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09/03/2023
Privacy-Utility Tradeoff of OLS with Random Projections
We study the differential privacy (DP) of a core ML problem, linear ordi...
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05/12/2023
Reduced Label Complexity For Tight ℓ_2 Regression
Given data X∈ℝ^n× d and labels 𝐲∈ℝ^n the goal is find 𝐰∈ℝ^d to minimize ...
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08/25/2021
Learning GraphQL Query Costs (Extended Version)
GraphQL is a query language for APIs and a runtime for executing those q...
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04/16/2021
NoisyCUR: An algorithm for two-cost budgeted matrix completion
Matrix completion is a ubiquitous tool in machine learning and data anal...
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09/01/2020
Training Deep Neural Networks with Constrained Learning Parameters
Today's deep learning models are primarily trained on CPUs and GPUs. Alt...
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08/24/2020
A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions
We present a new mathematical model to explicitly capture the effects th...
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04/21/2020
Inferring Degrees from Incomplete Networks and Nonlinear Dynamics
Inferring topological characteristics of complex networks from observed ...
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03/17/2020
Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics
We present a data-driven machine learning analysis of COVID-19 from its ...
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01/18/2020
True Nonlinear Dynamics from Incomplete Networks
We study nonlinear dynamics on complex networks. Each vertex i has a sta...
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09/27/2019
Fast Fixed Dimension L2-Subspace Embeddings of Arbitrary Accuracy, With Application to L1 and L2 Tasks
We give a fast oblivious L2-embedding of A∈R^n x d to B∈R^r x d satisfyi...
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02/21/2019
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines
Data science relies on pipelines that are organized in the form of inter...
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01/23/2019
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
Privacy is a major issue in learning from distributed data. Recently the...
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07/17/2018
A Mathematical Model for Optimal Decisions in a Representative Democracy
Direct democracy is a special case of an ensemble of classifiers, where ...
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05/06/2018
Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines, and K-Nearest Neighbor Classifiers
Neural networks in many varieties are touted as very powerful machine le...
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04/17/2018
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
We propose a novel subgraph image representation for classification of n...
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03/12/2015
Approximating Sparse PCA from Incomplete Data
We study how well one can recover sparse principal components of a data ...
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03/02/2015
Recovering PCA from Hybrid-(ℓ_1,ℓ_2) Sparse Sampling of Data Elements
This paper addresses how well we can recover a data matrix when only giv...
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02/23/2015
Optimal Sparse Linear Auto-Encoders and Sparse PCA
Principal components analysis (PCA) is the optimal linear auto-encoder o...
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02/19/2015
NP-Hardness and Inapproximability of Sparse PCA
We give a reduction from clique to establish that sparse PCA is NP-hard...
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11/26/2012
Random Projections for Linear Support Vector Machines
Let X be a data matrix of rank ρ, whose rows represent n points in d-dim...
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07/19/2012
The Fast Cauchy Transform and Faster Robust Linear Regression
We provide fast algorithms for overconstrained ℓ_p regression and relate...
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02/14/2012
Near-Optimal Target Learning With Stochastic Binary Signals
We study learning in a noisy bisection model: specifically, Bayesian alg...
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09/08/2010