Papers Published

1. Rossetti, R; Nazer, B; Reeves, G, Linear operator approximate message passing (OpAMP), Information and Inference, vol. 14 no. 4 (December, 2025) [doi]  [abs].
2. Rossetti, R; Reeves, G, Fundamental Limits for High-Dimensional Factor Regression Models, IEEE International Symposium on Information Theory Proceedings (January, 2025) [doi]  [abs].
3. Reeves, G; Pfister, HD, Information-Theoretic Proofs for Diffusion Sampling, IEEE International Symposium on Information Theory Proceedings (January, 2025) [doi]  [abs].
4. Reeves, G; Pfister, HD, Reed-Muller Codes on BMS Channels Achieve Vanishing Bit-Error Probability for all Rates Below Capacity, IEEE Transactions on Information Theory, vol. 70 no. 2 (February, 2024), pp. 920-949 [doi]  [abs].
5. Rossetti, R; Nazer, B; Reeves, G, Linear Operator Approximate Message Passing: Power Method with Partial and Stochastic Updates, IEEE International Symposium on Information Theory Proceedings (January, 2024), pp. 741-746 [doi]  [abs].
6. Reeves, G; Pfister, HD, Achieving Capacity on Non-Binary Channels with Generalized Reed-Muller Codes, IEEE International Symposium on Information Theory Proceedings, vol. 2023-June (January, 2023), pp. 2057-2062 [doi]  [abs].
7. Van Den Boom, W; Reeves, G; Dunson, DB, Correction to: ‘Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation’, Biometrika, vol. 109 no. 1 (March, 2022), pp. 275 [doi]  [abs].
8. Behne, JK; Reeves, G, Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity, Proceedings of Machine Learning Research, vol. 151 (January, 2022), pp. 8650-8672  [abs].
9. Goldfeld, Z; Greenewald, K; Nuradha, T; Reeves, G, k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension, Advances in Neural Information Processing Systems, vol. 35 (January, 2022)  [abs].
10. Kipnis, A; Reeves, G, Gaussian Approximation of Quantization Error for Estimation from Compressed Data, IEEE Transactions on Information Theory, vol. 67 no. 8 (August, 2021), pp. 5562-5579 [doi]  [abs].
11. VAN DEN Boom, W; Reeves, G; Dunson, DB, Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation., Biometrika, vol. 108 no. 2 (June, 2021), pp. 269-282 [doi]  [abs].
12. Zhang, Y; Cheng, X; Reeves, G, Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples, Proceedings of Machine Learning Research, vol. 130 (January, 2021), pp. 2422-2430  [abs].
13. Goldt, S; Loureiro, B; Reeves, G; Krzakala, F; Mézard, M; Zdeborová, L, The Gaussian equivalence of generative models for learning with shallow neural networks, Proceedings of Machine Learning Research, vol. 145 (January, 2021), pp. 426-471  [abs].
14. Reeves, G, A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information., Entropy (Basel, Switzerland), vol. 22 no. 11 (November, 2020), pp. E1244 [doi]  [abs].
15. Reeves, G, Information-theoretic limits for the matrix tensor product, IEEE Journal on Selected Areas in Information Theory, vol. 1 no. 3 (November, 2020), pp. 777-798 [doi]  [abs].
16. Barbier, J; Reeves, G, Information-theoretic limits of a multiview low-rank symmetric spiked matrix model, IEEE International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 2771-2776 [doi]  [abs].
17. Mathews, H; Mayya, V; Volfovsky, A; Reeves, G, Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise, 2019 IEEE 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing Camsap 2019 Proceedings (December, 2019), pp. 699-703 [doi]  [abs].
18. Reeves, G; Xu, J; Zadik, I, All-or-Nothing Phenomena: From Single-Letter to High Dimensions, 2019 IEEE 8th International Workshop on Computational Advances in Multi Sensor Adaptive Processing Camsap 2019 Proceedings (December, 2019), pp. 654-658 [doi]  [abs].
19. Mayya, V; Reeves, G, Mutual Information in Community Detection with Covariate Information and Correlated Networks, 2019 57th Annual Allerton Conference on Communication Control and Computing Allerton 2019 (September, 2019), pp. 602-607 [doi]  [abs].
20. Reeves, G; Mayya, V; Volfovsky, A, The Geometry of Community Detection via the MMSE Matrix, IEEE International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 400-404 [doi]  [abs].
21. Kipnis, A; Reeves, G, Gaussian Approximation of Quantization Error for Estimation from Compressed Data, IEEE International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 2029-2033 [doi]  [abs].
22. Reeves, G; Pfister, HD, The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact, IEEE Transactions on Information Theory, vol. 65 no. 4 (April, 2019), pp. 2252-2283 [doi]  [abs].
23. Bertran, M; Martinez, N; Papadaki, A; Qiu, Q; Rodrigues, M; Reeves, G; Sapiro, G, Adversarially learned representations for information obfuscation and inference, 36th International Conference on Machine Learning Icml 2019, vol. 2019-June (January, 2019), pp. 960-974  [abs].
24. Reeves, G; Xu, J; Zadik, I, The All-or-Nothing Phenomenon in Sparse Linear Regression, Proceedings of Machine Learning Research, vol. 99 no. 3-4 (January, 2019), pp. 2652-2663 [doi]  [abs].
25. Bertran, M; Martinez, N; Papadaki, A; Qiu, Q; Rodrigues, M; Reeves, G; Sapiro, G, Adversarially Learned Representations for Information Obfuscation and Inference, Proceedings of Machine Learning Research, vol. 97 (January, 2019), pp. 614-623  [abs].
26. Reeves, G; Pfister, HD; Dytso, A, Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels, IEEE International Symposium on Information Theory Proceedings, vol. 2018-June (August, 2018), pp. 1754-1758, IEEE [doi]  [abs].
27. Kipnis, A; Reeves, G; Eldar, YC, Single Letter Formulas for Quantized Compressed Sensing with Gaussian Codebooks, IEEE International Symposium on Information Theory Proceedings, vol. 2018-June (August, 2018), pp. 71-75, IEEE [doi]  [abs].
28. Kipnis, A; Reeves, G; Eldar, YC; Goldsmith, AJ, Compressed sensing under optimal quantization, IEEE International Symposium on Information Theory Proceedings (August, 2017), pp. 2148-2152, IEEE [doi]  [abs].
29. Reeves, G, Two-moment inequalities for Rényi entropy and mutual information, IEEE International Symposium on Information Theory Proceedings (August, 2017), pp. 664-668, IEEE [doi]  [abs].
30. Reeves, G, Conditional central limit theorems for Gaussian projections, IEEE International Symposium on Information Theory Proceedings (August, 2017), pp. 3045-3049, IEEE [doi]  [abs].
31. Mainsah, BO; Reeves, G; Collins, LM; Throckmorton, CS, Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction., Journal of neural engineering, vol. 14 no. 4 (August, 2017), pp. 046025 [doi]  [abs].
32. Reeves, G, Additivity of information in multilayer networks via additive Gaussian noise transforms, 55th Annual Allerton Conference on Communication Control and Computing Allerton 2017, vol. 2018-January (July, 2017), pp. 1064-1070, IEEE [doi]  [abs].
33. Mainsah, BO; Collins, LM; Reeves, G; Throckmorton, CS, A performance-based approach to designing the stimulus presentation paradigm for the P300-based BCI by exploiting coding theory, ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (June, 2017), pp. 3026-3030, IEEE [doi]  [abs].
34. Mayya, V; Mainsah, B, Information Theoretic Analysis of the Impact of Refractory Effects on the P300 Speller (March, 2017), pp. 1621-1625, IEEE [doi]  [abs].
35. Mayya, V; Mainsah, B; Reeves, G, Modeling the P300-based brain-computer interface as a channel with memory, 54th Annual Allerton Conference on Communication Control and Computing Allerton 2016 (February, 2017), pp. 23-30, IEEE [doi]  [abs].
36. Renna, F; Wang, L; Yuan, X; Yang, J; Reeves, G; Calderbank, R; Carin, L; Rodrigues, MRD, Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information, IEEE Transactions on Information Theory, vol. 62 no. 11 (November, 2016), pp. 6459-6492, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs].
37. Reeves, G; Pfister, HD, The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact, IEEE International Symposium on Information Theory Proceedings, vol. 2016-August (August, 2016), pp. 665-669, IEEE [doi]  [abs].
38. Llull, P; Reeves, G; Carin, L; Brady, DJ, Performance assessment of image translation-engineered point spread functions, Optics Infobase Conference Papers, vol. Part F7-COSI 2016 (July, 2016), OSA [doi]  [abs].
