Improved Bounds on Restricted Isometry Constants for Gaussian Matrices. - Bubacarr Bah; Jared Tanner (SIAM J. Matrix Anal. Appl., 2010)

Bounds of restricted isometry constants in extreme asymptotics: formulae for Gaussian matrices - Bubacarr Bah; Jared Tanner (CoRR, 2012)

Energy-aware adaptive bi-Lipschitz embeddings. - Bubacarr Bah; Ali Sadeghian; Volkan Cevher (CoRR, 2013)

On construction and analysis of sparse random matrices and expander graphs with applications to compressed sensing. - Bubacarr Bah; Jared Tanner (CoRR, 2013)

Vanishingly Sparse Matrices and Expander Graphs, With Application to Compressed Sensing. - Bubacarr Bah; Jared Tanner (IEEE Trans. Inf. Theory, 2013)

Metric learning with rank and sparsity constraints. - Bubacarr Bah; Stephen Becker; Volkan Cevher; Baran Gozcu (ICASSP, 2014)

Model-based Sketching and Recovery with Expanders. - Bubacarr Bah; Luca Baldassarre; Volkan Cevher (SODA, 2014)

Convex Block-sparse Linear Regression with Expanders - Provably. - Anastasios Kyrillidis; Bubacarr Bah; Rouzbeh Hasheminezhad; Quoc Tran-Dinh; Luca Baldassarre; Volkan Cevher (AISTATS, 2016)

Weighted sparse recovery with expanders. - Bubacarr Bah (CoRR, 2016)

The Sample Complexity of Weighted Sparse Approximation. - Bubacarr Bah; Rachel A. Ward (IEEE Trans. Signal Process., 2016)

On the Construction of Sparse Matrices From Expander Graphs. - Bubacarr Bah; Jared Tanner (Frontiers Appl. Math. Stat., 2018)

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness. - Emily Muller; Jonathan P. Shock; Andreas Bender 003; Julian Kleeberger; Tobias Högen; Martin Justinus Rosenfelder; Bubacarr Bah; Alex Lopez-Rolon (Comput. Biol. Medicine, 2019)

Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers. - Bubacarr Bah; Holger Rauhut; Ulrich Terstiege; Michael Westdickenberg (CoRR, 2019)

On Error Correction Neural Networks for Economic Forecasting. - Mhlasakululeka Mvubu; Emmanuel Kabuga; Christian Plitz; Bubacarr Bah; Ronnie Becker; Hans-Georg Zimmermann (FUSION, 2020)

Efficient Noise-Blind 𝓁1-Regression of Nonnegative Compressible Signals. - Hendrik Bernd Petersen; Bubacarr Bah; Peter Jung 001 (CoRR, 2020)

An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions. - Bubacarr Bah; Jannis Kurtz (CoRR, 2020)

Practical High-Throughput, Non-Adaptive and Noise-Robust SARS-CoV-2 Testing. - Hendrik Bernd Petersen; Bubacarr Bah; Peter Jung 001 (CoRR, 2020)

Editorial: Recent Developments in Signal Approximation and Reconstruction. - Bubacarr Bah; Jean-Luc Bouchot (Frontiers Appl. Math. Stat., 2020)

Improving the Reliability of Pooled Testing with Combinatorial Decoding and Compressed Sensing. - Hendrik Bernd Petersen; Shankar Agarwal; Peter Jung 001; Bubacarr Bah (CISS, 2021)

Low Rank Matrix Approximation for Imputing Missing Categorical Data. - Joseph Muthui Wacira; Dinna Ranirina; Bubacarr Bah (SACAIR, 2021)

Towards the Localisation of Lesions in Diabetic Retinopathy. - Samuel Ofosu Mensah; Bubacarr Bah; Willie Brink (SAI (3), 2021)

Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks. - Jannis Kurtz; Bubacarr Bah (CoRR, 2021)

Discrete optimization methods for group model selection in compressed sensing. - Bubacarr Bah; Jannis Kurtz; Oliver Schaudt (Math. Program., 2021)

Wind Power Prediction with HCNNs for Turbines. - Rockefeller Rockefeller; Bubacarr Bah; Hans-Georg Zimmermann; Vukosi Marivate (ICECET, 2023)

Neural-Gradient Maximum Power Point Tracking Method for Solar Panel. - Joseph Muthui Wacira; Alessandro N. Vargas; Bubacarr Bah; Leonardo Poltronieri Sampaio (ICECET, 2023)

Sparse Activations for Interpretable Disease Grading. - Kerol R. Djoumessi Donteu; Indu Ilanchezian; Laura Kühlewein; Hanna Faber; Christian F. Baumgartner; Bubacarr Bah; Philipp Berens; Lisa M. Koch (MIDL, 2023)

A physics-informed neural network framework for modeling obstacle-related equations. - Hamid El Bahja; Jan Christian Hauffen; Peter Jung 001; Bubacarr Bah; Issa Karambal (CoRR, 2023)

Improved identification accuracy in equation learning via comprehensive R2-elimination and Bayesian model selection. - Daniel Nickelsen; Bubacarr Bah (CoRR, 2023)

Improved identification accuracy in equation learning via comprehensive R2-elimination and Bayesian model selection. - Daniel Nickelsen; Bubacarr Bah (Trans. Mach. Learn. Res., 2023)

This Actually Looks Like that: Proto-BagNets for Local and Global Interpretability-by-Design. - Kerol Djoumessi; Bubacarr Bah; Laura Kühlewein; Philipp Berens; Lisa M. Koch (MICCAI (10), 2024)

Physics-informed neural networks for Timoshenko system with Thermoelasticity. - Sabrine Chebbi; Joseph Muthui Wacira; Makram Hamouda; Bubacarr Bah (CoRR, 2024)

Recent methodological advances in federated learning for healthcare. - Fan Zhang; Daniel Kreuter; Yichen Chen; Sören Dittmer; Samuel Tull; Tolou Shadbahr; Martijn Schut; Folkert W. Asselbergs; Sujoy Kar; Suthesh Sivapalaratnam; Sophie Williams; Mickey Koh; et al. (Patterns, 2024)