Martin J. Wainwright: Publications

 

• Sorted by year • Books • Journals • Conferences • Research Reports • Theses • Miscellaneous • 

Books

  1. M. J. Wainwright, "High-dimensional statistics: A non-asymptotic viewpoint",Cambridge University Press , 2019. [bibtex] 
  2. T. Hastie, R. Tibshirani, M. J. Wainwright, "Statistical learning with sparsity: The Lasso and generalizations",CRC Press, Chapman and Hall , 2015. [bibtex] 

Journal Articles

  1. Y. Duan, M. Wang, M. J. Wainwright, "Optimal value estimation using kernel-based temporal difference methods", Annals of Statistics, vol. 1, pp. To appear, 2024. [pdf]  [bibtex] 
  2. W. Mou, A. Pananjady, M. J. Wainwright, P. L. Bartlett, "Optimal and instance-dependent guarantees for Markovian linear stochastic approximation", Mathematical Statistics and Learning, vol. 7, no. 1, pp. 41–153, 2024. [pdf]  [bibtex] 
  3. W. Mou, N. Ho, M. J. Wainwright, P. Bartlett, M. I. Jordan, "A diffusion process perspective on posterior contraction rates for parameters", SIAM Journal on Math. Data Sci., pp. To appear, 2024. [pdf]  [bibtex] 
  4. K. Khamaru, E. Xia, M. J. Wainwright and M. I. Jordan, "Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning", IEEE Trans. Info. Theory, vol. 1, pp. To appear, April, 2024. [pdf]  [bibtex] 
  5. C. Ma, R. Pathak, M. J. Wainwright, "Optimally tackling covariate shift in RKHS-based nonparametric regression", Annals of Statistics, vol. 51, no. 2, 2023. [pdf]  [bibtex] 
  6. E. Xia, K. Khamaru, M. J. Wainwright, M. I. Jordan, "Instance-dependent confidence and early stopping in reinforcement learning", Journal of Machine Learning Research, pp. 1–43, 2023. [pdf]  [bibtex] 
  7. W. Mou, A. Pananjady, M. J. Wainwright, "Optimal oracle inequalities for solving projected fixed-point equations", Mathematics of Operations Research, vol. 48, no. 4, pp. 2308–2336, November, 2023. [pdf]  [bibtex] 
  8. R. Dwivedi, C. Singh, B. Yu, M. J. Wainwright, "Revisiting minimum description length complexity in overparameterized models", Journal of Machine Learning Research, vol. 24, pp. 1–59, 2023. [pdf]  [bibtex] 
  9. N. Ho, K. Khamaru, R. Dwivedi, M. J. Wainwright, M. I. Jordan, B. Yu, "Instability, Computational Efficiency and Statistical Accuracy", Journal of Machine Learning Research, pp. To appear, November, 2022. [bibtex]  (Originally posted as arxiv:2005.11411) 
  10. W. Mou, N. Flammarion, M. J. Wainwright, P. L. Bartlett, "An efficient sampling algorithm for non-smooth composite potentials", Journal of Machine Learning Research, vol. 23, pp. 1–50, 2022. [pdf]  [bibtex] 
  11. C. Ma, B. Zhu, J. Jiao, M. J. Wainwright, "Minimax Off-Policy Evaluation for Multi-Armed Bandits", IEEE Trans. Information Theory, vol. 68, pp. 5314–5339, March, 2022. [bibtex] 
  12. A. Pananjady, M. J. Wainwright, "Instance-dependent $\ell_\infty$-bounds for policy evaluation in tabular reinforcement learning", IEEE Trans. Info. Theory, vol. 67, no. 1, pp. 566–585, January, 2021. [bibtex] 
  13. W. Mou, N. Flammarion, M. J. Wainwright, P. L. Bartlett, "Improved bounds for discretization of Langevin diffusions: Near optimal rates without convexity", Bernoulli, 2021. [bibtex] 
  14. W. Mou, Y. Ma, M. J. Wainwright, P. L. Bartlett, M. I. Jordan, "High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm", Journal of Machine Learning Research, vol. 22, pp. 1–41, January, 2021. [bibtex] 
  15. K. Khamaru, A. Pananjady, F. Ruan, M. J. Wainwright, M. I. Jordan, "Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis", SIAM J. Math. Data Science, vol. 3, no. 4, pp. 1013–1040, October, 2021. [bibtex] 
  16. N. B. Shah, S. Balakrishnan, M. J. Wainwright, "A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness", IEEE Trans. Info. Theory, vol. 67, pp. 4162–4184, 2021. [bibtex] 
  17. C. Mao, A. Pananjady, M. J. Wainwright, "Towards Optimal Estimation of Bivariate Isotonic Marices with Unknown Permutations", Annals of Statistics, vol. 48, no. 6, pp. 3183–3205, 2020. [bibtex] 
  18. Y. Wei, B. Fang, M. J. Wainwright, "From Gauss to Kolmogorov: Localized measures of complexity for ellipses", Electronic Journal of Statistics, vol. 14, no. 2, pp. 2988–3031, 2020. [bibtex] 
  19. Y. Chen, R. Dwivedi, M. J. Wainwright, B\ . Yu, "Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients", Journal of Machine Learning Research, vol. 21, no. 92, pp. 1–71, 2020. [bibtex] 
  20. M. Rabinovich, A. Ramdas, M. I. Jordan, M. J. Wainwright, "Function-Specific Mixing Times and Concentration Away from Equilibrium", Bayesian Analysis, vol. 2, pp. 505–532, 2020. [bibtex] 
  21. D. Malik, A. Pananjady, K. Bhatia, K. Khamaru, P. L. Bartlett, M. J. Wainwright, "Derivative-free methods for policy optimization: Guarantees for linear-quadratic systems", Journal of Machine Learning Research, vol. 51, pp. 1––51, 2020. [bibtex] 
  22. A. Pananjady, C. Mao, V. Muthukumar, M. J. Wainwright, T. A. Courtade, "Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons", Annals of Statistics, vol. 48, no. 2, pp. 1072–1097, 2020. [bibtex] 
  23. Y. Chen, R. Dwivedi, M. J. Wainwright, B. Yu, "Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients", Journal of Machine Learning Research, vol. 21, no. 92, pp. 1–72, May, 2020. [bibtex] 
  24. M. Rabinovich, A. Ramdas, M. J. Wainwright, M. I. Jordan, "Optimal Rates and Tradeoffs in Multiple Testing", Statistica Sinica, vol. 30, pp. 741–762, 2020. [bibtex] 
  25. Y. Wei, M. J. Wainwright, "The local geometry of testing in ellipses: Tight control via localized Kolmogorov widths", IEEE Trans. Info. Theory, vol. 66, no. 8, pp. 5110–5129, August, 2020. [bibtex] 
  26. R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan, B. Yu, "Singularity, Misspecification, and the Convergence Rate of EM", Annals of Statistics, vol. 48, no. 6, pp. 3161––3182, 2020. [bibtex] 
  27. Y. Wei, F. Yang, M. J. Wainwright, "Early stopping for kernel boosting algorithms: A general analysis with localized complexities", IEEE Trans. Info. Theory, vol. 65, no. 10, pp. 6685–6703, October, 2019. [bibtex] 
  28. N. B. Shah, S. Balakrishnan, M. J. Wainwright, "Low permutation-rank matrices: Structural properties and noisy completion", Journal of Machine Learning Research, vol. 20, pp. 1–43, June, 2019. [bibtex] 
  29. R. Heckel, N. B. Shah, K. Ramchandran, M. J. Wainwright, "Active Ranking from Pairwise Comparisons and When Parametric Assumptions Don’t Help", Annals of Statistics, vol. 47, no. 6, pp. 3099–3126, 2019. [bibtex] 
  30. Y. Wei, M. J. Wainwright, A. Guntuboyina, "The geometry of testing over convex cones: Generalized likelihood ratio tests and minimax radii", Annals of Statistics, vol. 47, no. 2, pp. 994–1024, 2019. [bibtex] 
  31. A. Ramdas, R. F. Barber, M. J. Wainwright, M. I. Jordan, "A Unified Treatment of Multiple Testing with Prior Knowledge using the $p$-filter", Annals of Statistics, vol. 47, no. 5, pp. 2790–2821, 2019. [bibtex] 
  32. K. Khamaru, M. J. Wainwright, "Convergence guarantees for a class of non-convex and non-smooth optimization problems", Journal of Machine Learning Research, vol. 20, pp. 1–52, 2019. [bibtex] 
  33. R. Dwivedi, Y. Chen, M. J. Wainwright, B. Yu, "Log-concave sampling: Metropolis-Hastings algorithms are fast.", Journal of Machine Learning Research, vol. 20, no. 183, pp. 1–42, 2019. [bibtex] 
  34. A. Ramdas, J. Chen, M. J. Wainwright, M. I. Jordan, "DAGGER: A sequential algorithm for FDR control on DAGs", Biometrika, vol. 106, no. 1, pp. 69–86, March, 2019. [bibtex] 
  35. N. B. Shah, S. Balakrishnan, M. J. Wainwright, "Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons", IEEE Trans. Info. Theory, vol. 65, no. 8, pp. 4854–4874, August, 2019. [bibtex] 
  36. F. Yang, S. Balakrishnan, M. J. Wainwright, "Statistical and Computational Guarantees for the Baum-Welch Algorithm", Journal of Machine Learning Research, vol. 18, pp. 1–53, 2018. [bibtex] 
  37. Y. Chen, R. Dwivedi, M. J. Wainwright, B. Yu, "Fast MCMC Sampling Algorithms on Polytopes", Journal of Machine Learning Research, vol. 19, pp. 1–86, 2018. [bibtex] 
  38. A. Pananjady, M. J. Wainwright, T. A. Courtade, "Linear regression with shuffled data: Statistical and computational limits of permutation recovery", IEEE Transactions on Information Theory, vol. 64, no. 5, pp. 3286–3300, 2018. [bibtex] 
  39. H. Mania, A. Ramdas, M. J. Wainwright, M. I. Jordan and B. Recht, "On kernel methods for covariates that are rankings", Electronic Journal of Statistics, vol. 12, pp. 2537–2577, 2018. [bibtex] 
  40. N. B. Shah, M. J. Wainwright, "Simple, Robust and Optimal Ranking from Pairwise Comparisons", Journal of Machine Learning Research, vol. 18, pp. 1––18, 2018. [bibtex] 
  41. J. C. Duchi, M. I. Jordan, M. J. Wainwright, "Minimax optimal procedures for locally private estimation", Journal of the American Statistical Association, vol. 133, no. 521, pp. 182–215, June, 2018. [bibtex] 
  42. S. Van de Geer, M. J. Wainwright, "Concentration for (regularized) empirical risk minimization", Sankhya A, vol. 79, pp. 159–200, August, 2017. [bibtex] 
  43. M. Pilanci, M. J. Wainwright, "Newton Sketch: A Linear-time Optimization Algorithm with Linear-Quadratic Convergence", SIAM Jour. Opt., vol. 27, no. 1, pp. 205–245, March, 2017. [bibtex] 
  44. Y. Yang, M. Pilanci, M. J. Wainwright, "Randomized sketches for kernels: Fast and optimal non-parametric regression", Annals of Statistics, vol. 45, no. 3, pp. 991–1023, 2017. [bibtex] 
  45. S. Balakrishnan, M. J. Wainwright, B. Yu, "Statistical guarantees for the EM algorithm: From population to sample-based analysis", Annals of Statistics, vol. 45, no. 1, pp. 77––120, 2017. [bibtex] 
  46. P. Loh, M. J. Wainwright, "Support recovery without incoherence: A case for nonconvex regularization", Annals of Statistics, vol. 45, no. 6, pp. 2455–2482, 2017. [bibtex] 
  47. Y. Zhang, M. J. Wainwright, M. I. Jordan, "Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators", Elec. Jour. Statistics, vol. 11, pp. 752–799, 2017. [bibtex] 
  48. N. B. Shah, S. Balakrishnan, A. Guntuboyina, M. J. Wainwright, "Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues", IEEE Trans. Info. Theory, vol. 63, no. 2, pp. 934–959, February, 2017. [bibtex] 
  49. M. Pilanci, M. J. Wainwright, "Iterative Hessian Sketch: Fast and accurate solution approximation for constrained least-squares", Journal of Machine Learning Research, vol. 17, no. 53, pp. 1–38, April, 2016. [bibtex] 
  50. Y. Yang, M. J. Wainwright, M. I. Jordan, "On the computational complexity of high-dimensional Bayesian variable selection", Annals of Statistics, vol. 44, no. 6, pp. 2497–2532, 2016. [bibtex] 
  51. M. Chichignoud, J. Lederer, M. J. Wainwright, "A practical scheme and fast algorithm to tune the Lasso with optimality guarantees", Journal of Machine Learning Research, vol. 17, pp. 1–17, 2016. [bibtex] 
  52. N. B. Shah, S. Balakrishnan, J. Bradley, A. Parekh and K. Ramchandran, M. J. Wainwright, "Estimation from pairwise comparisons: Sharp minimax bounds with topology dependence", Journal of Machine Learning Research, vol. 17, no. 58, pp. 1–46, February, 2016. [bibtex] 
  53. M. Pilanci, M. J. Wainwright, "Randomized sketches of convex programs with sharp guarantees", IEEE Trans. Info. Theory, vol. 9, no. 61, pp. 5096–5115, September, 2015. [bibtex] 
  54. M. Pilanci, M. J. Wainwright, L. El Ghaoui, "Sparse learning via Boolean relaxations", Mathematical Programming, vol. 151, no. 1, pp. 63–87, June, 2015. [bibtex] 
  55. M. Pilanci, M. J. Wainwright, "Randomized sketches of convex programs with sharp guarantees", IEEE Trans. Info. Theory, vol. 9, no. 61, pp. 5096–5115, September, 2015. [bibtex] 
  56. G. Schiebinger, M. J. Wainwright, B. Yu, "The geometry of kernelized spectral clustering", Annals of Statistics, vol. 43, no. 2, pp. 819–846, 2015. [bibtex] 
  57. P. Loh, M. J. Wainwright, "Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima", Journal of Machine Learning Research, vol. 16, pp. 559–616, April, 2015. [bibtex] 
  58. Y. Zhang, J. C. Duchi, M. J. Wainwright, "Divide and Conquer Kernel Ridge Regression: A distributed algorithm with minimax optimal rates", Journal of Machine Learning Research, vol. 16, pp. 3299–3340, December, 2015. [bibtex] 
  59. J. C. Duchi, M. I. Jordan, M. J. Wainwright and A. Wibisono, "Optimal rates for zero-order optimization: the power of two function evaluations", IEEE Trans. Info. Theory, vol. 61, no. 5, pp. 2788–2806, 2015. [bibtex] 
  60. G. Raskutti, M. J. Wainwright, B. Yu, "Early stopping and non-parametric regression: An optimal data-dependent stopping rule", Journal of Machine Learning Research, vol. 15, pp. 335–366, 2014. [bibtex] 
  61. J. C. Duchi, M. J. Wainwright, M. I. Jordan, "Privacy-aware learning", Journal of the ACM, vol. 61, no. 6, pp. Article 37, November, 2014. [bibtex] 
  62. N. Noorshams, M. J. Wainwright, "Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees", Journal of Machine Learning Research, vol. 14, pp. 2799–2835, 2013. [www]  [bibtex] 
  63. N. Noorshams, M. J. Wainwright, "Stochastic belief propagation: A low-complexity alternative to the sum-product algorithm", IEEE Trans. Info. Theory, vol. 59, no. 4, pp. 1981–2000, April, 2013. [bibtex] 
  64. P. Loh, M. J. Wainwright, "Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses", Annals of Statistics, vol. 41, no. 6, pp. 3022–3049, December, 2013. [bibtex] 
  65. Y. Zhang, J. C. Duchi, M. J. Wainwright, "Communication-efficient algorithms for statistical optimization", Journal of Machine Learning Research, vol. 14, pp. 3321–3363, November, 2013. [bibtex] 
  66. M. J. Wainwright, "Discussion: Latent graphical model selection by convex optimization", Annals of Statistics, vol. 40, no. 4, pp. 1978–1983, 2012. [bibtex] 
  67. S. Negahban, M. J. Wainwright, "Restricted strong convexity and (weighted) matrix completion: Optimal bounds with noise", Journal of Machine Learning Research, vol. 13, pp. 1665–1697, May, 2012. [bibtex] 
  68. A. Agarwal, S. Negahban, M. J. Wainwright, "Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions", Annals of Statistics, vol. 40, no. 2, pp. 1171–1197, 2012. [bibtex] 
  69. A. Agarwal, S. Negahban, M. J. Wainwright, "Fast global convergence of gradient methods for high-dimensional statistical recovery", Annals of Statistics, vol. 40, no. 5, pp. 2452–2482, 2012. [bibtex] 
  70. G. Raskutti, M. J. Wainwright, B. Yu, "Minimax-optimal rates for sparse additive models over kernel classes via convex programming", Journal of Machine Learning Research, vol. 12, pp. 389–427, March, 2012. [bibtex] 
  71. J. C. Duchi, A. Agarwal, M. J. Wainwright, "Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling", IEEE Trans. Automatic Control, vol. 57, no. 3, pp. 592–606, March, 2012. [bibtex] 
  72. A. Agarwal, P. L. Bartlett, P. Ravikumar, M. J. Wainwright, "Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization", IEEE Trans. Info. Theory, vol. 58, no. 5, pp. 3235–3249, May, 2012. [bibtex] 
  73. S. Negahban, P. Ravikumar, M. J. Wainwright, B. Yu, "A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers", Statistical Science, vol. 27, no. 4, pp. 538–557, December, 2012. [bibtex] 
  74. N. P. Santhanam, M. J. Wainwright, "Information-theoretic limits of selecting binary graphical models in high dimensions", IEEE Trans. Info Theory, vol. 58, no. 7, pp. 4117–4134, May, 2012. [bibtex] 
  75. J. C. Duchi, P. L. Bartlett, M. J. Wainwright, "Randomized smoothing for stochastic optimization", SIAM Journal on Optimization, vol. 22, no. 2, pp. 674–701, 2012. [bibtex] 
  76. P. Loh, M. J. Wainwright, "High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity", Annals of Statistics, vol. 40, no. 3, pp. 1637–1664, September, 2012. [bibtex] 
  77. A. A. Amini, M. J. Wainwright, "Sampled forms of functional PCA in reproducing kernel Hilbert spaces", Annals of Statistics, vol. 40, no. 5, pp. 2483–2510, 2012. [bibtex] 
  78. N. Noorshams, M. J. Wainwright, "Non-asymptotic analysis of an optimal algorithm for network-constrained averaging with noisy links", IEEE Journal Selected Topics in Signal Processing, vol. 5, no. 4, pp. 833–844, August, 2011. [bibtex] 
  79. R. Rajagopal, M. J. Wainwright, "Network-based consensus with general noisy channels", IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 373–385, January, 2011. [bibtex] 
  80. G. Raskutti, M. J. Wainwright, B. Yu, "Minimax rates of estimation for high-dimensional linear regression over $\ell_q$-balls", IEEE Trans. Information Theory, vol. 57, no. 10, pp. 6976––6994, October, 2011. [bibtex] 
  81. P. Ravikumar, M. J. Wainwright, G. Raskutti, B. Yu, "High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence", Electronic Journal of Statistics, vol. 5, pp. 935–980, 2011. [bibtex] 
  82. S. Negahban, M. J. Wainwright, "Estimation of (near) low-rank matrices with noise and high-dimensional scaling", Annals of Statistics, vol. 39, no. 2, pp. 1069–1097, 2011. [bibtex] 
  83. G. Obozinski, M. J. Wainwright, M. I. Jordan, "Union support recovery in high-dimensional multivariate regression", Annals of Statistics, vol. 39, no. 1, pp. 1–47, January, 2011. [bibtex] 
  84. S. Negahban, M. J. Wainwright, "Simultaneous support recovery in high-dimensional regression: Benefits and perils of $\ell_1, \infty$-regularization", IEEE Trans. Info. Theory, vol. 57, no. 6, pp. 3481–3863, June, 2011. [bibtex] 
  85. M. J. Wainwright, E. Maneva, E. Martinian, "Lossy Source Compression using Low-Density Generator Matrix Codes: Analysis and Algorithms", IEEE Trans. Info. Theory, vol. 56, no. 3, pp. 1351–1368, March, 2010. [bibtex] 
  86. A. G. Dimakis, P. B. Godfrey, Y. Wu, M. J. Wainwright, K. Ramchandran, "Network coding for distributed storage systems", IEEE Trans. Info. Theory, vol. 56, no. 9, pp. 4539–4551, September, 2010. [bibtex] 
  87. G. Raskutti, M. J. Wainwright, B. Yu, "Restricted eigenvalue conditions for correlated Gaussian designs", Journal of Machine Learning Research, vol. 11, pp. 2241–2259, August, 2010. [bibtex] 
  88. P. Ravikumar, A. Agarwal, M. J. Wainwright, "Message-passing for graph-structured linear programs: Proximal projections, convergence and rounding schemes", Journal of Machine Learning Research, vol. 11, pp. 1043–1080, March, 2010. [bibtex] 
  89. W. Wang, M. J. Wainwright, K. Ramchandran, "Information-Theoretic Limits on Sparse Signal Recovery: Dense versus Sparse Measurement Matrices", IEEE Trans. Info Theory, vol. 56, no. 6, pp. 2967–2979, June, 2010. [bibtex] 
  90. P. Ravikumar, M. J. Wainwright, J. D. Lafferty, "High-dimensional Ising model selection using $\ell_1$-regularized logistic regression", Annals of Statistics, vol. 38, no. 3, pp. 1287–1319, 2010. [bibtex] 
  91. Z. Zhang, V. Anantharam, M. J. Wainwright, V. Anantharam, "An efficient 10GBASE-T Ethernet LDPC decoder design with low error floors", IEEE Jour. Solid-State Circuits, vol. 45, no. 4, pp. 843–855, March, 2010. [bibtex] 
  92. D. Omidiran, M. J. Wainwright, "High-dimensional Variable Selection with Sparse Random Projections: Measurement sparsity and statistical efficiency", Journal of Machine Learning Research, vol. 11, pp. 2361–2386, August, 2010. [bibtex] 
  93. M. J. Wainwright, "Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting", IEEE Trans. Info. Theory, vol. 55, pp. 5728–5741, December, 2009. [bibtex] 
  94. M. J. Wainwright, "Sharp thresholds for high-dimensional and noisy sparsity recovery using $\ell_1$-constrained quadratic programming (Lasso)", IEEE Trans. Info. Theory, vol. 55, pp. 2183–2202, May, 2009. [bibtex] 
  95. M. J. Wainwright, E. Martinian, "Low-density codes that are optimal for binning and coding with side information", IEEE Trans. Info. Theory, vol. 55, no. 3, pp. 1061–1079, March, 2009. [bibtex] 
  96. X. Nguyen, M. J. Wainwright, M. I. Jordan, "On surrogate losses and $f$-divergences", Annals of Statistics, vol. 37, no. 2, pp. 876–903, 2009. [bibtex] 
  97. A. G. Dimakis, A. A. Gohari, M. J. Wainwright, "Guessing facets: Polytope structure and improved LP decoding", IEEE Trans. Information Theory, vol. 55, no. 8, pp. 3479–3487, August, 2009. [bibtex] 
  98. L. Dolecek, P. Lee, Z. Zhang, V. Anantharam, B. Nikolic, M. J. Wainwright, "Predicting error floors of structured LDPC codes: Deterministic bounds and estimates", IEEE Jour. Sel. Areas. Comm, vol. 27, no. 6, pp. 908–917, August, 2009. [bibtex] 
  99. L. Dolecek, Z. Zhang, V. Anantharam, M. J. Wainwright, B. Nikolic, "Analysis of Absorbing Sets and Fully Absorbing Sets for Array-Based LDPC Codes", IEEE Trans. Info. Theory, vol. 56, no. 1, pp. 181–201, January, 2009. [bibtex] 
  100. Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam, M. J. Wainwright, B. Nikolic, "Design of LDPC Decoders for Low Bit Error Rate Performance: Quantization and Algorithm Choices", IEEE Trans. Communications, 2009. [bibtex]  (To appear) 
