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Armeen Taeb
Armeen Taeb
Verified email at uw.edu
Title
Cited by
Cited by
Year
Inverse problems and data assimilation
D Sanz-Alonso, AM Stuart, A Taeb
Cambridge University Press, 2023
72*2023
Visual stylometry using background selection and wavelet-HMT-based Fisher information distances for attribution and dating of impressionist paintings
H Qi, A Taeb, SM Hughes
Signal Processing 93 (3), 541-553, 2013
262013
Interpreting latent variables in factor models via convex optimization
A Taeb, V Chandrasekaran
Mathematical programming 167, 129-154, 2018
172018
A statistical graphical model of the California reservoir system
A Taeb, JT Reager, M Turmon, V Chandrasekaran
Water Resources Research 53 (11), 9721-9739, 2017
162017
A fast non-parametric approach for local causal structure learning
M Azadkia, A Taeb, P Bühlmann
arXiv preprint arXiv:2111.14969, 2022
11*2022
Provable concept learning for interpretable predictions using variational inference
A Taeb, N Ruggeri, C Schnuck, F Yang
ICML workshop on AI4Science, 2022
10*2022
Causality-oriented robustness: exploiting general additive interventions
X Shen, P Bühlmann, A Taeb
arXiv preprint arXiv:2307.10299, 2023
92023
A Look at Robustness and Stability of 1-versus 0-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al.
Y Chen, A Taeb, P Bühlmann
Statistical Science 35 (4), 614-622, 2020
92020
False discovery and its control in low rank estimation
A Taeb, P Shah, V Chandrasekaran
Journal of Royal Statistical Society, Series B, 2020
92020
Learning and scoring Gaussian latent causal models with unknown additive interventions
A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann
Journal of Machine Learning Research, 2024
8*2024
Maximin analysis of message passing algorithms for recovering block sparse signals
A Taeb, A Maleki, C Studer, R Baraniuk
Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2013
82013
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
JL Gamella, A Taeb, C Heinze-Deml, P Bühlmann
arXiv preprint arXiv:2211.14897, 2022
72022
Model Selection over Partially Ordered Sets
A Taeb, P Bühlmann, V Chandrasekaran
Proceedings of National Academy of Sciences, 2023
52023
Learning exponential family graphical models with latent variables using regularized conditional likelihood
A Taeb, P Shah, V Chandrasekaran
arXiv preprint arXiv:2010.09386, 2020
32020
A spectral method for multi-view subspace learning using the product of projections
R Sergazinov, A Taeb, I Gaynanova
arXiv preprint arXiv:2410.19125, 2024
22024
Extremal graphical modeling with latent variables via convex optimization
S Engelke, A Taeb
Journal of Machine Learning Research, 2024
2*2024
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
T Xu*, A Taeb*, S Küçükyavuz, A Shojaie
arXiv preprint arXiv:2404.12592, 2024
12024
An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models
T Xu, S Küçükyavuz, A Shojaie, A Taeb
arXiv preprint arXiv:2408.11977, 2024
2024
Latent-Variable Modeling: Algorithms, Inference, and Applications
A Taeb
California Institute of Technology, 2020
2020
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Articles 1–19