| 2026 | ISIT | Reed-Muller Codes Achieve the Symmetric Capacity on Finite-State Channels. | Henry D. Pfister, Navin Kashyap, Jean-Franois Chamberland, Galen Reeves |
| 2025 | ISIT | Information-Theoretic Proofs for Diffusion Sampling. | Galen Reeves, Henry D. Pfister |
| 2025 | ISIT | Fundamental Limits for High-Dimensional Factor Regression Models. | Riccardo Rossetti, Galen Reeves |
| 2024 | ISIT | Linear Operator Approximate Message Passing: Power Method with Partial and Stochastic Updates. | Riccardo Rossetti, Bobak Nazer, Galen Reeves |
| 2023 | ISIT | Achieving Capacity on Non-Binary Channels with Generalized Reed-Muller Codes. | Galen Reeves, Henry D. Pfister |
| 2022 | AISTATS | Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity. | Joshua K. Behne, Galen Reeves |
| 2021 | AISTATS | Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples. | Yixing Zhang, Xiuyuan Cheng, Galen Reeves |
| 2020 | ISIT | Information-theoretic limits of a multiview low-rank symmetric spiked matrix model. | Jean Barbier, Galen Reeves |
| 2019 | COLT | The All-or-Nothing Phenomenon in Sparse Linear Regression. | Galen Reeves, Jiaming Xu, Ilias Zadik |
| 2019 | ICML | Adversarially Learned Representations for Information Obfuscation and Inference. | Martn Bertrn, Natalia Martnez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, Guillermo Sapiro |
| 2019 | ISIT | Gaussian Approximation of Quantization Error for Estimation from Compressed Data. | Alon Kipnis, Galen Reeves |
| 2019 | ISIT | The Geometry of Community Detection via the MMSE Matrix. | Galen Reeves, Vaishakhi Mayya, Alexander Volfovsky |
| 2018 | ISIT | Single Letter Formulas for Quantized Compressed Sensing with Gaussian Codebooks. | Alon Kipnis, Galen Reeves, Yonina C. Eldar |
| 2018 | ISIT | Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels. | Galen Reeves, Henry D. Pfister, Alex Dytso |
| 2017 | CISS | Decoupling in random linear estimation. | Galen Reeves, Harry Pfister |
| 2017 | ICASSP | A performance-based approach to designing the stimulus presentation paradigm for the P300-based BCI by exploiting coding theory. | B. O. Mainsah, Leslie M. Collins, Galen Reeves, Chandra S. Throckmorton |
| 2017 | ISIT | Compressed sensing under optimal quantization. | Alon Kipnis, Galen Reeves, Yonina C. Eldar, Andrea J. Goldsmith |
| 2017 | ISIT | Two-moment inequalities for Rnyi entropy and mutual information. | Galen Reeves |
| 2017 | ISIT | Conditional central limit theorems for Gaussian projections. | Galen Reeves |
| 2016 | ACSSC | Information-theoretic analysis of refractory effects in the P300 speller. | Vaishakhi Mayya, Boyla Mainsah, Galen Reeves |
| 2016 | ISIT | The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact. | Galen Reeves, Henry D. Pfister |
| 2015 | ISIT | Classification and reconstruction of compressed GMM signals with side information. | Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, A. Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues |
| 2014 | ISIT | The fundamental limits of stable recovery in compressed sensing. | Galen Reeves |
| 2013 | ISIT | Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown. | David L. Donoho, Galen Reeves |
| 2013 | ISIT | The minimax noise sensitivity in compressed sensing. | Galen Reeves, David L. Donoho |
| 2012 | CISS | Compressed sensing phase transitions: Rigorous bounds versus replica predictions. | Galen Reeves, Michael Gastpar |
| 2012 | ISIT | The sensitivity of compressed sensing performance to relaxation of sparsity. | David L. Donoho, Galen Reeves |
| 2011 | ISIT | On the role of diversity in sparsity estimation. | Galen Reeves, Michael Gastpar |
| 2011 | ITW | A compressed sensing wire-tap channel. | Galen Reeves, Naveen Goela, Nebojsa Milosavljevic, Michael Gastpar |
| 2010 | ISIT | "Compressed" compressed sensing. | Galen Reeves, Michael Gastpar |
| 2008 | ISIT | Sampling bounds for sparse support recovery in the presence of noise. | Galen Reeves, Michael Gastpar |