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 inductive structures in machine learning and artificial intelligence, with a focus on useful structures for better perception and decision-making and learning such structures with neural nets (e.g., invariances to imperceptible changes, distances w.r.t. decision-making capabilities, task-specific factorizations of signal and noise).
Most of my works are related to these specific topics:
- Learning fundamental structures for better AI systems, with theoretical guarantees and empirical benefits.
- Analyzing and discovering useful structures, e.g., what structure an algorithm learns, what makes for a good training set.
Outside research, I spent my time on
developing 1st offering of MIT’s Deep Learning course,
open-source ML projects,
organizing a NeurIPS workshop on Goal-Conditional RL,
mentoring SGI students (blog),
pro bono office hours (book me!),
and with 😸😼.
On 2023-2024 faculty job market: YES ✅
Email: tongzhou _AT_ mit _DOT_ edu
Selected Publications (full list)
Learned structure for better agents | Analyze and discover useful structures |
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning [ICML 2023][Project Page] [arXiv] [Code] Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang |
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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 |
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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 |
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On the Learning and Learnability of Quasimetrics [ICLR 2022] [Project Page] [arXiv] [OpenReview] [code] Tongzhou Wang, Phillip Isola |
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Learning to See by Looking at Noise
[NeurIPS 2021] [Project Page] [arXiv] [code & datasets] Manel Baradad*, Jonas Wulff*, Tongzhou Wang, Phillip Isola, Antonio Torralba |
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Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere [ICML 2020] [Project Page] [arXiv] [code] Tongzhou Wang, Phillip Isola |
![]() # 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() PyTorch implementation of the alignment and uniformity losses
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Dataset Distillation [Project Page] [arXiv] [code] [DD Papers] Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros |
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