Approximate lumpability for Markovian agent-based models using local symmetries. - Wasiur R. KhudaBukhsh; Arnab Auddy; Yann Disser; Heinz Koeppl (J. Appl. Probab., 2019)

Perturbation Bounds for Orthogonally Decomposable Tensors and Their Applications in High Dimensional Data Analysis. - Arnab Auddy; Ming Yuan (CoRR, 2020)

On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors. - Arnab Auddy; Ming Yuan (CoRR, 2021)

On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors. - Arnab Auddy; Ming Yuan 001 (IEEE Trans. Inf. Theory, 2022)

Approximate Leave-one-out Cross Validation for Regression with ℓ1 Regularizers. - Arnab Auddy; Haolin Zou; Kamiar Rahnama Rad; Arian Maleki (AISTATS, 2024)

Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges. - Arnab Auddy; Dong Xia; Ming Yuan 001 (CoRR, 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, 2024)

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 (AISTATS, 2025)

Transfer learning via Regularized Linear Discriminant Analysis. - Hongzhe Zhang; Arnab Auddy; Hongzhe Lee (CoRR, 2025)

Certified Data Removal Under High-dimensional Settings. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)

Newfluence: Boosting Model interpretability and Understanding in High Dimensions. - Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki (CoRR, 2025)

On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms. - Arnab Auddy; Ming Yuan (CoRR, 2025)