Aditya Gangrade

34 papers A* 13A 2Misc 1Journal 17Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Constrained Linear Thompson Sampling. CoRR Aditya Gangrade, Venkatesh Saligrama
2025	J	`jnl`	Domain Generalization: A Tale of Two ERMs. CoRR Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott
2025	A*	`conf`	Feasible Action Search for Bandit Linear Programs via Thompson Sampling. ICML Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama
2025	J	`jnl`	Linear Transformers Implicitly Discover Unified Numerical Algorithms. CoRR Patrick Lutz, Aditya Gangrade, Hadi Daneshmand, Venkatesh Saligrama
2025	A*	`conf`	SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models. CVPR Kevin Miller, Aditya Gangrade, Samarth Mishra, Kate Saenko, Venkatesh Saligrama
2025	J	`jnl`	SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models. CoRR Kevin Miller, Samarth Mishra, Aditya Gangrade, Kate Saenko, Venkatesh Saligrama
2025	J	`jnl`	Universal Inference Meets Random Projections: A Scalable Test for Log-Concavity. J. Comput. Graph. Stat. Robin Dunn, Aditya Gangrade, Larry A. Wasserman, Aaditya Ramdas
2024	A*	`conf`	Label Noise: Ignorance Is Bliss. NeurIPS Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott
2024	J	`jnl`	Label Noise: Ignorance Is Bliss. CoRR Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott
2024	A*	`conf`	Safe Linear Bandits over Unknown Polytopes. COLT Aditya Gangrade, Tianrui Chen, Venkatesh Saligrama
2024	A*	`conf`	Testing the Feasibility of Linear Programs with Bandit Feedback. ICML Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
2024	J	`jnl`	Testing the Feasibility of Linear Programs with Bandit Feedback. CoRR Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
2023	A*	`conf`	Counterfactually Comparing Abstaining Classifiers. NeurIPS Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
2023	J	`jnl`	Counterfactually Comparing Abstaining Classifiers. CoRR Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas
2023	A*	`conf`	Efficient Edge Inference by Selective Query. ICLR Anil Kag, Igor Fedorov, Aditya Gangrade, Paul N. Whatmough, Venkatesh Saligrama
2023	A*	`conf`	Scaffolding a Student to Instill Knowledge. ICLR Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama
2022	J	`jnl`	A Doubly Optimistic Strategy for Safe Linear Bandits. CoRR Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama
2022	A*	`conf`	Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk. ICML Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama
2022	J	`jnl`	Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk. CoRR Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama
2021	A*	`conf`	Online Selective Classification with Limited Feedback. NeurIPS Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama
2021	J	`jnl`	Online Selective Classification with Limited Feedback. CoRR Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama
2021	A	`conf`	Selective Classification via One-Sided Prediction. AISTATS Aditya Gangrade, Anil Kag, Venkatesh Saligrama
2020	A	`conf`	Budget Learning via Bracketing. AISTATS Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama
2020	J	`jnl`	Budget Learning via Bracketing. CoRR Aditya Gangrade, Durmus Alp Emre Acar, Venkatesh Saligrama
2020	A*	`conf`	Limits on Testing Structural Changes in Ising Models. NeurIPS Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
2020	J	`jnl`	Limits on Testing Structural Changes in Ising Models. CoRR Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
2020	A*	`conf`	Piecewise Linear Regression via a Difference of Convex Functions. ICML Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama
2020	J	`jnl`	Piecewise Linear Regression via a Difference of Convex Functions. CoRR Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama
2020	J	`jnl`	Selective Classification via One-Sided Prediction. CoRR Aditya Gangrade, Anil Kag, Venkatesh Saligrama
2019	A*	`conf`	Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models. NeurIPS Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama
2018	J	`jnl`	Testing Changes in Communities for the Stochastic Block Model. CoRR Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama
2018	Misc	`conf`	Two-Sample Testing can be as Hard as Structure Learning in Ising Models: Minimax Lower Bounds. ICASSP Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
2017	J	`jnl`	Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models. CoRR Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama
2017	—	`conf`	Lower bounds for two-sample structural change detection in ising and Gaussian models. Allerton Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama