Publications by Galen Reeves.

Papers Published

  1. Reeves, G, A Two-Moment Inequality with Applications to Rényi Entropy and Mutual Information., Entropy, vol. 22 no. 11 (November, 2020), pp. 1-26 [doi]  [abs].
  2. 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].
  3. 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].
  4. 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].
  5. 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].
  6. 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].
  7. 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].
  8. 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].
  9. 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].
  10. 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].
  11. 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].
  12. 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 (January, 2018), pp. 1064-1070, IEEE [doi]  [abs].
  13. 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].
  14. Reeves, G, Conditional central limit theorems for Gaussian projections, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 3045-3049, IEEE [doi]  [abs].
  15. 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].
  16. 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].
  17. 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, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp) (June, 2017), pp. 3026-3030, IEEE [doi]  [abs].
  18. Mayya, V; Mainsah, B; Reeves, G, Information-theoretic analysis of refractory effects in the P300 speller, Conference Record Asilomar Conference on Signals, Systems and Computers (March, 2017), pp. 1621-1625, IEEE [doi]  [abs].
  19. 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].
  20. 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].
  21. 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].
  22. 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].
  23. 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].
  24. 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].
  25. 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].
  26. 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].
  27. 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].
  28. 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].
  29. 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].
  30. 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].
  31. 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].
  32. 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].
  33. 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].
  34. 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].
  35. Reeves, G; Gastpar, M, "Compressed" compressed sensing, Ieee International Symposium on Information Theory Proceedings (August, 2010), pp. 1548-1552, IEEE [doi]  [abs].
  36. 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].
  37. 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].
  38. 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].
  39. 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].