Learning to Synthesize a 4D RGBD Light Field from a Single Image. - Pratul P. Srinivasan; Tongzhou Wang 001; Ashwin Sreelal; Ravi Ramamoorthi; Ren Ng (ICCV, 2017)

Neural Block Sampling. - Tongzhou Wang 001; Yi Wu 013; David A. Moore; Stuart J. Russell (CoRR, 2017)

Meta-Learning MCMC Proposals. - Tongzhou Wang 001; Yi Wu 013; Dave Moore; Stuart J. Russell (NeurIPS, 2018)

Dataset Distillation. - Tongzhou Wang 001; Jun-Yan Zhu; Antonio Torralba 001; Alexei A. Efros (CoRR, 2018)

Diverse Image Generation via Self-Conditioned GANs. - Steven Liu; Tongzhou Wang 001; David Bau; Jun-Yan Zhu; Antonio Torralba 001 (CVPR, 2020)

Rewriting a Deep Generative Model. - David Bau; Steven Liu; Tongzhou Wang 001; Jun-Yan Zhu; Antonio Torralba 001 (ECCV (1), 2020)

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. - Tongzhou Wang 001; Phillip Isola (ICML, 2020)

Learning to See by Looking at Noise. - Manel Baradad Jurjo; Jonas Wulff; Tongzhou Wang 001; Phillip Isola; Antonio Torralba 001 (NeurIPS, 2021)

Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation. - George Cazenavette; Tongzhou Wang 001; Antonio Torralba 001; Alexei A. Efros; Jun-Yan Zhu (CVPR Workshops, 2022)

Dataset Distillation by Matching Training Trajectories. - George Cazenavette; Tongzhou Wang 001; Antonio Torralba 001; Alexei A. Efros; Jun-Yan Zhu (CVPR, 2022)

Totems: Physical Objects for Verifying Visual Integrity. - Jingwei Ma; Lucy Chai; Minyoung Huh; Tongzhou Wang 001; Ser-Nam Lim; Phillip Isola; Antonio Torralba 001 (ECCV (14), 2022)

On the Learning and Learnability of Quasimetrics. - Tongzhou Wang 001; Phillip Isola (ICLR, 2022)

Denoised MDPs: Learning World Models Better Than the World Itself. - Tongzhou Wang 001; Simon S. Du; Antonio Torralba 001; Phillip Isola; Amy Zhang 001; Yuandong Tian (ICML, 2022)

Procedural Image Programs for Representation Learning. - Manel Baradad; Chun-Fu Richard Chen; Jonas Wulff; Tongzhou Wang 001; Rogério Feris; Antonio Torralba 001; Phillip Isola (NeurIPS, 2022)

On the Learning and Learnablity of Quasimetrics. - Tongzhou Wang 001; Phillip Isola (CoRR, 2022)

Improved Representation of Asymmetrical Distances with Interval Quasimetric Embeddings. - Tongzhou Wang 001; Phillip Isola (CoRR, 2022)

Generalizing Dataset Distillation via Deep Generative Prior. - George Cazenavette; Tongzhou Wang 001; Antonio Torralba 001; Alexei A. Efros; Jun-Yan Zhu (CVPR, 2023)

Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. - Tongzhou Wang 001; Antonio Torralba 001; Phillip Isola; Amy Zhang 001 (ICML, 2023)

Steerable Equivariant Representation Learning. - Sangnie Bhardwaj; Willie McClinton; Tongzhou Wang 001; Guillaume Lajoie; Chen Sun 002; Phillip Isola; Dilip Krishnan (CoRR, 2023)

Deep Augmentation: Enhancing Self-Supervised Learning through Transformations in Higher Activation Space. - Rickard Brüel Gabrielsson; Tongzhou Wang 001; Manel Baradad; Justin Solomon 001 (CoRR, 2023)

Characterizing Model Robustness via Natural Input Gradients. - Adrián Rodríguez-Muñoz; Tongzhou Wang 001; Antonio Torralba 001 (ECCV (76), 2024)

Position: The Platonic Representation Hypothesis. - Minyoung Huh; Brian Cheung; Tongzhou Wang 001; Phillip Isola (ICML, 2024)

The Platonic Representation Hypothesis. - Minyoung Huh; Brian Cheung; Tongzhou Wang 001; Phillip Isola (CoRR, 2024)

CycleGAN with Better Cycles. - Tongzhou Wang 001; Yihan Lin (CoRR, 2024)

Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning. - Rickard Brüel Gabrielsson; Tongzhou Wang 001; Manel Baradad; Justin Solomon 001 (Trans. Mach. Learn. Res., 2025)