Learning

Systems Research

Theory

Yuandong's Work

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

darkforestGO Github Repository

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Yuandong's Publications

CORE MACHINE LEARNING

SYSTEMS RESEARCH

Latent Execution for Neural Program Synthesis

RESEARCH

Understanding Deep Contrastive Learning via Coordinate-wise Optimization

On the Importance of Asymmetry for Siamese Representation Learning

Denoised MDPs: Learning World Models Better Than the World Itself

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

Learning Search Space Partition for Path Planning

CORE MACHINE LEARNING

SYSTEMS RESEARCH

Latent Execution for Neural Program Synthesis

REINFORCEMENT LEARNING

NovelD: A Simple yet Effective Exploration Criterion

REINFORCEMENT LEARNING

MADE: Exploration via Maximizing Deviation from Explored Regions

REINFORCEMENT LEARNING

SYSTEMS RESEARCH

Coda: An End-to-End Neural Program Decompiler

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

ML APPLICATIONS

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

RESEARCH

COMPUTER VISION

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

RESEARCH

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

RESEARCH

COMPUTER VISION

M3RL: MIND-AWARE MULTI-AGENT MANAGEMENT REINFORCEMENT LEARNING