148 citations · 790 across the 56 of their papers we have counts for
20 papers · 1 filter
The Missing Indicator Method: From Low to High Dimensions
Mike Van Ness, Tomas M. Bosschieter, Roberto Halpin-Gregorio +1
Missing data is common in applied data science, particularly for tabular data sets found in healthcare, social sciences, and natural sciences. Most supervised learning methods only…
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams
Iddo Drori, Sarah J. Zhang, Reece Shuttleworth +13
A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large…
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
Chengrun Yang, Gabriel Bender, Hanxiao Liu +5
The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints in…
Data-Efficient and Interpretable Tabular Anomaly Detection
Chun-Hao Chang, Jinsung Yoon, Sercan Arik +2
Anomaly detection (AD) plays an important role in numerous applications. We focus on two understudied aspects of AD that are critical for integration into real-world applications.…
Towards Group Robustness in the presence of Partial Group Labels
Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon +3
Learning invariant representations is an important requirement when training machine learning models that are driven by spurious correlations in the datasets. These spurious correl…
ControlBurn: Feature Selection by Sparse Forests
Brian Liu, Miaolan Xie, Madeleine Udell
Tree ensembles distribute feature importance evenly amongst groups of correlated features. The average feature ranking of the correlated group is suppressed, which reduces interpre…
Privileged Zero-Shot AutoML
Nikhil Singh, Brandon Kates, Jeff Mentch +3
This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minute…
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang, Ziyang Wu, Jerry Chee +2
Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-o…
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…
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…
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…
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,…
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…
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…
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…
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…
Why are Big Data Matrices Approximately Low Rank?
Madeleine Udell, Alex Townsend
Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a…