1 citations · 1 across the 8 of their papers we have counts for
8 papers
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory
Jiajun Liang, Qian Zhang, Wei Deng +2
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimat…
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability
Rajdeep Haldar, Yue Xing, Qifan Song
The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two not…
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
Yue Xing, Xiaofeng Lin, Qifan Song +3
Pre-training is known to generate universal representations for downstream tasks in large-scale deep learning such as large language models. Existing literature, e.g., \cite{kim202…
Personalized Federated X -armed Bandit
Wenjie Li, Qifan Song, Jean Honorio
In this work, we study the personalized federated -armed bandit problem, where the heterogeneous local objectives of the clients are optimized simultaneously in the fe…
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training
Rajdeep Haldar, Qifan Song
Adversarial attacks are usually expressed in terms of a gradient-based operation on the input data and model, this results in heavy computations every time an attack is generated.…
Matrix Completion from General Deterministic Sampling Patterns
Hanbyul Lee, Rahul Mazumder, Qifan Song +1
Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or th…