Abhinav Chakraborty

Abhinav Chakraborty

PhD Student at Wharton, Department of Statistics and Data Science

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High Dimensional PCA: A New Model Selection Criterion

Published in arXiv preprint, 2020

This paper investigates model selection criteria in high-dimensional PCA, focusing on the Akaike Information Criterion (AIC) and its consistency under varying conditions of eigenvalue separation.

Recommended citation: Abhinav Chakraborty, Soumendu Sundar Mukherjee, Arijit Chakrabarti. (2020). "High Dimensional PCA: A New Model Selection Criterion." arXiv preprint arXiv:2011.04470.
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Optimal Differentially Private Ranking from Pairwise Comparisons

Published in Technical Report, 2023

This paper proposes optimal differentially private algorithms for ranking from noisy pairwise comparisons, establishing minimax rates for both parametric and nonparametric settings.

Recommended citation: T. Tony Cai, Abhinav Chakraborty, Yichen Wang. (2023). "Optimal Differentially Private Ranking from Pairwise Comparisons.".
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PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model

Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2024

This paper introduces a novel differentially private algorithm for peer effect estimation using the Ising model, with applications in healthcare and social sciences.

Recommended citation: Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal. (2024). "PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model." International Conference on Artificial Intelligence and Statistics, PMLR, 2692-2700.
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Reconciling model-X and doubly robust approaches to conditional independence testing

Published in The Annals of Statistics, 2024

This paper investigates the connections between model-X and doubly robust approaches to conditional independence testing, particularly through the dCRT and GCM tests.

Recommended citation: Ziang Niu, Abhinav Chakraborty, Oliver Dukes, Eugene Katsevich. (2024). "Reconciling model-X and doubly robust approaches to conditional independence testing." The Annals of Statistics, 52(3), 895-921.
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PriME: Privacy-aware Membership Profile Estimation in Networks

Published in arXiv preprint, 2024

This paper introduces a privacy-preserving algorithm for estimating community membership probabilities in networks, using a symmetric edge flip mechanism and spectral clustering under local differential privacy constraints.

Recommended citation: Abhinav Chakraborty, Sayak Chatterjee, Sagnik Nandy. (2024). "PriME: Privacy-aware Membership Profile Estimation in Networks." arXiv preprint arXiv:2406.02794.
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Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests

Published in arXiv preprint, 2024

This paper investigates federated nonparametric goodness-of-fit testing under distributed differential privacy constraints, establishing optimal rates and adaptive testing procedures.

Recommended citation: T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen. (2024). "Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests." arXiv preprint arXiv:2406.06749.
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Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints

Published in arXiv preprint, 2024

This paper explores federated learning for nonparametric regression under heterogeneous differential privacy constraints, establishing optimal rates of convergence for both global and pointwise estimation.

Recommended citation: T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen. (2024). "Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints." arXiv preprint arXiv:2406.06755.
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Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints

Published in arXiv preprint, 2024

This paper explores minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints, focusing on the posterior drift model.

Recommended citation: Arnab Auddy, T. Tony Cai, Abhinav Chakraborty. (2024). "Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints." arXiv preprint arXiv:2406.20088.
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Doubly Robust and Computationally Efficient High-Dimensional Variable Selection

Published in arXiv preprint, 2024

This paper introduces the tPCM method, a doubly robust and computationally efficient approach to high-dimensional variable selection, offering improvements over existing methods such as HRT and PCM.

Recommended citation: Abhinav Chakraborty, Jeffrey Zhang, Eugene Katsevich. (2024). "Doubly Robust and Computationally Efficient High-Dimensional Variable Selection." arXiv preprint arXiv:2409.09512.
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Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints

Published in arXiv preprint, 2024

This paper examines the statistical optimality of federated learning for functional mean estimation under heterogeneous privacy constraints, addressing both theoretical and practical aspects.

Recommended citation: Tony Cai, Abhinav Chakraborty, Lasse Vuursteen. (2024). "Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints." arXiv preprint arXiv:2412.18992v2.
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