39 citations · 175 across the 48 of their papers we have counts for
10 papers · 1 filter
Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning
Zeyang Li, Yunan Wang, Risheek Garrepalli +2
Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performance. However, training these…
Stable and Interpretable Multi-Mode Rheological Universal Differential Equations (mmRUDEs) for Data-Driven Constitutive Modeling
Mohua Das, Nicholas King, Navid Azizan +1
The Rheological Universal Differential Equation (RUDE) framework embeds neural networks within a frame-indifferent tensorial constitutive backbone and so enables data-driven discov…
Safe Meta-Reinforcement Learning via Information Space Reachability
Zeyang Li, Sunbochen Tang, Navid Azizan
Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL in real-world tasks is hinder…
Newton Matching for Generative Modeling: A Unified Framework for Fine-Tuning and Sampling
Zeyang Li, Yunan Wang, Paolo Giaretta +1
We develop Newton Matching, a unified framework for fine-tuning and sampling in generative modeling. The target is , where is the reward, the inverse tem…
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Youngjae Min, Jovin D'sa, Faizan M. Tariq +3
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios b…
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
Paolo Giaretta, Zeyang Li, Navid Azizan
Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories. Yet reliable inference-time safety enfo…
Provably Safe, Yet Scalable Reinforcement Learning
Kai S. Yun, Zeyang Li, Navid Azizan
Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints. Predominant approaches rely on soft-constrained policy optimization, whi…
HardNet++: Nonlinear Constraint Enforcement in Neural Networks
Andrea Goertzen, Kaveh Alim, Youngjae Min +1
Enforcing constraint satisfaction in neural network outputs is critical for safety, reliability, and physical fidelity in many control and decision-making applications. While soft-…
LMI-Net: Linear Matrix Inequality--Constrained Neural Networks via Differentiable Projection Layers
Sunbochen Tang, Andrea Goertzen, Navid Azizan
Linear matrix inequalities (LMIs) have played a central role in certifying stability, robustness, and forward invariance of dynamical systems. Despite rapid development in learning…
Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
Zeyang Li, Sunbochen Tang, Navid Azizan
Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.…