12 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…
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…
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…