69 citations · 202 across the 23 of their papers we have counts for
24 papers
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Rindranirina Ramamonjison, Haley Li, Timothy T. Yu +5
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formula…
Knowledge-Injected Federated Learning
Zhenan Fan, Zirui Zhou, Jian Pei +4
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold…
Revealing Unfair Models by Mining Interpretable Evidence
Mohit Bajaj, Lingyang Chu, Vittorio Romaniello +5
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical…
A class of singular bilinear maximal functions
Michael Christ, Zirui Zhou
Lebesgue space bounds are established for certain maximal bilinear operators. The proof combines a trilinear smoo…
Sparse bounds for the bilinear spherical maximal function
Tainara Borges, Benjamin Foster, Yumeng Ou +2
We derive sparse bounds for the bilinear spherical maximal function in any dimension . When , this immediately recovers the sharp bound of…
Fair and efficient contribution valuation for vertical federated learning
Zhenan Fan, Huang Fang, Xinglu Wang +4
Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as…
Cubic regularization methods with second-order complexity guarantee based on a new subproblem reformulation
Rujun Jiang, Zhishuo Zhou, Zirui Zhou
The cubic regularization (CR) algorithm has attracted a lot of attentions in the literature in recent years. We propose a new reformulation of the cubic regularization subproblem.…
Penalty and Augmented Lagrangian Methods for Constrained DC Programming
Zhaosong Lu, Zhe Sun, Zirui Zhou
In this paper we consider a class of structured nonsmooth difference-of-convex (DC) constrained DC program in which the first convex component of the objective and constraints is t…
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Liang Hu, Jiangcheng Zhu, Zirui Zhou +3
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs. Elastic training is a service e…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou +4
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federat…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…
Training Fair Models in Federated Learning without Data Privacy Infringement
Xin Che, Jingdi Hu, Zirui Zhou +2
Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data…
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method
Peng Wang, Huikang Liu, Zirui Zhou +1
In this paper, we study the problem of exact community recovery in the symmetric stochastic block model, where a graph of vertices is randomly generated by partitioning the ver…
Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence
Changxin Liu, Zirui Zhou, Jian Pei +2
Decentralized optimization, particularly the class of decentralized composite convex optimization (DCCO) problems, has found many applications. Due to ubiquitous communication cong…
Personalized Cross-Silo Federated Learning on Non-IID Data
Yutao Huang, Lingyang Chu, Zirui Zhou +4
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We…
Non-Convex Exact Community Recovery in Stochastic Block Model
Peng Wang, Zirui Zhou, Anthony Man-Cho So
Community detection in graphs that are generated according to stochastic block models (SBMs) has received much attention lately. In this paper, we focus on the binary symmetric SBM…
Analysis on hybrid fractals
Patricia Alonso Ruiz, Yuming Chen, Haotian Gu +2
We introduce hybrid fractals as a class of fractals constructed by gluing several fractal pieces in a specific manner and study energy forms and Laplacians on them. We consider in…
Iteration complexity of first-order augmented Lagrangian methods for convex conic programming
Zhaosong Lu, Zirui Zhou
In this paper we consider a class of convex conic programming. In particular, we first propose an inexact augmented Lagrangian (I-AL) method that resembles the classical I-AL metho…
On the Quadratic Convergence of the Cubic Regularization Method under a Local Error Bound Condition
Man-Chung Yue, Zirui Zhou, Anthony Man-Cho So
In this paper we consider the cubic regularization (CR) method for minimizing a twice continuously differentiable function. While the CR method is widely recognized as a globally c…
Partially Bounded Transformations have Trivial Centralizer
Johann Gaebler, Alexander Kastner, Cesar E. Silva +2
We prove that for infinite rank-one transformations satisfying a property called "partial boundedness," the only commuting transformations are powers of the original transformation…
A Family of Inexact SQA Methods for Non-Smooth Convex Minimization with Provable Convergence Guarantees Based on the Luo-Tseng Error Bound Property
Man-Chung Yue, Zirui Zhou, Anthony Man-Cho So
We propose a new family of inexact sequential quadratic approximation (SQA) methods, which we call the inexact regularized proximal Newton () method, for minimizing…
A Unified Approach to Error Bounds for Structured Convex Optimization Problems
Zirui Zhou, Anthony Man-Cho So
Error bounds, which refer to inequalities that bound the distance of vectors in a test set to a given set by a residual function, have proven to be extremely useful in analyzing th…
Non-Asymptotic Convergence Analysis of Inexact Gradient Methods for Machine Learning Without Strong Convexity
Anthony Man-Cho So
Many recent applications in machine learning and data fitting call for the algorithmic solution of structured smooth convex optimization problems. Although the gradient descent met…