2 citations · 3 across the 10 of their papers we have counts for
11 papers
A Theoretical Analysis of Discrete Flow Matching Generative Models
Maojiang Su, Mingcheng Lu, Jerry Yao-Chieh Hu +4
We provide a theoretical analysis for end-to-end training Discrete Flow Matching (DFM) generative models. DFM is a promising discrete generative modeling framework that learns the…
Are Hallucinations Bad Estimations?
Hude Liu, Jerry Yao-Chieh Hu, Jennifer Yuntong Zhang +2
We formalize hallucinations in generative models as failures to link an estimate to any plausible cause. Under this interpretation, we show that even loss-minimizing optimal estima…
Minimalist Softmax Attention Provably Learns Constrained Boolean Functions
Jerry Yao-Chieh Hu, Xiwen Zhang, Maojiang Su +2
We study the computational limits of learning -bit Boolean functions (specifically, , , and their noisy variants), using a minimalist single-head soft…
Attention Mechanism, Max-Affine Partition, and Universal Approximation
Hude Liu, Jerry Yao-Chieh Hu, Zhao Song +1
We establish the universal approximation capability of single-layer, single-head self- and cross-attention mechanisms with minimal attached structures. Our key insight is to interp…
On Differentially Private String Distances
Jerry Yao-Chieh Hu, Erzhi Liu, Han Liu +2
Given a database of bit strings , a fundamental data structure task is to estimate the distances between a given query with all the st…
In-Context Deep Learning via Transformer Models
Weimin Wu, Maojiang Su, Jerry Yao-Chieh Hu +2
We investigate the transformer's capability to simulate the training process of deep models via in-context learning (ICL), i.e., in-context deep learning. Our key contribution is p…
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani +3
We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on \emph{single-head} tran…
Differentially Private Kernel Density Estimation
Erzhi Liu, Jerry Yao-Chieh Hu, Alex Reneau +2
We introduce a refined differentially private (DP) data structure for kernel density estimation (KDE), offering not only improved privacy-utility tradeoff but also better efficienc…
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
Jerry Yao-Chieh Hu, Weimin Wu, Zhao Song +1
We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study th…
Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
Jerry Yao-Chieh Hu, Maojiang Su, En-Jui Kuo +2
We study the computational limits of Low-Rank Adaptation (LoRA) for finetuning transformer-based models using fine-grained complexity theory. Our key observation is that the existe…
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song +1
We investigate the computational limits of the memory retrieval dynamics of modern Hopfield models from the fine-grained complexity analysis. Our key contribution is the characteri…