Preprints

• "Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models "
       T. Xu^*, A. Taeb^*, S. Küçükyavuz, A. Shojaie [software]
       2024.
• "Extremal Graphical Modeling with Latent Variables"
       S. Engelke, A. Taeb [software]
       2024.
• "Causality-oriented Robustness: Exploiting General Additive Interventions"
       X. Shen, P. Bühlmann, A. Taeb [software][poster]
       2023.
• "Learning and scoring Gaussian latent causal models with unknown additive interventions"
       A. Taeb, J. Gamella, C. Heinze-Deml, P. Bühlmann, [software] [video]
       2023.
• "Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions "
       J. Gamella, A. Taeb, C. Heinze-Deml, P. Bühlmann, [software]
       2022.

    Journal

• "Model Selection over Partially Ordered Sets"
       A. Taeb, P. Bühlmann, V. Chandrasekaran [software][poster]
       Proceedings of National Academy of Sciences, 2024.
• "A Look at Robusness and Stability of L1 versus L0 regularization : Discussion of Papers by Bertsimas et al. and Hastie et al."
       Y. Chen, A. Taeb, P. Bühlmann, [software]
       Statistical Science, 2020.
• "False Discovery and Its Control in Low-rank Estimation"
       A. Taeb, P. Shah, V. Chandrasekaran, [software] [video].
       Journal of the Royal Statistical Society (Series B), 2020.
• "Interpreting Latent Variables in Factor Models via Convex Optimization"
       A. Taeb, V. Chandrasekaran
       Mathematical Programming, 2018.

• "A Statistical Graphical Model of the California Reservoir Network"
       A. Taeb, J.T. Reager, M. Turmon, V. Chandrasekaran [software] [press release] [broad audience]
       Water Resources Research, 2017.
• "Visual Stylometry using background selection and wavelet-HMT-based Fisher Information distances"
       H. Qi, A. Taeb, and S. Hughes
       EURASIP Signal Processing, 2012

.

    PhD Thesis

• "Latent-variable Modeling: Algorithms, Inference, and Applications"
      A. Taeb, California Institute of Technology, 2019
      W. P. Carey & Co. Prize for oustanding thesis in Applied Mathematics

    Book

• "Inverse Problems and Data Assimilation"
      D. Sanz-Alonso, A. Stuart, and A. Taeb
      Cambridge University Press, London Mathematical Society Student Texts, 2023

    Conferences and Workshops

• "Provable Concept Learning for Interpretable Predictions using Variational Inference"
       A. Taeb, N. Ruggeri, C. Schnuck, F. Yang, [software][video][poster]
       ICML Workshop on AI4Science,2022.
• "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.

    Technical notes

• "A Fast Non-parametric Approach for Local Causal Structure Learning"
       M. Azadkia, A. Taeb, P. Bühlmann
       2022.
• "Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood"
       A. Taeb, P. Shah, V. Chandrasekaran
       2020.

• University of Washington
       Multiple Testing and Modern Inference (Fall 2023). Notes can be viewed here
       Statistical Machine Learning for Data Scientists (Spring 2023).
       Applied Statistics Capstone (Winter 2023).
       Statistical Learning: Modeling, Prediction, and Computing (Winter 2023).
  

• Instructor, ETH Zürich
      Topic: Seminar on Multiple Testing for Modern Data Science
      Co-teaching (with Matthias Löffler), 2020

• Summer School Lectures, University della Svizzera Italiana, Switzerland
      Topic: Data Assimilation and Inverse Problems
      Co-Instructor (with Andrew Stuart), 2018

• Graduate Teaching Assistant, Caltech
       Inverse Problems and Data Assimilation (Fall 2017).
       Mathematical Statistics (Spring 2016).
       Mathematical Optimization (Fall 2014, 2015).