6 citations · 19 across the 11 of their papers we have counts for
12 papers
ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
Xiang Meng, Kayhan Behdin, Haoyue Wang +1
The impressive performance of Large Language Models (LLMs) across various natural language processing tasks comes at the cost of vast computational resources and storage requiremen…
On the Convergence of CART under Sufficient Impurity Decrease Condition
Rahul Mazumder, Haoyue Wang
The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART. In this pape…
Linear programming using diagonal linear networks
Haoyue Wang, Promit Ghosal, Rahul Mazumder
Linear programming has played a crucial role in shaping decision-making, resource allocation, and cost reduction in various domains. In this paper, we investigate the application o…
A Cyclic Coordinate Descent Method for Convex Optimization on Polytopes
Rahul Mazumder, Haoyue Wang
Coordinate descent algorithms are popular for huge-scale optimization problems due to their low cost per-iteration. Coordinate descent methods apply to problems where the constrain…
A Light-speed Linear Program Solver for Personalized Recommendation with Diversity Constraints
Haoyue Wang, Miao Cheng, Kinjal Basu +3
We study a structured linear program (LP) that emerges in the need of ranking candidates or items in personalized recommender systems. Since the candidate set is only known in real…
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder, Xiang Meng, Haoyue Wang
Decision trees are one of the most useful and popular methods in the machine learning toolbox. In this paper, we consider the problem of learning optimal decision trees, a combinat…
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach
Haoyue Wang, Shibal Ibrahim, Rahul Mazumder
We explore computational aspects of maximum likelihood estimation of the mixture proportions of a nonparametric finite mixture model -- a convex optimization problem with old roots…
Linear regression with partially mismatched data: local search with theoretical guarantees
Rahul Mazumder, Haoyue Wang
Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response…
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
Haoyue Wang, Haihao Lu, Rahul Mazumder
The Frank-Wolfe (FW) method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank…
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Dongdong Ge, Haoyue Wang, Zikai Xiong +1
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, suc…
Solving L1-regularized SVMs and related linear programs: Revisiting the effectiveness of Column and Constraint Generation
Antoine Dedieu, Rahul Mazumder, Haoyue Wang
The linear Support Vector Machine (SVM) is a classic classification technique in machine learning. Motivated by applications in modern high dimensional statistics, we consider pena…
An Optimal High-Order Tensor Method for Convex Optimization
Bo Jiang, Haoyue Wang, Shuzhong Zhang
This paper is concerned with finding an optimal algorithm for minimizing a composite convex objective function. The basic setting is that the objective is the sum of two convex fun…