I am… a final-year PhD student at MIT with Antonio Torralba and Phillip Isola. I work on machine learning, representation learning, and reinforcement learning.

Research. I am interested in the two intertwined paths towards generalist agents: (1) agents trained for multi-task (2) agents that can adaptively solve new tasks. My research learns structured representations that aggregate and select information about the world from various data sources, improving efficiency and generality of decision-making agents.

Outside research, I spend my time developing 1st offering of MIT’s Deep Learning course, writing open-source ML projects GitHub User's stars, organizing a NeurIPS workshop on Goal-Conditional RL, mentoring SGI students (blog), pro bono office hours (book me!), brewing coffee ☕, travelling, and with 😸😼.

Email: tongzhou _AT_ mit _DOT_ edu

Selected Publications (full list)

Structured representation for better agents What is machine learning really learning?

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
[ICML 2023][Project Page] [arXiv] [Code]
Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang
paper thumbnail Quasimetric Geometry +
Novel Objective
(Push apart s_start & goal
while keeping local dists.)
= Optimal Value V
AND
High-Performing
Goal-Reaching Agents

Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings
[NeurIPS 2022 NeurReps Workshop] [Project Page] [arXiv] [PyTorch Package for Quasimetric Learning]
Tongzhou Wang, Phillip Isola
computing-iqe

Denoised MDPs: Learning World Models Better Than The World Itself
[ICML 2022] [Project Page] [arXiv] [code]
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian

On the Learning and Learnability of Quasimetrics
[ICLR 2022] [Project Page] [arXiv] [OpenReview] [code]
Tongzhou Wang, Phillip Isola
quasimetric-function-spaces
Learning to See by Looking at Noise
[NeurIPS 2021] [Project Page] [arXiv] [code & datasets]
Manel Baradad*, Jonas Wulff*, Tongzhou Wang, Phillip Isola, Antonio Torralba
learning-to-see-by-looking-at-noises

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
[ICML 2020] [Project Page] [arXiv] [code]
Tongzhou Wang, Phillip Isola
hypersphere_stl10_scatter_linear_output
# bsz : batch size (number of positive pairs)
# d   : latent dim
# x   : Tensor, shape=[bsz, d]
#       latents for one side of positive pairs
# y   : Tensor, shape=[bsz, d]
#       latents for the other side of positive pairs
def align_loss(x, y, alpha=2):
  return (x - y).norm(p=2, dim=1).pow(alpha).mean()
def uniform_loss(x, t=2): return torch.pdist(x, p=2).pow(2).mul(-t).exp().mean().log()
PyTorch implementation of the alignment and uniformity losses

Dataset Distillation
[Project Page] [arXiv] [code] [DD Papers]
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros
dataset_distillation_fixed_mnist