3 citations · 5 across the 5 of their papers we have counts for
8 papers
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama
We investigate a natural but surprisingly unstudied approach to the multi-armed bandit problem under safety risk constraints. Each arm is associated with an unknown law on safety r…
Online Selective Classification with Limited Feedback
Aditya Gangrade, Anil Kag, Ashok Cutkosky +1
Motivated by applications to resource-limited and safety-critical domains, we study selective classification in the online learning model, wherein a predictor may abstain from clas…
Limits on Testing Structural Changes in Ising Models
Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
We present novel information-theoretic limits on detecting sparse changes in Ising models, a problem that arises in many applications where network changes can occur due to some ex…
Selective Classification via One-Sided Prediction
Aditya Gangrade, Anil Kag, Venkatesh Saligrama
We propose a novel method for selective classification (SC), a problem which allows a classifier to abstain from predicting some instances, thus trading off accuracy against covera…
Piecewise Linear Regression via a Difference of Convex Functions
Ali Siahkamari, Aditya Gangrade, Brian Kulis +1
We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. These are functions that may be repr…
Budget Learning via Bracketing
Aditya Gangrade, Durmus Alp Emre Acar, Venkatesh Saligrama
Conventional machine learning applications in the mobile/IoT setting transmit data to a cloud-server for predictions. Due to cost considerations (power, latency, monetary), it is d…