3 citations · 5 across the 18 of their papers we have counts for
18 papers
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
Jae Ho Chang, Arnab Auddy, Subhadeep Paul
We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB). Leveraging the asymptotic normality of local p…
Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering
Arkajyoti Bhattacharjee, Arnab Auddy
Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexpl…
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Arnab Auddy, Xiangni Peng, Subhadeep Paul
Federated Learning is a leading framework for training ML and AI models collaboratively across numerous user devices or databases. We study the trade-offs among estimation accuracy…
Gaussian Certified Unlearning in High Dimensions: A Hypothesis Testing Approach
Aaradhya Pandey, Arnab Auddy, Haolin Zou +2
Machine unlearning seeks to efficiently remove the influence of selected data while preserving generalization. Significant progress has been made in low dimensions , but…
On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms
Arnab Auddy, Ming Yuan
We study spectral learning for orthogonally decomposable (odeco) tensors, emphasizing the interplay between statistical limits, optimization geometry, and initialization. Unlike ma…
Newfluence: Boosting Model interpretability and Understanding in High Dimensions
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
The increasing complexity of machine learning (ML) and artificial intelligence (AI) models has created a pressing need for tools that help scientists, engineers, and policymakers i…
Certified Data Removal Under High-dimensional Settings
Haolin Zou, Arnab Auddy, Yongchan Kwon +2
Machine unlearning focuses on the computationally efficient removal of specific training data from trained models, ensuring that the influence of forgotten data is effectively elim…
Transfer learning via Regularized Linear Discriminant Analysis
Hongzhe Zhang, Arnab Auddy, Hongzhe Lee
Linear discriminant analysis is a widely used method for classification. However, the high dimensionality of predictors combined with small sample sizes often results in large clas…
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…
Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges
Arnab Auddy, Dong Xia, Ming Yuan
Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics,…
Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad +1
Despite a large and significant body of recent work focused on estimating the out-of-sample risk of regularized models in the high dimensional regime, a theoretical understanding o…
Approximate Leave-one-out Cross Validation for Regression with Regularizers (extended version)
Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad +1
The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet co…
The Gromov-Wasserstein distance between spheres
Shreya Arya, Arnab Auddy, Ranthony Edmonds +3
In this paper we consider a two-parameter family {dGWp,q}p,q of Gromov- Wasserstein distances between metric measure spaces. By exploiting a suitable interaction between specific v…
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability
Arnab Auddy, Ming Yuan
In this paper, we investigate the optimal statistical performance and the impact of computational constraints for independent component analysis (ICA). Our goal is twofold. On the…
On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors
Arnab Auddy, Ming Yuan
In this paper, we study the estimation of a rank-one spiked tensor in the presence of heavy tailed noise. Our results highlight some of the fundamental similarities and differences…
Exact Detection Thresholds and Minimax Optimality of Chatterjee's Correlation Coefficient
Arnab Auddy, Nabarun Deb, Sagnik Nandy
Recently, Chatterjee (2021) introduced a new rank-based correlation coefficient which can be used to measure the strength of dependence between two random variables. This coefficie…
Perturbation Bounds for (Nearly) Orthogonally Decomposable Tensors
Arnab Auddy, Ming Yuan
We develop deterministic perturbation bounds for singular values and vectors of orthogonally decomposable tensors, in a spirit similar to classical results for matrices such as tho…
Approximate lumpability for Markovian agent-based models using local symmetries
Wasiur R. KhudaBukhsh, Arnab Auddy, Yann Disser +1
We study a Markovian agent-based model (MABM) in this paper. Each agent is endowed with a local state that changes over time as the agent interacts with its neighbours. The neighbo…