4 citations · 8 across the 13 of their papers we have counts for
14 papers
FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff +2
Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Fey…
Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
Carles Domingo-Enrich, Jiequn Han
Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learn…
Extrapolative Quantum Error Mitigation in Continuous-Variable Systems beyond the Training Horizon
Jingpeng Zhang, Shengyong Li, Jie Han +3
Continuous-variable (CV) quantum systems provide a versatile platform for quantum information processing, in which quantum states can be represented in the quadrature phase space.…
Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
Minhuan Li, Jiequn Han, Pilar Cossio +1
A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experimental measurements. While sequence-…
Test-time Generalization for Physics through Neural Operator Splitting
Louis Serrano, Jiequn Han, Edouard Oyallon +2
Neural operators have shown promise in learning solution maps of partial differential equations (PDEs), but they often struggle to generalize when test inputs lie outside the train…
Self-Supervised Amortized Neural Operators for Optimal Control: Scaling Laws and Applications
Wuzhe Xu, Jiequn Han, Rongjie Lai
Optimal control provides a principled framework for transforming dynamical system models into intelligent decision-making, yet classical computational approaches are often too expe…
Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants
Chirag Modi, Jiequn Han, Eric Vanden-Eijnden +1
Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean dat…
ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
Haolan Zheng, Yanlai Chen, Jiequn Han +1
We propose a novel data-lean operator learning algorithm, the Reduced Basis Neural Operator (ReBaNO), to solve a group of PDEs with multiple distinct inputs. Inspired by the Reduce…
DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
Yinuo Ren, Wenhao Gao, Lexing Ying +2
We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods…
Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Jiequn Han, Kui Ren, Nathan Soedjak
We propose an instance-wise adaptive sampling framework for constructing compact and informative training datasets for supervised learning of inverse problem solutions. Typical lea…
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
Rudy Morel, Jiequn Han, Edouard Oyallon
We address the problem of predicting the next state of a dynamical system governed by unknown temporal partial differential equations (PDEs) using only a short trajectory. While st…
Posterior Sampling with Denoising Oracles via Tilted Transport
Joan Bruna, Jiequn Han
Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets. From a Ba…
Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations
Xu-Hui Zhou, Jiequn Han, Muhammad I. Zafar +4
Recently, the use of neural networks to accelerate the solving of partial differential equations (PDEs) has gained significant traction in both academia and industry. However, empl…
Stochastic Optimal Control Matching
Carles Domingo-Enrich, Jiequn Han, Brandon Amos +2
Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introdu…