2 citations · 2 across the 1 of their papers we have counts for
4 papers
Self-Supervised Contextual Bandits in Computer Vision
Aniket Anand Deshmukh, Abhimanu Kumar, Levi Boyles +3
Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations. There have been a lot of app…
Data Transformation Insights in Self-supervision with Clustering Tasks
Abhimanu Kumar, Aniket Anand Deshmukh, Urun Dogan +2
Self-supervision is key to extending use of deep learning for label scarce domains. For most of self-supervised approaches data transformations play an important role. However, up…
A Unified Batch Online Learning Framework for Click Prediction
Rishabh Iyer, Nimit Acharya, Tanuja Bompada +2
We present a unified framework for Batch Online Learning (OL) for Click Prediction in Search Advertisement. Machine Learning models once deployed, show non-trivial accuracy and cal…
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
John Moore, Joel Pfeiffer, Kai Wei +5
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data…