Arnab Auddy

According to our database, Arnab Auddy authored at least 12 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms.

Arnab Auddy, Ming Yuan

CoRR, September, 2025

Newfluence: Boosting Model interpretability and Understanding in High Dimensions.

Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki

CoRR, July, 2025

Certified Data Removal Under High-dimensional Settings.

Haolin Zou, Arnab Auddy, Yongchan Kwon, Kamiar Rahnama Rad, Arian Maleki

CoRR, May, 2025

Transfer learning via Regularized Linear Discriminant Analysis.

Hongzhe Zhang, Arnab Auddy, Hongzhe Lee

CoRR, January, 2025

Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings.

Haolin Zou, Arnab Auddy, Kamiar Rahnama Rad, Arian Maleki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Approximate Leave-One-Out Cross Validation for Regression With ℓ₁ Regularizers.

Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki

IEEE Trans. Inf. Theory, November, 2024

Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges.

Arnab Auddy, Dong Xia, Ming Yuan

CoRR, 2024

Approximate Leave-one-out Cross Validation for Regression with ℓ₁ Regularizers.

Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2022

On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors.

Arnab Auddy, Ming Yuan

IEEE Trans. Inf. Theory, 2022

2021

On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors.

Arnab Auddy, Ming Yuan

CoRR, 2021

2020

Perturbation Bounds for Orthogonally Decomposable Tensors and Their Applications in High Dimensional Data Analysis.

Arnab Auddy, Ming Yuan

CoRR, 2020

2019

Approximate lumpability for Markovian agent-based models using local symmetries.

Wasiur R. KhudaBukhsh, Arnab Auddy, Yann Disser, Heinz Koeppl

J. Appl. Probab., 2019