8 citations · 15 across the 12 of their papers we have counts for
12 papers
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Ziwei Li, Shuyao Li, Xufeng Cai +12
In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this…
Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
Yi Wei, Xufeng Cai, Jelena Diakonikolas
Cyclic block coordinate methods are a fundamental class of first-order algorithms, widely used in practice for their simplicity and strong empirical performance. Yet, their theoret…
Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm
Xufeng Cai, Jason M. Altschuler, Jelena Diakonikolas
In 1960, Osborne proposed a simple iterative algorithm for matrix balancing with outstanding numerical performance. Today, it is the default preconditioning procedure before eigenv…
HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback
Xufeng Cai, Ziwei Guan, Lei Yuan +11
Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate…
Last Iterate Convergence of Incremental Methods and Applications in Continual Learning
Xufeng Cai, Jelena Diakonikolas
Incremental gradient and incremental proximal methods are a fundamental class of optimization algorithms used for solving finite sum problems, broadly studied in the literature. Ye…
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
Xufeng Cai, Ahmet Alacaoglu, Jelena Diakonikolas
Machine learning approaches relying on such criteria as adversarial robustness or multi-agent settings have raised the need for solving game-theoretic equilibrium problems. Of part…
Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds
Xufeng Cai, Cheuk Yin Lin, Jelena Diakonikolas
Stochastic gradient descent (SGD) is perhaps the most prevalent optimization method in modern machine learning. Contrary to the empirical practice of sampling from the datasets wit…
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization
Xufeng Cai, Chaobing Song, Stephen J. Wright +1
Nonconvex optimization is central in solving many machine learning problems, in which block-wise structure is commonly encountered. In this work, we propose cyclic block coordinate…
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Xufeng Cai, Chaobing Song, Cristóbal Guzmán +1
We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel varian…
On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature
Xu Cai, Chi Thanh Lam, Jonathan Scarlett
In this paper, we study error bounds for {\em Bayesian quadrature} (BQ), with an emphasis on noisy settings, randomized algorithms, and average-case performance measures. We seek t…
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai, Selwyn Gomes, Jonathan Scarlett
In this paper, we study the problem of Gaussian process (GP) bandits under relaxed optimization criteria stating that any function value above a certain threshold is "good enough".…
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
Xu Cai, Jonathan Scarlett
In this paper, we consider algorithm-independent lower bounds for the problem of black-box optimization of functions having a bounded norm is some Reproducing Kernel Hilbert Space…