Rajiv Khanna | DeepAI

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∙ 07/04/2023

Generalization Guarantees via Algorithm-dependent Rademacher Complexity

Algorithm- and data-dependent generalization bounds are required to expl...

0 Sarah Sachs, et al. ∙

∙ 03/24/2023

Feature Space Sketching for Logistic Regression

We present novel bounds for coreset construction, feature selection, and...

0 Gregory Dexter, et al. ∙

∙ 02/28/2022

Fast Feature Selection with Fairness Constraints

We study the fundamental problem of selecting optimal features for model...

0 Francesco Quinzan, et al. ∙

∙ 08/02/2021

Generalization Properties of Stochastic Optimizers via Trajectory Analysis

Despite the ubiquitous use of stochastic optimization algorithms in mach...

0 Liam Hodgkinson, et al. ∙

∙ 05/16/2021

LocalNewton: Reducing Communication Bottleneck for Distributed Learning

To address the communication bottleneck problem in distributed optimizat...

10 Vipul Gupta, et al. ∙

∙ 07/09/2020

Boundary thickness and robustness in learning models

Robustness of machine learning models to various adversarial and non-adv...

14 Yaoqing Yang, et al. ∙

∙ 07/01/2020

Bayesian Coresets: An Optimization Perspective

Bayesian coresets have emerged as a promising approach for implementing ...

0 Jacky Y. Zhang, et al. ∙

∙ 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...

0 Michał Dereziński, et al. ∙

∙ 10/29/2019

Learning Sparse Distributions using Iterative Hard Thresholding

Iterative hard thresholding (IHT) is a projected gradient descent algori...

0 Jacky Y. Zhang, et al. ∙

∙ 07/19/2019

On Linear Convergence of Weighted Kernel Herding

We provide a novel convergence analysis of two popular sampling algorith...

0 Rajiv Khanna, et al. ∙

∙ 10/23/2018

Interpreting Black Box Predictions using Fisher Kernels

Research in both machine learning and psychology suggests that salient e...

5 Rajiv Khanna, et al. ∙

∙ 06/06/2018

Boosting Black Box Variational Inference

Approximating a probability density in a tractable manner is a central t...

0 Francesco Locatello, et al. ∙

∙ 12/26/2017

IHT dies hard: Provable accelerated Iterative Hard Thresholding

We study --both in theory and practice-- the use of momentum motions in ...

0 Rajiv Khanna, et al. ∙

∙ 08/05/2017

Boosting Variational Inference: an Optimization Perspective

Variational Inference is a popular technique to approximate a possibly i...

0 Francesco Locatello, et al. ∙

∙ 03/08/2017

Scalable Greedy Feature Selection via Weak Submodularity

Greedy algorithms are widely used for problems in machine learning such ...

0 Rajiv Khanna, et al. ∙

∙ 03/08/2017

On Approximation Guarantees for Greedy Low Rank Optimization

We provide new approximation guarantees for greedy low rank matrix estim...

0 Rajiv Khanna, et al. ∙

∙ 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...

0 Francesco Locatello, et al. ∙

∙ 07/12/2016

Information Projection and Approximate Inference for Structured Sparse Variables

Approximate inference via information projection has been recently intro...

0 Rajiv Khanna, et al. ∙

∙ 02/12/2016

Pursuits in Structured Non-Convex Matrix Factorizations

Efficiently representing real world data in a succinct and parsimonious ...

0 Rajiv Khanna, et al. ∙

∙ 11/06/2015

Towards a Better Understanding of Predict and Count Models

In a recent paper, Levy and Goldberg pointed out an interesting connecti...

0 S Sathiya Keerthi, et al. ∙

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