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no code implementations • 24 Oct 2023 • Mike Van Ness, Tomas Bosschieter, Natasha Din, Andrew Ambrosy, Alexander Sandhu, Madeleine Udell

Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions.

1 code implementation • 9 Oct 2023 • Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell

Optimization problems are pervasive across various sectors, from manufacturing and distribution to healthcare.

no code implementations • 5 Sep 2023 • Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

This paper introduces PROMISE ($\textbf{Pr}$econditioned Stochastic $\textbf{O}$ptimization $\textbf{M}$ethods by $\textbf{I}$ncorporating $\textbf{S}$calable Curvature $\textbf{E}$stimates), a suite of sketching-based preconditioned stochastic gradient algorithms for solving large-scale convex optimization problems arising in machine learning.

no code implementations • 15 Jun 2023 • Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, Iddo Drori

We curate a comprehensive dataset of 4, 550 questions and solutions from problem sets, midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and Computer Science (EECS) courses required for obtaining a degree.

no code implementations • 16 Nov 2022 • Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

Numerical experiments on both ridge and logistic regression problems with dense and sparse data, show that SketchySGD equipped with its default hyperparameters can achieve comparable or better results than popular stochastic gradient methods, even when they have been tuned to yield their best performance.

1 code implementation • 16 Nov 2022 • Mike Van Ness, Tomas M. Bosschieter, Roberto Halpin-Gregorio, Madeleine Udell

In this paper, we show empirically and theoretically that MIM improves performance for informative missing values, and we prove that MIM does not hurt linear models asymptotically for uninformative missing values.

1 code implementation • 8 Jul 2022 • Brian Liu, Miaolan Xie, Haoyue Yang, Madeleine Udell

ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning.

no code implementations • 11 Jun 2022 • Iddo Drori, Sarah J. Zhang, Reece Shuttleworth, Sarah Zhang, Keith Tyser, Zad Chin, Pedro Lantigua, Saisamrit Surbehera, Gregory Hunter, Derek Austin, Leonard Tang, Yann Hicke, Sage Simhon, Sathwik Karnik, Darnell Granberry, Madeleine Udell

We curate a dataset and benchmark of questions from machine learning final exams available online and code for answering these questions and generating new questions.

1 code implementation • 15 Apr 2022 • Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc Le, Da Huang

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 including latency, compute, energy consumption, etc.

no code implementations • 3 Mar 2022 • Chun-Hao Chang, Jinsung Yoon, Sercan Arik, Madeleine Udell, Tomas Pfister

In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.

no code implementations • 10 Jan 2022 • Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister

Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.

no code implementations • 29 Sep 2021 • Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon, Madeleine Udell, Chen-Yu Lee, Tomas Pfister

Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.

no code implementations • NeurIPS Workshop ICBINB 2021 • Mike Van Ness, Madeleine Udell

Batch Normalizaiton (BN) is a normalization method for deep neural networks that has been shown to accelerate training.

no code implementations • NeurIPS 2021 • William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

In the present paper, we show that, in the case of ridge regression, the CV loss may fail to be quasiconvex and thus may have multiple local optima.

1 code implementation • 1 Jul 2021 • Brian Liu, Miaolan Xie, Madeleine Udell

Like the linear LASSO, ControlBurn assigns all the feature importance of a correlated group of features to a single feature.

no code implementations • 25 Jun 2021 • Nikhil Singh, Brandon Kates, Jeff Mentch, Anant Kharkar, Madeleine Udell, Iddo Drori

This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach.

1 code implementation • ICLR 2022 • Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell

Low-precision arithmetic trains deep learning models using less energy, less memory and less time.

no code implementations • 30 Apr 2021 • Yiming Sun, Yang Guo, Joel A. Tropp, Madeleine Udell

The TRP map is formed as the Khatri-Rao product of several smaller random projections, and is compatible with any base random projection including sparse maps, which enable dimension reduction with very low query cost and no floating point operations.

no code implementations • 1 Jan 2021 • Iddo Drori, Brandon Kates, Anant Kharkar, Lu Liu, Qiang Ma, Jonah Deykin, Nihar Sidhu, Madeleine Udell

We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset.

1 code implementation • 1 Jan 2021 • Chengrun Yang, Lijun Ding, Ziyang Wu, Madeleine Udell

Tensors are widely used to represent multiway arrays of data.

no code implementations • 7 Dec 2020 • Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang, Madeleine Udell

The theorems show that a relatively sharper regularizer leads to a tighter error bound, which is consistent with our numerical results.

no code implementations • 17 Nov 2020 • Brian Liu, Madeleine Udell

Model interpretations are often used in practice to extract real world insights from machine learning models.

no code implementations • NeurIPS Workshop LMCA 2020 • Iddo Drori, Brandon J Kates, William R. Sickinger, Anant Girish Kharkar, Brenda Dietrich, Avi Shporer, Madeleine Udell

We approximate a Traveling Salesman Problem (TSP) three orders of magnitude larger than the largest known benchmark, increasing the number of nodes from millions to billions.

no code implementations • 25 Sep 2020 • Yuxuan Zhao, Eric Landgrebe, Eliot Shekhtman, Madeleine Udell

Missing value imputation is crucial for real-world data science workflows.

no code implementations • 2 Sep 2020 • Elizabeth A. Ricci, Madeleine Udell, Ross A. Knepper

We combine a low rank model of a target attribute with an information-maximizing path planner to predict the state of the attribute throughout a region.

no code implementations • NeurIPS 2020 • William T. Stephenson, Madeleine Udell, Tamara Broderick

Our second key insight is that, in the presence of ALR data, error in existing ACV methods roughly grows with the (approximate, low) rank rather than with the (full, high) dimension.

no code implementations • 29 Jun 2020 • Lijun Ding, Jicong Fan, Madeleine Udell

This paper proposes a new variant of Frank-Wolfe (FW), called $k$FW.

