Representative Publications

Think While You Generate: Discrete Diffusion with Planned Denoising. Paper | Code
Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi Jaakkola, Rafael Gómez-Bombarelli.
Under submission, 2024.

Flow Matching for Accelerated Simulation of Atomic Transport in Materials. Paper
Juno Nam, Sulin Liu, Gavin Winter, KyuJung Jun, Soojung Yang, Rafael Gómez-Bombarelli.
Under submission, 2024.
short version at ICML Workshop on Machine Learning for Life and Material Science: From Theory to Industry applications (Best paper in Material Science track, 2/141)

Generative Marginalization Models. Paper | Code | Video
Sulin Liu, Peter J. Ramadge, Ryan P. Adams.
International Conference on Machine Learning (ICML), 2024.
short version at ICML Workshop on Structured Probabilistic Inference & Generative Modeling. (Contributed talk, 6/125)

Sparse Bayesian Optimization. Paper | Code | Tutorial | Video
Sulin Liu* (equal contr.), Qing Feng*, David Eriksson*, Benjamin Letham, Eytan Bakshy.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
short version at at NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems. (Contributed talk, top 5 selected)

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. Paper | Code | Slides | Video
Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams.
Advances in Neural Information Processing Systems (NeurIPS), 2020.
short version at 7th ICML Workshop on Automated Machine Learning. (Spotlight talk)

Full Publications

A Chemically-Guided Generative Diffusion Model for Materials Synthesis Planning.
Elton Pan, Soonhyoung Kwon, Sulin Liu, Mingrou Xie, Yifei Duan, Thorben Prein, Killian Sheriff, Yuriy Roman, Manuel Moliner, Rafael Gómez-Bombarelli, Elsa Olivetti.
short version at NeurIPS AI for Accelerated Materials Design Workshop, 2024. (Oral spotlight)

Scaling Autoregressive Models for Lattice Thermodynamics.
Xiaochen Du, Sulin Liu, Rafael Gómez-Bombarelli.
short version at NeurIPS AI for Accelerated Materials Design Workshop, 2024.

Towards Long Rollout of Neural Operators with Local Attention and Flow Matching-inspired Correction: An Example in Frontal Polymerization PDEs.
Pengfei Cai, Sulin Liu, Qibang Liu, Philippe Geubelle, Rafael Gómez-Bombarelli.
short version at NeurIPS Machine Learning and the Physical Sciences Workshop, 2024.

Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching.
Akshay Subramanian, Shuhui Qu, Cheol Woo Park, Sulin Liu, Janghwan Lee, Rafael Gómez-Bombarelli.
short version at NeurIPS Machine Learning and the Physical Sciences Workshop, 2024.

ProBF : Probabilistic Safety Certificates with Barrier Functions. Paper | Code
Athindran Ramesh Kumar*, Sulin Liu* (equal contr., random order), Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge. Preprint, 2021.
short version at NeurIPS Safe and Robust Control of Uncertain Systems Workshop, 2021.

Revisiting the Landscape of Matrix Factorization. Paper | Video
Hossein Valavi, Sulin Liu, Peter J. Ramadge.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. (Oral presentation)

Data Poisoning Attacks on Multi-Task Relationship Learning. Paper
Mengchen Zhao, Bo An, Yaodong Yu, Sulin Liu, Sinno Jialin Pan.
AAAI Conference on Artificial Intelligence (AAAI), 2018.

Adaptive Group Sparse Multi-task Learning via Trace Lasso. Paper
Sulin Liu, Sinno Jialin Pan.
International Joint Conference on Artificial Intelligence (IJCAI), 2017.

Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problems. Paper
Yaodong Yu*, Sulin Liu* (equal contr.), Sinno Jialin Pan.
Conference on Uncertainty in Artificial Intelligence (UAI), 2017.

Distributed Multi-Task Relationship Learning. Paper | Video
Sulin Liu, Sinno Jialin Pan, Qirong Ho.
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. (Oral presentation)