6 citations · 11 across the 6 of their papers we have counts for
6 papers
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
Juno Nam, Sulin Liu, Gavin Winter +3
Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular,…
Generative Marginalization Models
Sulin Liu, Peter J. Ramadge, Ryan P. Adams
We introduce marginalization models (MAMs), a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling by explicitly…
Sparse Bayesian Optimization
Sulin Liu, Qing Feng, David Eriksson +2
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions. However, the application of BO to areas such as recommendation…
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions
Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac +2
Safety-critical applications require controllers/policies that can guarantee safety with high confidence. The control barrier function is a useful tool to guarantee safety if we ha…
Distributed Multi-Task Relationship Learning
Sulin Liu, Sinno Jialin Pan, Qirong Ho
Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-ta…