2 citations · 4 across the 15 of their papers we have counts for
16 papers
MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching
Jiahui Huang, Yasi Zhang, Tianyu Chen +6
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality…
REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
Yasi Zhang, Tianyu Chen, Mingyuan Zhou +3
Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Re…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
EdiVal-Agent: An Object-Centric Framework for Automated, Fine-Grained Evaluation of Multi-Turn Editing
Tianyu Chen, Yasi Zhang, Zhi Zhang +13
Instruction-based image editing has advanced rapidly, yet reliable and interpretable evaluation remains a bottleneck. Current protocols either (i) depend on paired reference images…
Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
Yasi Zhang, Tianyu Chen, Zhendong Wang +3
Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD…
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Tianyu Chen, Yasi Zhang, Zhendong Wang +3
Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on…
Learning Difference-of-Convex Regularizers for Inverse Problems: A Flexible Framework with Theoretical Guarantees
Yasi Zhang, Oscar Leong
Learning effective regularization is crucial for solving ill-posed inverse problems, which arise in a wide range of scientific and engineering applications. While data-driven metho…
Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory
Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang +4
Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these…
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory
Zhi Zhang, Chris Chow, Yasi Zhang +7
Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC
Yaxuan Zhu, Zehao Dou, Haoxin Zheng +3
Recent studies demonstrate that diffusion models can serve as a strong prior for solving inverse problems. A prominent example is Diffusion Posterior Sampling (DPS), which approxim…
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Yasi Zhang, Peiyu Yu, Yaxuan Zhu +4
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the in…
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Peiyu Yu, Dinghuai Zhang, Hengzhi He +10
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating r…
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models
Yasi Zhang, Peiyu Yu, Ying Nian Wu
Text-to-image diffusion models have shown great success in generating high-quality text-guided images. Yet, these models may still fail to semantically align generated images with…
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
Yingshan Chang, Yasi Zhang, Zhiyuan Fang +3
The literature on text-to-image generation is plagued by issues of faithfully composing entities with relations. But there lacks a formal understanding of how entity-relation compo…
Transductive Matrix Completion with Calibration for Multi-Task Learning
Hengfang Wang, Yasi Zhang, Xiaojun Mao +1
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful…