579 citations · 698 across the 20 of their papers we have counts for
20 papers
A New Perspective on Clustering: A Mixed-norm Model and its Solution by Progressive Integer Programming
Junyi Liu, Yulin Peng, Yao Xie +2
Extending the classical -means and -medians models, this paper introduces an mixed-norm clustering model where the centroid updates and cluster assignments are u…
DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems
Maojun Sun, Yifei Xie, Yue Wu +5
Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, whi…
A Survey on Large Language Model-based Agents for Statistics and Data Science
Maojun Sun, Ruijian Han, Binyan Jiang +4
In recent years, data science agents powered by Large Language Models (LLMs), known as "data agents," have shown significant potential to transform the traditional data analysis pa…
HOT: An Efficient Halpern Accelerating Algorithm for Optimal Transport Problems
Guojun Zhang, Zhexuan Gu, Yancheng Yuan +1
This paper proposes an efficient HOT algorithm for solving the optimal transport (OT) problems with finite supports. We particularly focus on an efficient implementation of the HOT…
LAMBDA: A Large Model Based Data Agent
Maojun Sun, Ruijian Han, Binyan Jiang +4
We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is des…
Accelerating preconditioned ADMM via degenerate proximal point mappings
Defeng Sun, Yancheng Yuan, Guojun Zhang +1
In this paper, we aim to accelerate a preconditioned alternating direction method of multipliers (pADMM), whose proximal terms are convex quadratic functions, for solving linearly…
An efficient sieving based secant method for sparse optimization problems with least-squares constraints
Qian Li, Defeng Sun, Yancheng Yuan
In this paper, we propose an efficient sieving based secant method to address the computational challenges of solving sparse optimization problems with least-squares constraints. A…
A Highly Efficient Algorithm for Solving Exclusive Lasso Problems
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses…
Adaptive sieving: A dimension reduction technique for sparse optimization problems
Yancheng Yuan, Meixia Lin, Defeng Sun +1
In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, whe…
Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees
Ziwen Wang, Yancheng Yuan, Jiaming Ma +2
In this paper, we propose a randomly projected convex clustering model for clustering a collection of high dimensional data points in with hidden clusters. C…
Invariant Collaborative Filtering to Popularity Distribution Shift
An Zhang, Jingnan Zheng, Xiang Wang +2
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and…
An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with Computational Complexity
Guojun Zhang, Yancheng Yuan, Defeng Sun
In this paper, we propose and analyze an efficient Halpern-Peaceman-Rachford (HPR) algorithm for solving the Wasserstein barycenter problem (WBP) with fixed supports. While the Pea…
Understanding Deep Learning via Decision Boundary
Shiye Lei, Fengxiang He, Yancheng Yuan +1
This paper discovers that the neural network with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and -data…
A Dimension Reduction Technique for Large-scale Structured Sparse Optimization Problems with Application to Convex Clustering
Yancheng Yuan, Tsung-Hui Chang, Defeng Sun +1
In this paper, we propose a novel adaptive sieving (AS) technique and an enhanced AS (EAS) technique, which are solver independent and could accelerate optimization algorithms for…
Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang, Tinglin Huang, Dingxian Wang +4
Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs).…
Adaptive Sieving with PPDNA: Generating Solution Paths of Exclusive Lasso Models
Meixia Lin, Yancheng Yuan, Defeng Sun +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on structured sparsity. Its complex nature poses diffic…
A dual Newton based preconditioned proximal point algorithm for exclusive lasso models
Meixia Lin, Defeng Sun, Kim-Chuan Toh +1
The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regulari…
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan
Clustering is a fundamental problem in unsupervised learning. Popular methods like K-means, may suffer from poor performance as they are prone to get stuck in its local minima. Rec…
An Efficient Semismooth Newton Based Algorithm for Convex Clustering
Yancheng Yuan, Defeng Sun, Kim-Chuan Toh
Clustering may be the most fundamental problem in unsupervised learning which is still active in machine learning research because its importance in many applications. Popular meth…
SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan +1
SDPNAL+ is a {\sc Matlab} software package that implements an augmented Lagrangian based method to solve large scale semidefinite programming problems with bound constraints. The i…