148 citations · 790 across the 56 of their papers we have counts for
13 papers · 1 filter
Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis
Jicong Fan, Lijun Ding, Chengrun Yang +2
The nuclear norm and Schatten- quasi-norm are popular rank proxies in low-rank matrix recovery. However, computing the nuclear norm or Schatten- quasi-norm of a tensor is har…
A Strict Complementarity Approach to Error Bound and Sensitivity of Solution of Conic Programs
Lijun Ding, Madeleine Udell
In this paper, we provide an elementary, geometric, and unified framework to analyze conic programs that we call the strict complementarity approach. This framework allows us to es…
Impact of Accuracy on Model Interpretations
Brian Liu, Madeleine Udell
Model interpretations are often used in practice to extract real world insights from machine learning models. These interpretations have a wide range of applications; they can be p…
An Information-Theoretic Approach to Persistent Environment Monitoring Through Low Rank Model Based Planning and Prediction
Elizabeth A. Ricci, Madeleine Udell, Ross A. Knepper
Robots can be used to collect environmental data in regions that are difficult for humans to traverse. However, limitations remain in the size of region that a robot can directly o…
Online Missing Value Imputation and Change Point Detection with the Gaussian Copula
Yuxuan Zhao, Eric Landgrebe, Eliot Shekhtman +1
Missing value imputation is crucial for real-world data science workflows. Imputation is harder in the online setting, as it requires the imputation method itself to be able to evo…
Approximate Cross-Validation with Low-Rank Data in High Dimensions
William T. Stephenson, Madeleine Udell, Tamara Broderick
Many recent advances in machine learning are driven by a challenging trifecta: large data size ; high dimensions; and expensive algorithms. In this setting, cross-validation (CV…
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
Iddo Drori, Anant Kharkar, William R. Sickinger +7
Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure. In this work…
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Chengrun Yang, Jicong Fan, Ziyang Wu +1
Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension,…
FW: A Frank-Wolfe style algorithm with stronger subproblem oracles
Lijun Ding, Jicong Fan, Madeleine Udell
This paper proposes a new variant of Frank-Wolfe (FW), called FW. Standard FW suffers from slow convergence: iterates often zig-zag as update directions oscillate around extreme…
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
Yuxuan Zhao, Madeleine Udell
Modern large scale datasets are often plagued with missing entries. For tabular data with missing values, a flurry of imputation algorithms solve for a complete matrix which minimi…
Robust Non-Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering
Jicong Fan, Chengrun Yang, Madeleine Udell
Low dimensional nonlinear structure abounds in datasets across computer vision and machine learning. Kernelized matrix factorization techniques have recently been proposed to learn…
Online high rank matrix completion
Jicong Fan, Madeleine Udell
Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new model…
On the simplicity and conditioning of low rank semidefinite programs
Lijun Ding, Madeleine Udell
Low rank matrix recovery problems appear widely in statistics, combinatorics, and imaging. One celebrated method for solving these problems is to formulate and solve a semidefinite…