2 citations · 2 across the 10 of their papers we have counts for
12 papers
MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling
Wenbo Chen, Puheng Li, Mengyang Liu +2
Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial c…
One-Step Generative Modeling via Wasserstein Gradient Flows
Jiaqi Han, Puheng Li, Qiushan Guo +3
Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it requires many iterative updat…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
Jiaqi Han, Juntong Shi, Puheng Li +3
Diffusion models have become the dominant tool for high-fidelity image and video generation, yet are critically bottlenecked by their inference speed due to the numerous iterative…
Data-regularized Reinforcement Learning for Diffusion Models at Scale
Haotian Ye, Kaiwen Zheng, Jiashu Xu +15
Aligning generative diffusion models with human preferences via reinforcement learning (RL) is critical yet challenging. Most existing algorithms are often vulnerable to reward hac…
InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression
Haotian Ye, Qiyuan He, Jiaqi Han +12
Accurate and efficient discrete video tokenization is essential for long video sequences processing. Yet, the inherent complexity and variable information density of videos present…
Robust Sampling for Active Statistical Inference
Puheng Li, Tijana Zrnic, Emmanuel Candès
Active statistical inference is a new method for inference with AI-assisted data collection. Given a budget on the number of labeled data points that can be collected and assuming…
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
Jiaqi Han, Haotian Ye, Puheng Li +3
Diffusion-based generative models have become dominant generators of high-fidelity images and videos but remain limited by their computationally expensive inference procedures. Exi…
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity
Keyao Zhan, Puheng Li, Lei Wu
It was empirically observed in Entezari et al. (2021) that when accounting for the permutation invariance of neural networks, there is likely no loss barrier along the linear inter…
Exploring Neural Network Landscapes: Star-Shaped and Geodesic Connectivity
Zhanran Lin, Puheng Li, Lei Wu
One of the most intriguing findings in the structure of neural network landscape is the phenomenon of mode connectivity: For two typical global minima, there exists a path connecti…
On the Generalization Properties of Diffusion Models
Puheng Li, Zhong Li, Huishuai Zhang +1
Diffusion models are a class of generative models that serve to establish a stochastic transport map between an empirically observed, yet unknown, target distribution and a known p…
FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
Puheng Li, James Zou, Linjun Zhang
Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guara…