Cheng-Hao Liu

According to our database, Cheng-Hao Liu authored at least 19 papers between 2020 and 2025.

Collaborative distances:

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

2025

SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling.

Andrei Rekesh, Miruna T. Cretu, Dmytro Shevchuk, Vignesh Ram Somnath, Pietro Liò, Robert A. Batey, Mike Tyers, Michal Koziarski, Cheng-Hao Liu

CoRR, July, 2025

Generating π-Functional Molecules Using STGG+ with Active Learning.

Alexia Jolicoeur-Martineau, Yan Zhang, Boris Knyazev, Aristide Baratin, Cheng-Hao Liu

CoRR, February, 2025

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction.

Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Cheng-Hao Liu, Sarthak Mittal, Nouha Dziri, Michael M. Bronstein, Pranam Chatterjee, Alexander Tong, Joey Bose

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Flow Factorization for Efficient Generative Flow Networks.

Jiashun Liu, Chunhui Li, Cheng-Hao Liu, Dianbo Liu, Qingpeng Cai, Ling Pan

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Multi-Fidelity Active Learning with GFlowNets.

Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

Trans. Mach. Learn. Res., 2024

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction.

Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng, Zachary Quinn, Cheng-Hao Liu, Sarthak Mittal, Nouha Dziri, Michael M. Bronstein, Yoshua Bengio, Pranam Chatterjee, Alexander Tong, Avishek Joey Bose

CoRR, 2024

Bifurcated Generative Flow Networks.

Chunhui Li, Cheng-Hao Liu, Dianbo Liu, Qingpeng Cai, Ling Pan

CoRR, 2024

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation.

Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose

CoRR, 2024

Generative Active Learning for the Search of Small-molecule Protein Binders.

Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio

CoRR, 2024

RGFN: Synthesizable Molecular Generation Using GFlowNets.

Michal Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gainski, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, Robert A. Batey

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

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation.

Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose

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

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong

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

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization.

Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio

Proceedings of the Twelfth International Conference on Learning Representations, 2024

SE(3)-Stochastic Flow Matching for Protein Backbone Generation.

Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael M. Bronstein, Alexander Tong

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Towards equilibrium molecular conformation generation with GFlowNets.

Alexandra Volokhova, Michal Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca A. Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio

CoRR, 2023

Thompson sampling for improved exploration in GFlowNets.

Jarrid Rector-Brooks, Kanika Madan, Moksh Jain, Maksym Korablyov, Cheng-Hao Liu, Sarath Chandar, Nikolay Malkin, Yoshua Bengio

CoRR, 2023

GFlowNets for AI-Driven Scientific Discovery.

Moksh Jain, Tristan Deleu, Jason S. Hartford, Cheng-Hao Liu, Alex Hernández-García, Yoshua Bengio

CoRR, 2023

2022

RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software.

Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler

J. Chem. Inf. Model., 2022

2020

RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design.

Cheng-Hao Liu, Maksym Korablyov, Stanislaw Jastrzebski, Pawel Wlodarczyk-Pruszynski, Yoshua Bengio, Marwin H. S. Segler

CoRR, 2020