1 citations · 2 across the 14 of their papers we have counts for
15 papers
Convergence of cyclic steepest descent for quadratics with two distinct eigenvalues
Yu Li, Qihang Wang
The cyclic steepest descent method computes the exact steepest descent stepsize at the start of each cycle and reuses it for a fixed number of iterations. We study this met…
AKG kernel Agent: A Multi-Agent Framework for Cross-Platform Kernel Synthesis
Jinye Du, Quan Yuan, Zuyao Zhang +16
Modern AI models demand high-performance computation kernels. The growing complexity of LLMs, multimodal architectures, and recommendation systems, combined with techniques like sp…
VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automation
Yunxuan Jiang, Silan Hu, Xiaoning Wang +2
Large language models (LLMs) become increasingly integrated into data science workflows for automated system design. However, these LLM-driven data science systems rely solely on t…
A Unified Zeroth-Order Optimization Framework via Oblivious Randomized Sketching
Haishan Ye, Xiangyu Chang, Xi Chen
We propose a new framework for analyzing zeroth-order optimization (ZOO) from the perspective of \emph{oblivious randomized sketching}.In this framework, commonly used gradient est…
Can a One-Point Feedback Zeroth-order Algorithm Achieve Linear Dimension Dependent Sample Complexity?
Haishan Ye, Xiangyu Chang
We revisit the one-point feedback zeroth-order (ZO) optimization problem, a classical setting in derivative-free optimization where only a single noisy function evaluation is avail…
Stochastic Diagonal Estimation Based on Matrix Quadratic Form Oracles
Haishan Ye, Xiangyu Chang
We study the problem of estimating the diagonal of an implicitly given matrix $\Ab$. For such a matrix we have access to an oracle that allows us to evaluate the matrix quadratic f…
Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack
Jing Xue, Zhishen Sun, Haishan Ye +4
Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to det…
Continuum-armed Bandit Optimization with Batch Pairwise Comparison Oracles
Xiangyu Chang, Xi Chen, Yining Wang +1
This paper studies a bandit optimization problem where the goal is to maximize a function over periods for some unknown strongly concave function . We consider a new…
Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection
Xiangyu Chang, Guang Dai, Hao Di +1
This report presents a real-world case study demonstrating how prompt injection can attack large language model platforms such as ChatGPT according to a proposed injection framewor…
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
Haishan Ye, Yinghui Huang, Hao Di +1
We propose an enhanced zeroth-order stochastic Frank-Wolfe framework to address constrained finite-sum optimization problems, a structure prevalent in large-scale machine-learning…
AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. H…
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
Hao Di, Haishan Ye, Yueling Zhang +3
Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods.…
Anderson Acceleration Without Restart: A Novel Method with -Step Super Quadratic Convergence Rate
Haishan Ye, Dachao Lin, Xiangyu Chang +1
In this paper, we propose a novel Anderson's acceleration method to solve nonlinear equations, which does \emph{not} require a restart strategy to achieve numerical stability. We p…
Optimal Decentralized Composite Optimization for Convex Functions
Haishan Ye, Xiangyu Chang
In this paper, we focus on the decentralized composite optimization for convex functions. Because of advantages such as robust to the network and no communication bottle-neck in th…
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Hao Di, Yi Yang, Haishan Ye +1
Personalization aims to characterize individual preferences and is widely applied across many fields. However, conventional personalized methods operate in a centralized manner, po…