15 papers
D-IMPL: A Diffusion-based Solver for Parameterized BBOs
Yang Hu, Na Li
Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samp…
ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings
Na Li, Yuchen Jiao, Changxiao Cai +1
Recent advances in continuous diffusion and flow-based language models (LMs) have achieved performance competitive with discrete LMs. However, existing continuous frameworks still…
Provable diffusion-based posterior sampling for linear inverse problems via DDIM
Yuchen Jiao, Na Li, Changxiao Cai +2
Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantee…
Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning
Bo Dai, Na Li, Dale Schuurmans
Self-supervised learning (SSL) has improved empirical performance by unleashing the power of unlabeled data for practical applications. Specifically, SSL extracts the representatio…
Spectral Representation-based Reinforcement Learning
Chenxiao Gao, Haotian Sun, Na Li +2
In real-world applications with large state and action spaces, reinforcement learning (RL) typically employs function approximations to represent core components like the policies,…
DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
Tao Li, Qing Li, Na Li +1
Accurately predicting the upgrade of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is crucial for surgical planning. However, traditional deep learning methods…
Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach
Yuchen Jiao, Na Li, Changxiao Cai +1
Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inhe…
Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making
Haldun Balim, Na Li, Yilun Du
Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability for control. We propose Model Predicti…
TARDis: Time Attenuated Representation Disentanglement for Incomplete Multi-Modal Tumor Segmentation and Classification
Zishuo Wan, Qinqin Kang, Na Li +6
The accurate diagnosis and segmentation of tumors in contrast-enhanced Computed Tomography (CT) are fundamentally driven by the distinctive hemodynamic profiles of contrast agents…
Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces
Haitong Ma, Ofir Nabati, Aviv Rosenberg +7
Reinforcement learning (RL) struggles to scale to large, combinatorial action spaces common in many real-world problems. This paper introduces a novel framework for training discre…
One-Step Flow Policy Mirror Descent
Tianyi Chen, Haitong Ma, Na Li +2
Diffusion policies have achieved great success in online reinforcement learning (RL) due to their strong expressive capacity. However, the inference of diffusion policy models reli…
Efficient Online Reinforcement Learning for Diffusion Policy
Haitong Ma, Tianyi Chen, Kai Wang +2
Diffusion policies have achieved superior performance in imitation learning and offline reinforcement learning (RL) due to their rich expressiveness. However, the conventional diff…
Scalable spectral representations for multi-agent reinforcement learning in network MDPs
Zhaolin Ren, Runyu Zhang, Bo Dai +1
Network Markov Decision Processes (MDPs), a popular model for multi-agent control, pose a significant challenge to efficient learning due to the exponential growth of the global st…
Primal-Dual Spectral Representation for Off-policy Evaluation
Yang Hu, Tianyi Chen, Na Li +2
Off-policy evaluation (OPE) is one of the most fundamental problems in reinforcement learning (RL) to estimate the expected long-term payoff of a given target policy with only expe…
An AI-Driven Approach to Wind Turbine Bearing Fault Diagnosis from Acoustic Signals
Zhao Wang, Xiaomeng Li, Na Li +1
This study aimed to develop a deep learning model for the classification of bearing faults in wind turbine generators from acoustic signals. A convolutional LSTM model was successf…