J. K. Behne and G. Reeves
Rank-one matrix estimation with groupwise heteroskedasticity
[arxiv]
G. Reeves,
Understanding Phase Transitions via Mutual Information and MMSE,
Presented at ISIT Tutorial, July 2019.
[slides]
G. Reeves,
Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms,
Presented at the Caltech EE Systems Seminar, May 2018.
[slides]
G. Reeves,
Understanding the Random Linear Estimation using a Conditional CLT,
Presented at the Workshop on Statistical physics, Learning, Inference and Networks, École de Physique, des Houches, March 2017.
[slides]
G. Reeves,
Understanding the MMSE of compressed sensing one measurement at a time,
Presented at the Institut Henri Poincaré Thematic Program on the Nexus of Information and Computation Theories, March 2016.
[video]
G. Reeves,
Robust Compressed Sensing: How Undersampling Introduces Noise and What We Can Do About It,
Presented at the Duke Engineering New Faculty Lecture Series, February 2014.
[video]
S. Goldt, G. Reeves, M. Mézard, F. Krzakala, and L. Zdeborová
The Gaussian equivalence of generative models for learning with two-layer neural networks
[arxiv]
A. Kipnis and G. Reeves,
Gaussian Approximation of Quantization Error for Estimation from Compressed Data,
IEEE Transactions on Information Theory, vo. 67, no. 8, pp. 5562 -- 5579, August, 2021.
[doi]
W. van den Boom, G. Reeves, and D. B. Dunson,
Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation,
Biometrika, vo. 108, no. 2, pp. 269--282, June 2021
[doi]
[arxiv]
G. Reeves,
Information-theoretic limits of a matrix tensor product model,
IEEE Journal on Selected Areas in Information Theory, vo. 1, no. 2, 2020
[doi]
G. Reeves,
A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information,
Entropy, vo. 22 , no. 11, 2020.
[doi]
G. Reeves and H. Pfister,
Understanding Phase Transitions via Mutual Information and MMSE,
In Information-Theoretic Methods in Data Science; Rodrigues, M.R.D.; Eldar, Y.C., Eds.; Cambridge University
Press: Cambridge, UK, 2020; Chapter 7.
[arxiv]
J. Barbier and G. Reeves,
Information-theoretic limits of a multiview low-rank symmetric spiked matrix model,
Proc. IEEE International Symposium on Information Theory (ISIT), Long Beach, CA, June 2020.
[arxiv]
G. Reeves, J. Xu, and I. Zadik,
All-or-nothing phenomena from single-letter to high dimensions,
Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Guadeloupe, December 2019.
[arxiv]
G. Reeves, V. Mayya, and A. Volfovsky,
The Geometry of Community Detection via the MMSE Matrix,
Proc. IEEE International Symposium on Information Theory (ISIT), Paris, France, July 2019.
[arxiv] [slides]
A. Kipnis and G. Reeves,
Gaussian Approximation of Quantization Error for Estimation from Compressed Data,
Proc. IEEE International Symposium on Information Theory (ISIT), Paris, France, July 2019.
[arxiv]
G. Reeves, J. Xu, and I. Zadik,
The All-or-Nothing Phenomenon in Sparse Linear Regression,
Proc. Conference on Learning Theory (COLT), June 2019.
[arxiv]
M. Bertran, N. Martinez, A. Papadaki, Q. Qiu, M. Rodrigues, G. Reeves, and G. Sapiro,
Adversarially Learned Representations for Information Obfuscation and Inference,
Proc. International Conference on Machine Learning (ICML), June 2019.
G. Reeves and H. Pfister,
The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact,
IEEE Transactions on Information Theory, vo. 65, no. 4, pp. 2252 -- 2283, January, 2019.
[doi]
G. Reeves, H. Pfister, and A. Dytso,
Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels,
Proc. IEEE International Symposium on Information Theory (ISIT), Vail, CO, June 2018.
[slides]
A. Kipnis, G. Reeves, Y. C. Eldar,
Single Letter Formulas for Quantized Compressed Sensing with Gaussian Codebooks,
Proc. IEEE International Symposium on Information Theory (ISIT), Vail, CO, June 2018.
G. Reeves,
Additivity of Information in Multilayer Networks via Additive Gaussian Noise Transforms,
Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, October 2017.
[arxiv][slides]
G. Reeves,
Two-Moment Inequalities for Rényi Entropy and Mutual Information,
Proc. IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, July 2017.
[arxiv][slides]
A. Kipnis, G. Reeves, Y. C. Eldar, A. Goldsmith,
Compressed Sensing under Optimal Quantization,
Proc. IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, July 2017.
[slides]
G. Reeves,
Conditional Central Limit Theorems for Gaussian Projections,
Proc. IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, July 2017.
[arxiv][slides]
B. O. Mainsah, G. Reeves, L. M. Collins, and C. S. Throckmorton,
Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction,
Journal of Neural Engineering. vo. 14, no. 4, June, 2017.
[doi]
B. O. Mainsah, L. M. Collins, G. Reeves, and C. S. Throckmorton,
A Performance-Based Approach to Designing the Stimulus Presentation Paradigm for the P300-Based BCI by Exploiting Coding Theory,
Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, March 2017.
