Chenyu Wang

According to our database, Chenyu Wang authored at least 16 papers between 2024 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models.

Chenyu Wang, Paria Rashidinejad, DiJia Su, Song Jiang, Sid Wang, Siyan Zhao, Cai Zhou, Shannon Zejiang Shen, Feiyu Chen, Tommi S. Jaakkola, Yuandong Tian, Bo Liu

CoRR, October, 2025

Next Semantic Scale Prediction via Hierarchical Diffusion Language Models.

Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, Tommi S. Jaakkola

CoRR, October, 2025

Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models.

Sharut Gupta, Shobhita Sundaram, Chenyu Wang, Stefanie Jegelka, Phillip Isola

CoRR, October, 2025

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner.

Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang, Chubin Zhang, Muhan Zhang, Lester Mackey, Tommi S. Jaakkola, Stephen Bates, Dinghuai Zhang

CoRR, October, 2025

Inpainting-Guided Policy Optimization for Diffusion Large Language Models.

Siyan Zhao, Mengchen Liu, Jing Huang, Miao Liu, Chenyu Wang, Bo Liu, Yuandong Tian, Guan Pang, Sean Bell, Aditya Grover, Feiyu Chen

CoRR, September, 2025

Learning Diffusion Models with Flexible Representation Guidance.

Chenyu Wang, Cai Zhou, Sharut Gupta, Zongyu Lin, Stefanie Jegelka, Stephen Bates, Tommi S. Jaakkola

CoRR, July, 2025

CellFlow: Simulating Cellular Morphology Changes via Flow Matching.

Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey J. Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy

CoRR, February, 2025

Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review.

Masatoshi Uehara, Yulai Zhao, Chenyu Wang, Xiner Li, Aviv Regev, Sergey Levine, Tommaso Biancalani

CoRR, January, 2025

CellFlux: Simulating Cellular Morphology Changes via Flow Matching.

Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey J. Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design.

Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Avantika Lal, Tommi S. Jaakkola, Sergey Levine, Aviv Regev, Hanchen Wang, Tommaso Biancalani

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

An Information Criterion for Controlled Disentanglement of Multimodal Data.

Chenyu Wang, Sharut Gupta, Xinyi Zhang, Sana Tonekaboni, Stefanie Jegelka, Tommi S. Jaakkola, Caroline Uhler

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation.

Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, Serena Yeung-Levy

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding.

Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gökcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, Masatoshi Uehara

CoRR, 2024

In-Context Symmetries: Self-Supervised Learning through Contextual World Models.

Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Dirichlet Flow Matching with Applications to DNA Sequence Design.

Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Removing Biases from Molecular Representations via Information Maximization.

Chenyu Wang, Sharut Gupta, Caroline Uhler, Tommi S. Jaakkola

Proceedings of the Twelfth International Conference on Learning Representations, 2024