activity
20182021
most citedMixture Proportion Estimation and PU Learning: A Modern Approach

5 citations · 9 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG20215 cited

Mixture Proportion Estimation and PU Learning: A Modern Approach

Saurabh Garg, Yifan Wu, Alex Smola +2

Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifie…

cs.DC2021

SDP: Scalable Real-time Dynamic Graph Partitioner

Md Anwarul Kaium Patwary, Saurabh Garg, Sudheer Kumar Battula +1

Time-evolving large graph has received attention due to their participation in real-world applications such as social networks and PageRank calculation. It is necessary to partitio…

cs.CR20214 cited

A Taxonomy Study on Securing Blockchain-based Industrial Applications: An Overview, Application Perspectives, Requirements, Attacks, Countermeasures, and Open Issues

Khizar Hameed, Mutaz Barika, Saurabh Garg +2

Blockchain technology has taken on a leading role in today's industrial applications by providing salient features and showing significant performance since its beginning. Blockcha…

cs.LG2021

RATT: Leveraging Unlabeled Data to Guarantee Generalization

Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter +1

To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on th…

cs.LG2020

A Unified View of Label Shift Estimation

Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan +1

Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label margin…

cs.CL2018

Code-switched Language Models Using Dual RNNs and Same-Source Pretraining

Saurabh Garg, Tanmay Parekh, Preethi Jyothi

This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit…