Publications by Galen Reeves.

Papers Published

  1. 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].
  2. 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].
  3. Van Den Boom, W; Reeves, G; Dunson, DB, Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068), Biometrika, vol. 109 no. 1 (March, 2022), pp. 275 [doi]  [abs].
  4. 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].
  5. 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].
  6. 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].
  7. 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].
  8. 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].
  9. 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].
  10. 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].
  11. 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].
  12. 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].
  13. 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].
  14. 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].
  15. 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].
  16. 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].
  17. 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].
  18. 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].
  19. 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].
  20. 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].
  21. 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].
  22. 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].
  23. 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].
  24. Reeves, G, Conditional central limit theorems for Gaussian projections, IEEE International Symposium on Information Theory - Proceedings (August, 2017), pp. 3045-3049, IEEE [doi]  [abs].
  25. 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].
  26. 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].
  27. 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].
  28. 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].
  29. 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].
  30. 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].
  31. 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].
  32. 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].
  33. 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].
  34. 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].
  35. 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].
  36. 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].
  37. 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].
  38. 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].
  39. 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].
  40. 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].
  41. 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].
  42. 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].
  43. 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].
  44. 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].
  45. 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].
  46. Reeves, G; Gastpar, M, "Compressed" compressed sensing, IEEE International Symposium on Information Theory - Proceedings (August, 2010), pp. 1548-1552, IEEE [doi]  [abs].
  47. 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].
  48. 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].
  49. 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].
  50. 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].