2 code implementations • NeurIPS 2020 • Yuxuan Zhao, Madeleine Udell

The time required to fit the model scales linearly with the number of rows and the number of columns in the dataset.

1 code implementation • 7 Jun 2020 • Chengrun Yang, Jicong Fan, Ziyang Wu, Madeleine Udell

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, select an estimation algorithm, and choose hyperparameters for each of these pipeline components.

no code implementations • 6 Jun 2020 • Iddo Drori, Anant Kharkar, William R. Sickinger, Brandon Kates, Qiang Ma, Suwen Ge, Eden Dolev, Brenda Dietrich, David P. Williamson, Madeleine Udell

Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure.

no code implementations • 4 May 2020 • Jicong Fan, Chengrun Yang, Madeleine Udell

RNLMF constructs a dictionary for the data space by factoring a kernelized feature space; a noisy matrix can then be decomposed as the sum of a sparse noise matrix and a clean data matrix that lies in a low dimensional nonlinear manifold.

no code implementations • 25 Feb 2020 • Lijun Ding, Madeleine Udell

It is more challenging to show that an approximate solution to the SDP formulated with noisy problem data acceptably solves the original problem; arguments are usually ad hoc for each problem setting, and can be complex.

no code implementations • CVPR 2019 • Jicong Fan, Madeleine Udell

Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure.

no code implementations • 15 Dec 2019 • 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 dimension.

no code implementations • NeurIPS 2019 • Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell

Compared to the max norm and the factored formulation of the nuclear norm, factor group-sparse regularizers are more efficient, accurate, and robust to the initial guess of rank.

2 code implementations • 8 Oct 2019 • Iddo Drori, Lu Liu, Yi Nian, Sharath C. Koorathota, Jie S. Li, Antonio Khalil Moretti, Juliana Freire, Madeleine Udell

We use these embeddings in a neural architecture to learn the distance between best-performing pipelines.

no code implementations • 29 Apr 2019 • Yu-jia Zhang, Kuangyan Song, Yiming Sun, Sarah Tan, Madeleine Udell

Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms.

2 code implementations • 24 Apr 2019 • Yiming Sun, Yang Guo, Charlene Luo, Joel Tropp, Madeleine Udell

This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor.

no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

Machine learning (ML) techniques are enjoying rapidly increasing adoption.

no code implementations • 9 Feb 2019 • Lijun Ding, Alp Yurtsever, Volkan Cevher, Joel A. Tropp, Madeleine Udell

This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form.

1 code implementation • 27 Nov 2018 • Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell

We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership.

no code implementations • 15 Aug 2018 • Lijun Ding, Madeleine Udell

We introduce a few variants on Frank-Wolfe style algorithms suitable for large scale optimization.

1 code implementation • 9 Aug 2018 • Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine Udell

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning.

1 code implementation • NeurIPS 2018 • Nathan Kallus, Xiaojie Mao, Madeleine Udell

Valid causal inference in observational studies often requires controlling for confounders.

no code implementations • NeurIPS 2017 • Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher

Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates.

no code implementations • 21 May 2017 • Madeleine Udell, Alex Townsend

Here, we explain the effectiveness of low rank models in data science by considering a simple generative model for these matrices: we suppose that each row or column is associated to a (possibly high dimensional) bounded latent variable, and entries of the matrix are generated by applying a piecewise analytic function to these latent variables.

1 code implementation • 22 Feb 2017 • Alp Yurtsever, Madeleine Udell, Joel A. Tropp, Volkan Cevher

This paper concerns a fundamental class of convex matrix optimization problems.

no code implementations • NeurIPS 2016 • Damek Davis, Brent Edmunds, Madeleine Udell

We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems.

no code implementations • 18 Oct 2016 • Nathan Kallus, Madeleine Udell

In the dynamic setting, we show that structure-aware dynamic assortment personalization can have regret that is an order of magnitude smaller than structure-ignorant approaches.

3 code implementations • 12 Sep 2016 • Xinyue Shen, Steven Diamond, Madeleine Udell, Yuantao Gu, Stephen Boyd

A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed.

Optimization and Control

no code implementations • 31 Aug 2016 • Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher

This paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch.

no code implementations • 17 Sep 2015 • Nathan Kallus, Madeleine Udell

In our model, the preferences of each customer or segment follow a separate parametric choice model, but the underlying structure of these parameters over all the models has low dimension.

1 code implementation • 17 Oct 2014 • Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, Stephen Boyd

This paper describes Convex, a convex optimization modeling framework in Julia.

1 code implementation • 1 Oct 2014 • Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen Boyd

Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.

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