activity
20152025
most citedEfficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

12 citations · 19 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2025

Foundational theory for optimal decision tree problems. I. Algorithmic and geometric foundations

Xi He

In the first paper (part I) of this series of two, we introduce four novel definitions of the ODT problems: three for size-constrained trees and one for depth-constrained trees. Th…

cs.LG2025

Optimal hypersurface decision trees

Xi He

Optimal decision trees have attracted increasing attention in recent years, yet existing algorithms face two closely related challenges. First, the trees they construct have limite…

cs.DM2025

Functional design of efficient and parallelizable combinatorial generators using convolution

Xi He, Zhenjiang Hu, Max. A. Little

The application of program transformation and algebraic methods to the development of efficient combinatorial optimization (CO) algorithms relies on an exhaustive combinatorial gen…

cs.LG2025

Deep-ICE: the first globally optimal algorithm for minimizing 0-1 loss in two-layer ReLU and maxout networks

Xi He, Yi Miao, Max A. Little

This paper introduces the first globally optimal algorithm for the empirical risk minimization problem of two-layer maxout and ReLU networks, i.e., minimizing the number of misclas…

cs.LG2025

Proper decision trees: An axiomatic framework for solving optimal decision tree problems with arbitrary splitting rules

Xi He, Max A. Little

We present an axiomatic framework for analyzing the algorithmic properties of decision trees. This framework supports the classification of decision tree problems through structura…

cs.LG2024

EKM: An exact, polynomial-time algorithm for the -medoids problem

Xi He, Max A. Little

The -medoids problem is a challenging combinatorial clustering task, widely used in data analysis applications. While numerous algorithms have been proposed to solve this proble…