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07/04/2023
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Algorithm- and data-dependent generalization bounds are required to expl...
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03/24/2023
Feature Space Sketching for Logistic Regression
We present novel bounds for coreset construction, feature selection, and...
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02/28/2022
Fast Feature Selection with Fairness Constraints
We study the fundamental problem of selecting optimal features for model...
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08/02/2021
Generalization Properties of Stochastic Optimizers via Trajectory Analysis
Despite the ubiquitous use of stochastic optimization algorithms in mach...
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05/16/2021
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To address the communication bottleneck problem in distributed optimizat...
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07/09/2020
Boundary thickness and robustness in learning models
Robustness of machine learning models to various adversarial and non-adv...
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07/01/2020
Bayesian Coresets: An Optimization Perspective
Bayesian coresets have emerged as a promising approach for implementing ...
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02/21/2020
Improved guarantees and a multiple-descent curve for the Column Subset Selection Problem and the Nyström method
The Column Subset Selection Problem (CSSP) and the Nyström method are am...
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10/29/2019
Learning Sparse Distributions using Iterative Hard Thresholding
Iterative hard thresholding (IHT) is a projected gradient descent algori...
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07/19/2019
On Linear Convergence of Weighted Kernel Herding
We provide a novel convergence analysis of two popular sampling algorith...
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10/23/2018
Interpreting Black Box Predictions using Fisher Kernels
Research in both machine learning and psychology suggests that salient e...
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06/06/2018
Boosting Black Box Variational Inference
Approximating a probability density in a tractable manner is a central t...
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12/26/2017
IHT dies hard: Provable accelerated Iterative Hard Thresholding
We study --both in theory and practice-- the use of momentum motions in ...
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08/05/2017
Boosting Variational Inference: an Optimization Perspective
Variational Inference is a popular technique to approximate a possibly i...
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03/08/2017
Scalable Greedy Feature Selection via Weak Submodularity
Greedy algorithms are widely used for problems in machine learning such ...
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03/08/2017
On Approximation Guarantees for Greedy Low Rank Optimization
We provide new approximation guarantees for greedy low rank matrix estim...
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02/21/2017
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
Two of the most fundamental prototypes of greedy optimization are the ma...
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07/12/2016
Information Projection and Approximate Inference for Structured Sparse Variables
Approximate inference via information projection has been recently intro...
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02/12/2016
Pursuits in Structured Non-Convex Matrix Factorizations
Efficiently representing real world data in a succinct and parsimonious ...
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11/06/2015
