148 citations · 790 across the 56 of their papers we have counts for
9 papers · 1 filter
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
Jicong Fan, Yuqian Zhang, Madeleine Udell
This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimen…
Scalable Semidefinite Programming
Alp Yurtsever, Joel A. Tropp, Olivier Fercoq +2
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct ra…
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery
Jicong Fan, Lijun Ding, Yudong Chen +1
This paper develops a new class of nonconvex regularizers for low-rank matrix recovery. Many regularizers are motivated as convex relaxations of the matrix rank function. Our new f…
AutoML using Metadata Language Embeddings
Iddo Drori, Lu Liu, Yi Nian +5
As a human choosing a supervised learning algorithm, it is natural to begin by reading a text description of the dataset and documentation for the algorithms you might use. We demo…
Missing Value Imputation for Mixed Data via Gaussian Copula
Yuxuan Zhao, Madeleine Udell
Missing data imputation forms the first critical step of many data analysis pipelines. The challenge is greatest for mixed data sets, including real, Boolean, and ordinal data, whe…
Low-Rank Tucker Approximation of a Tensor From Streaming Data
Yiming Sun, Yang Guo, Charlene Luo +2
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor. The method applies a randomized linear map to the tensor to obtain a sketch that cap…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collect…
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
Lijun Ding, Alp Yurtsever, Volkan Cevher +2
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly con…