2 citations · 4 across the 13 of their papers we have counts for
13 papers
Optimal Inference with Black-box Predictions
Lucas Kania, Abhinav Chakraborty, Edward Kennedy +2
Powerful black-box predictive models have motivated many proposals for combining observed data with predictions to perform valid statistical inference. Despite this progress, the f…
Transfer Learning in High-Dimensional Clustering: Minimax Thresholds and Applications in Single-Cell Data
Abhinav Chakraborty, Sagnik Nandy
Clustering is a fundamental problem in statistics, with applications across many scientific disciplines. In many modern applications involving clustering, the primary dataset (the…
The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Abhinav Chakraborty, Subha Maity
Multi-source transfer learning can improve target-domain estimation by leveraging related source data, but its benefits depend on unknown source-to-target biases. This raises a fun…
Stability and Accuracy Trade-offs in Statistical Estimation
Abhinav Chakraborty, Yuetian Luo, Rina Foygel Barber
Algorithmic stability is a central concept in statistics and learning theory that measures how sensitive an algorithm's output is to small changes in the training data. Stability p…
The Cost of Adaptation under Differential Privacy: Optimal Adaptive Federated Density Estimation
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Privacy-preserving data analysis has become a central challenge in modern statistics. At the same time, a long-standing goal in statistics is the development of adaptive procedures…
Asymptotic Normality of Subgraph Counts in Sparse Inhomogeneous Random Graphs
Sayak Chatterjee, Anirban Chatterjee, Abhinav Chakraborty +1
In this paper, we derive the asymptotic distribution of the number of copies of a fixed graph in a random graph sampled from a sparse graphon model. Specifically, we prov…
Optimal Differentially Private Ranking from Pairwise Comparisons
T. Tony Cai, Abhinav Chakraborty, Yichen Wang
Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, an…
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints
Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range…
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
Arnab Auddy, T. Tony Cai, Abhinav Chakraborty
This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our…
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy co…
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locatio…
PriME: Privacy-aware Membership profile Estimation in networks
Abhinav Chakraborty, Sayak Chatterjee, Sagnik Nandy
This paper presents a novel approach to estimating community membership probabilities for network vertices generated by the Degree Corrected Mixed Membership Stochastic Block Model…
PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model
Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal
The Ising model, originally developed as a spin-glass model for ferromagnetic elements, has gained popularity as a network-based model for capturing dependencies in agents' outputs…