39 citations · 175 across the 48 of their papers we have counts for
58 papers
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.…
ECO: Energy-Constrained Operator Learning for Chaotic Dynamics with Boundedness Guarantees
Andrea Goertzen, Sunbochen Tang, Navid Azizan
Chaos is a fundamental feature of many complex dynamical systems, including weather systems and fluid turbulence. These systems are inherently difficult to predict due to their ext…
Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
Allison Li, Kristjan Greenewald, Thomas Parnell +1
Modern large language model (LLM) systems increasingly rely on multi-turn pipelines that are composed of multiple task-specific adapters, yet existing serving frameworks remain ine…
ATOM-CBF: Adaptive Safe Perception-Based Control under Out-of-Distribution Measurements
Kai S. Yun, Navid Azizan
Ensuring the safety of real-world systems is challenging, especially when they rely on learned perception modules to infer the system state from high-dimensional sensor data. These…
HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization
Zeyang Li, Kaveh Alim, Navid Azizan
Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distributions and enabling flexible gu…
Personalized Collaborative Learning with Affinity-Based Variance Reduction
Chenyu Zhang, Navid Azizan
Multi-agent learning faces a fundamental tension: leveraging distributed collaboration without sacrificing the personalization needed for diverse agents. This tension intensifies w…
Hierarchical Vision-Language Planning for Multi-Step Humanoid Manipulation
André Schakkal, Ben Zandonati, Zhutian Yang +1
Enabling humanoid robots to reliably execute complex multi-step manipulation tasks is crucial for their effective deployment in industrial and household environments. This paper pr…
Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning
Ji Young Byun, Young-Jin Park, Navid Azizan +1
As a cornerstone of patient care, clinical decision-making significantly influences patient outcomes and can be enhanced by large language models (LLMs). Although LLMs have demonst…
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Young-Jin Park, Kristjan Greenewald, Kaveh Alim +2
Process reward models (PRMs) play a central role in guiding inference-time scaling algorithms for large language models (LLMs). However, we observe that even state-of-the-art PRMs…
Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
Young Jin Park, Francois Germain, Jing Liu +6
Decision-making in building energy systems critically depends on the predictive accuracy of relevant time-series models. In scenarios lacking extensive data from a target building,…
Eluder-Dimension-Free Regret for Stage-Wise Constrained Contextual Bandits via Offline Regression
Haichen Hu, David Simchi-Levi, Navid Azizan
Contextual bandits with stage-wise constraints model decisions that must control risk while learning from their outcomes. We study this problem when only the selected action's rewa…
Activation-Informed Merging of Large Language Models
Amin Heyrani Nobari, Kaveh Alim, Ali ArjomandBigdeli +3
Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance ac…
On the Role of Transformer Feed-Forward Layers in Nonlinear In-Context Learning
Haoyuan Sun, Ali Jadbabaie, Navid Azizan
Transformer-based models demonstrate a remarkable ability for in-context learning (ICL), where they can adapt to unseen tasks from a few prompt examples without parameter updates.…
Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
Young-Jin Park, Carson Sobolewski, Navid Azizan
DETR and its variants have emerged as promising architectures for object detection, offering an end-to-end prediction pipeline. In practice, however, DETRs generate hundreds of pre…
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
Zeyang Li, Navid Azizan
Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative tasks, demonstrating impressive performance and scalability. However, deploying MARL agents in…