F. Renna, L. Wang and X. Yuan and J. Yang, G. Reeves, R. Calderbank, and L. Carin, and M. R. D. Rodrigues,
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Noisy Features in the Presence of Side Information,
IEEE Transactions on Information Theory, vo. 62, no. 11, pp. 6459-6492, November, 2016.
[doi][arxiv]
V. Mayya, B. Mainsah, and G. Reeves,
Information-Theoretic Analysis of Refractory Effects in the P300 Speller,
Proc. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2016.
[arxiv]
V. Mayya, B. Mainsah, and G. Reeves,
Modeling the P300-Based Brain-Computer Interface As a Channel with Memory,
Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, October 2016.
[doi] [paper]
P. Llull, G. Reeves, D. Brady, and L. Carin,
Performance Assessment of Image Translation-engineered Point Spread Functions,
Proc. Imaging and Applied Optics Congress, Heidelberg, Germany, July 2016.
[doi]
G. Reeves and H. D. Pfister,
The Replica-Symmetric Prediction for Compressed Sensing with Gaussian Matrices is Exact,
Proc. IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, July 2016.
[doi][arxiv]
W. van den Boom and D. B. Dunson and G. Reeves,
Quantifying Uncertainty in Variable Selection with Arbitrary Matrices,
Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 2015.
[paper]
F. Renna, L. Wang, X. Yuan, J. Yang, G. Reeves, R. Calderbank, L Carin, and M. R. D. Rodrigues,
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Noisy Features in the Presence of Side Information,
Proc. IEEE International Symposium on Information Theory (ISIT), Hong Kong, June 2015.
[arxiv]
G. Reeves,
The Fundamental Limits of Stable Recovery in Compressed Sensing,
Proc. IEEE International Symposium on Information Theory (ISIT), Honolulu, HI, July 2014.
[paper]
G. Reeves,
Beyond sparsity: Universally stable compressed sensing when the number of 'free' values is less than the number of observations,
Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Martin, France, December 2013.
[paper]
G. Reeves and D. L. Donoho,
The Minimax Noise Sensitivity in Compressed Sensing,
Proc. IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, July 2013.
[paper]
D. L. Donoho and G. Reeves,
Achieving Bayes MMSE Performance in the Sparse Signal + Gaussian White Noise Model when the Noise Level is Unknown,
Proc. IEEE International Symposium on Information Theory (ISIT), Istanbul, Turkey, July 2013.
[paper]
G. Reeves and M. Gastpar,
Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds,
IEEE Transactions on Information Theory, vo. 59, no. 6, pp. 3451-3465, June, 2013.
[doi]
[arxiv]
D. L. Donoho and G. Reeves,
The Sensitivity of Compressed Sensing Performance to Relaxation of Sparsity,
Proc. IEEE International Symposium on Information Theory (ISIT), Boston, MA, July 2012.
[arxiv]
G. Reeves and M. Gastpar,
The Sampling Rate-Distortion Tradeoff for Sparsity Pattern Recovery in Compressed Sensing,
IEEE Transactions on Information Theory, vo. 58, no. 10, pp. 3065-3092, May, 2012.
[doi][arxiv]
G. Reeves and M. Gastpar,
Compressed Sensing Phase Transitions: Rigorous Bounds versus Replica Predictions,
Proc. Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2012.
[paper]
G. Reeves, N. Goela, N. Milosavljevic, and M. Gastpar,
A Compressed Sensing Wire-Tap Channel,
Proc. IEEE Information Theory Workshop (ITW), Paraty, Brazil, October 2011.
[arxiv]
G. Reeves and M. Gastpar,
On the Role of Diversity in Sparsity Estimation,
Proc. IEEE International Symposium on Information Theory (ISIT), Saint Petersburg, Russia, August 2011.
[arxiv][video]
G. Reeves and M. Gastpar,
"Compressed" Compressed Sensing,
Proc. IEEE International Symposium on Information Theory (ISIT), Austin, TX, June 2010.
[arxiv]
G. Reeves and M. Gastpar,
A Note on Optimal Support Recovery in Compressed Sensing,
Proc. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2009.
[paper]
G. Reeves, J. Liu, S. Nath, and F. Zhao,
Managing Massive Time Series Streams with Multi-Scale Compressed Trickles,
Proc. International Conference on Very Large Data Bases (VLDB), Lyon, France, August 2009.
[paper]
G. Reeves and M. Gastpar,
Efficient Sparsity Pattern Recovery,
Proc. Symposium on Information Theory in the Benelux, Eindhoven, The Netherlands, May 2009.
[paper]
G. Reeves and M. Gastpar,
Sampling Bounds for Sparse Support Recovery in the Presence of Noise,
Proc. IEEE International Symposium on Information Theory (ISIT), Toronto, Canada, July 2008.
[paper]
G. Reeves and M. Gastpar,
Differences between Observation and Sampling Error in Sparse Signal Reconstruction,
Proc. IEEE Workshop on Statistical Signal Processing (SSP), Madison, WI, August, 2007.
[paper]
G. Reeves,
Sparsity Pattern Recovery in Compressed Sensing,
Ph.D. Thesis, Dec 2011.
[technical report]