Aditya Gangrade

According to our database, Aditya Gangrade authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Testing the Feasibility of Linear Programs with Bandit Feedback.

Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Safe Linear Bandits over Unknown Polytopes.

Aditya Gangrade, Tianrui Chen, Venkatesh Saligrama

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Counterfactually Comparing Abstaining Classifiers.

Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Edge Inference by Selective Query.

Anil Kag, Igor Fedorov, Aditya Gangrade, Paul N. Whatmough, Venkatesh Saligrama

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Scaffolding a Student to Instill Knowledge.

Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

A Doubly Optimistic Strategy for Safe Linear Bandits.

Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama

CoRR, 2022

Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk.

Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama

Proceedings of the International Conference on Machine Learning, 2022

2021

Online Selective Classification with Limited Feedback.

Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Selective Classification via One-Sided Prediction.

Aditya Gangrade, Anil Kag, Venkatesh Saligrama

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Limits on Testing Structural Changes in Ising Models.

Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Piecewise Linear Regression via a Difference of Convex Functions.

Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

Proceedings of the 37th International Conference on Machine Learning, 2020

Budget Learning via Bracketing.

Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models.

Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Testing Changes in Communities for the Stochastic Block Model.

Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

CoRR, 2018

Two-Sample Testing can be as Hard as Structure Learning in Ising Models: Minimax Lower Bounds.

Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

Lower bounds for two-sample structural change detection in ising and Gaussian models.

Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

Proceedings of the 55th Annual Allerton Conference on Communication, 2017