  101. A. A. Amini, M. J. Wainwright, "High-dimensional analysis of semdefinite relaxations for sparse principal component analysis", Annals of Statistics, vol. 5B, pp. 2877–2921, 2009. [bibtex] 
  102. M. J. Wainwright, M. I. Jordan, "Graphical models, exponential families and variational inference", Foundations and Trends in Machine Learning, vol. 1, pp. 1––305, December, 2008. [bibtex] 
  103. X. Nguyen, M. J. Wainwright, M. I. Jordan, "On optimal quantization rules for some sequential decision problems", IEEE Trans. Info. Theory, vol. 54, no. 7, pp. 3285–3295, July, 2008. [bibtex] 
  104. A. G. Dimakis, A. Sarwate, M. J. Wainwright, "Geographic gossip: Efficient averaging for sensor networks", IEEE Trans. Signal Processing, vol. 53, pp. 1205–1216, March, 2008. [bibtex] 
  105. C. Daskalakis, A. G. Dimakis, R. M. Karp, M. J. Wainwright, "Probabilistic analysis of linear programming decoding", IEEE Trans. Information Theory, vol. 54, no. 8, pp. 3565–3578, 2008. [bibtex] 
  106. T. G. Roosta, M. J. Wainwright, S. S. Sastry, "Convergence analysis of reweighted sum-product algorithms", IEEE Trans. Signal Processing, vol. 56, no. 9, pp. 4293–4305, September, 2008. [bibtex] 
  107. M. J. Wainwright, "Sparse graph codes for side information and binning", IEEE Signal Processing Magazine, vol. 24, no. 5, pp. 47–57, September, 2007. [bibtex] 
  108. E. Maneva, E. Mossel, M. J. Wainwright, "A new look at survey propagation and its generalizations", Journal of the ACM, vol. 54, no. 4, pp. 2–41, 2007. [bibtex] 
  109. J. Feldman, T. Malkin, R. A. Servedio, C. Stein, M. J. Wainwright, "LP Decoding Corrects a Constant Fraction of Errors", IEEE Trans. Information Theory, vol. 53, no. 1, pp. 82–89, January, 2007. [bibtex] 
  110. M. J. Wainwright, "Estimating the ``wrong'' graphical model: Benefits in the computation-limited regime", Journal of Machine Learning Research, vol. 7, pp. 1829–1859, September, 2006. [bibtex] 
  111. M. J. Wainwright, M. I. Jordan, "Log-determinant relaxation for approximate inference in discrete Markov random fields", IEEE Trans. Signal Processing, vol. 54, no. 6, pp. 2099–2109, June, 2006. [bibtex] 
  112. M. Cetin, L. Chen, J. W. Fisher, A. T. Ihler, R. L. Moses, M. J. Wainwright, A. S. Willsky, "Distributed fusion in sensor networks", IEEE Signal Processing Magazine, vol. 23, pp. 42–55, July, 2006. [bibtex] 
  113. M. J. Wainwright, T. S. Jaakkola, A. S. Willsky, "Exact MAP estimates via agreement on (hyper)trees: Linear programming and message-passing", IEEE Trans. Information Theory, vol. 51, no. 11, pp. 3697–3717, November, 2005. [bibtex] 
  114. M. J. Wainwright, T. S. Jaakkola and A. S. Willsky, "A new class of upper bounds on the log partition function", IEEE Trans. Info. Theory, vol. 51, no. 7, pp. 2313–2335, July, 2005. [bibtex] 
  115. X. Nguyen, M. J. Wainwright, M. I. Jordan, "Nonparametric decentralized detection using kernel methods", IEEE Trans. Signal Processing, vol. 53, no. 11, pp. 4053–4066, November, 2005. [bibtex] 
  116. J. Feldman, M. J. Wainwright, D. R. Karger, "Using linear programming to decode binary linear codes", IEEE Trans. Info. Theory, vol. 51, pp. 954–972, March, 2005. [bibtex] 
  117. M. J. Wainwright, T. S. Jaakkola and A. S. Willsky, "Tree consistency and bounds on the max-product algorithm and its generalizations", Statistics and Computing, vol. 14, pp. 143–166, April, 2004. [bibtex] 
  118. E. Sudderth, M. J. Wainwright, A. S. Willsky, "Embedded trees: Estimation of Gaussian processes on graphs with cycles", IEEE Trans. Signal Processing, vol. 52, no. 11, pp. 3136–3150, 2004. [bibtex] 
