9 papers
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
Anjian Li, Ryne Beeson
Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive t…
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
Anjian Li, Bartolomeo Stellato, Ryne Beeson
We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems. The goal is for these initia…
Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning
Ananya Trivedi, Anjian Li, Mohamed Elnoor +7
Autonomous driving requires reasoning about interactions with surrounding traffic. A prevailing approach is large-scale imitation learning on expert driving datasets, aimed at gene…
Recurrent Autoregressive Diffusion: Global Memory Meets Local Attention
Taiye Chen, Zihan Ding, Anjian Li +4
Recent advancements in video generation has shifted from bidirectional models for short videos to autoregressive ones for ultra long video generation. Previous models, which usuall…
Predictive Planner for Autonomous Driving with Consistency Models
Anjian Li, Sangjae Bae, David Isele +2
Trajectory prediction and planning are essential for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditional approaches often treat them separat…
Aligning Diffusion Model with Problem Constraints for Trajectory Optimization
Anjian Li, Ryne Beeson
Diffusion models have recently emerged as effective generative frameworks for trajectory optimization, capable of producing high-quality and diverse solutions. However, training th…