4 citations · 12 across the 13 of their papers we have counts for
8 papers · 1 filter
Compute-Optimal Pretrain--Fine-tune in Ridge Gradient Descent
Alex Buna, Fanghui Liu, Patrick Rebeschini
Pretraining followed by fine-tuning introduces a compute-allocation problem: under a fixed training budget, compute spent improving the upstream objective reduces the compute avail…
Shallow ReLU Networks in -Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization
Weizhao Li, Fanghui Liu, Lei Shi
This paper studies approximation by shallow ReLU networks, , together with their generalization behavior under path-norm control. For the -t…
How does feature learning reshape the function space?
João Lobo, Bruno Loureiro, Long Tran-Than +1
Feature learning is widely regarded as the key mechanism distinguishing neural networks from fixed-kernel methods, yet its impact on the induced function space remains poorly under…
Self-Regularized Learning Methods
Max Schölpple, Liu Fanghui, Ingo Steinwart
We introduce a general framework for analyzing learning algorithms based on the notion of self-regularization, which captures implicit complexity control without requiring explicit…
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
Yihang Chen, Fanghui Liu, Taiji Suzuki +1
This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of h…
Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias
Honam Wong, Wendao Wu, Fanghui Liu +1
Recent advances in machine learning have inspired a surge of research into reconstructing specific quantities of interest from measurements that comply with certain physical laws.…
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
Fanghui Liu, Leello Dadi, Volkan Cevher
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evad…
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
Jiayuan Ye, Zhenyu Zhu, Fanghui Liu +2
We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, w…