39. Renna, F; Wang, L; Yuan, X; Yang, J; Reeves, G; Calderbank, R; Carin, L; Rodrigues, MRD, Classification and reconstruction of compressed GMM signals with side information, IEEE International Symposium on Information Theory Proceedings, vol. 2015-June (September, 2015), pp. 994-998 [doi]  [abs].
40. Van Den Boom, W; Dunson, D; Reeves, G, Quantifying uncertainty in variable selection with arbitrary matrices, 2015 IEEE 6th International Workshop on Computational Advances in Multi Sensor Adaptive Processing Camsap 2015 (January, 2015), pp. 385-388 [doi]  [abs].
41. Reeves, G, The fundamental limits of stable recovery in compressed sensing, IEEE International Symposium on Information Theory Proceedings (January, 2014), pp. 3017-3021, IEEE [doi]  [abs].
42. Reeves, G, Beyond sparsity: Universally stable compressed sensing when the number of 'free' values is less than the number of observations, 2013 5th IEEE International Workshop on Computational Advances in Multi Sensor Adaptive Processing Camsap 2013 (December, 2013), pp. 17-20, IEEE [doi]  [abs].
43. Reeves, G; Gastpar, MC, Approximate sparsity pattern recovery: Information-theoretic lower bounds, IEEE Transactions on Information Theory, vol. 59 no. 6 (May, 2013), pp. 3451-3465, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs].
44. Donoho, D; Reeves, G, Achieving Bayes MMSE performance in the sparse signal + Gaussian white noise model when the noise level is unknown, IEEE International Symposium on Information Theory Proceedings (January, 2013), pp. 101-105, IEEE [doi]  [abs].
45. Reeves, G; Donoho, D, The minimax noise sensitivity in compressed sensing, IEEE International Symposium on Information Theory Proceedings (January, 2013), pp. 116-120, IEEE [doi]  [abs].
46. Reeves, G; Gastpar, M, Compressed sensing phase transitions: Rigorous bounds versus replica predictions, 2012 46th Annual Conference on Information Sciences and Systems Ciss 2012 (November, 2012), IEEE [doi]  [abs].
47. Donoho, D; Reeves, G, The sensitivity of compressed sensing performance to relaxation of sparsity, IEEE International Symposium on Information Theory Proceedings (October, 2012), pp. 2211-2215, IEEE [doi]  [abs].
48. Reeves, G; Gastpar, M, The sampling rate-distortion tradeoff for sparsity pattern recovery in compressed sensing, IEEE Transactions on Information Theory, vol. 58 no. 5 (May, 2012), pp. 3065-3092, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs].
49. Reeves, G; Goela, N; Milosavljevic, N; Gastpar, M, A compressed sensing wire-tap channel, 2011 IEEE Information Theory Workshop Itw 2011 (December, 2011), pp. 548-552, IEEE [doi]  [abs].
50. Reeves, G; Gastpar, M, On the role of diversity in sparsity estimation, IEEE International Symposium on Information Theory Proceedings (October, 2011), pp. 119-123, IEEE [doi]  [abs].
51. Reeves, G; Gastpar, M, "Compressed" compressed sensing, IEEE International Symposium on Information Theory Proceedings (August, 2010), pp. 1548-1552, IEEE [doi]  [abs].
52. Reeves, G; Gastpar, M, A note on optimal support recovery in compressed sensing, Conference Record Asilomar Conference on Signals Systems and Computers (December, 2009), pp. 1576-1580, IEEE [doi]  [abs].
53. Reeves, G; Liu, J; Nath, S; Zhao, F, Managing massive time series streams with multi-scale compressed trickles, Proceedings of the VLDB Endowment, vol. 2 no. 1 (January, 2009), pp. 97-108, VLDB Endowment [doi]  [abs].
54. Reeves, G; Gastpar, M, Sampling bounds for sparse support recovery in the presence of noise, IEEE International Symposium on Information Theory Proceedings (September, 2008), pp. 2187-2191, IEEE [doi]  [abs].
55. Reeves, G; Gastpar, M, Differences between observation and sampling error in sparse signal reconstruction, IEEE Workshop on Statistical Signal Processing Proceedings (December, 2007), pp. 690-694, IEEE [doi]  [abs].

Chapters

1. Reeves, G; Pfister, HD, Understanding Phase Transitions via Mutual Information and MMSE, in Information Theoretic Methods in Data Science (January, 2021), pp. 197-228 [doi]  [abs].