  119. M. J. Wainwright, T. S. Jaakkola and A. S. Willsky, "Tree-based reparameterization framework for analysis of sum-product and related algorithms", IEEE Trans. Info. Theory, vol. 49, no. 5, pp. 1120–1146, May, 2003. [bibtex] 
  120. J. Portilla, V. Strela, M. J. Wainwright, E. P. Simoncelli, "Image denoising using scale mixtures of Gaussians in the wavelet domain", IEEE Trans. Image Processing, vol. 12, pp. 1338–1351, 2003. [bibtex] 
  121. M. J. Wainwright, E. P. Simoncelli, A. S. Willsky, "Random cascades on wavelet trees and their use in modeling and analyzing natural images", Applied Computational and Harmonic Analysis, vol. 11, pp. 89–123, 2001. [bibtex] 
  122. M. J. Wainwright, "Visual adaptation as optimal information transmission", Vision Research, vol. 39, pp. 3960–3974, 1999. [bibtex] 

Conference Articles

  1. E. Xia, M. J. Wainwright, "Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces", Conference on Artificial Intellgence and Statistics, vol. 206, pp. 9137–9166, April, 2023. [pdf]  [bibtex] 
  2. R. Pathak, C. Ma, M. J. Wainwright, "A new similarity measure for covariate shift with applications to nonparametric regression", International Conference on Machine Learning, July, 2022. [bibtex] 
  3. A. Zanette, M. J. Wainwright, "Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning", International Conference on Machine Learning, July, 2022. [bibtex] 
  4. W. Mou, A. Pananjady, M. J. Wainwright, P. L. Bartlett, "Optimal and instance-dependent guarantees for Markovian linear stochastic approximation", Conference on Learning Theory, July, 2022. [bibtex] 
  5. A. Zanette, M. J. Wainwright, "Bellman Residual Orthogonalization for Offline Reinforcement Learning", Neural Information Processing Systems, December, 2022. [bibtex]  (Long version posted as arxiv:2203.12786) 
  6. C. J. Li, W. Mou, M. J. Wainwright, M. I. Jordan, "ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm", Conference on Learning Theory, July, 2022. [bibtex] 
  7. A. Zanette, M. J. Wainwright, E. Brunskill, "Provable benefits of actor-critic methods in offline reinforcement learning", Neural Information Processing Systems, December, 2021. [bibtex]  [arxiv]  (arXiv:2108.08812) 
  8. R. Dwivedi, K. Khamaru, N. Ho, M. J. Wainwright, M. I. Jordan, B. Yu, "Sharp analysis of expectation-maximization for weakly identifiable models", AISTATS, 2021. [bibtex] 
  9. R. Pathak, M. J. Wainwright, "FedSplit: An algorithmic framework for fast federated optimization", NeurIPS (Neural Information Processing Systems), December, 2020. [bibtex] 
  10. W. Mou, C. J. Li, M. J. Wainwright, P. L. Bartlett, M. I. Jordan, "On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration", Conference on Learning Theory (COLT), vol. 125, pp. 2947–2997, 2020. [bibtex] 
  11. K. Bhatia, A. Pananjady, P. L. Bartlett, A. D. Dragan, M. J. Wainwright, "Preference learning along multiple criteria: A game-theoretic perspective", Neural Information Processing Systems, 2020. [bibtex] 
  12. J. Chen, M. I. Jordan, M. J. Wainwright, "HopSkipJump Attack: A query-efficient decision-based attack", IEEE Conference on Security and Privacy, October, 2019. [bibtex] 
  13. D. Malik, A. Pananjady, K. Bhatia, K. Khamaru, P. L. Bartlett, M. J. Wainwright, "Derivative-free methods for policy optimization: Guarantees for linear-quadratic systems", AISTATS: Conference on AI and Statistics, 2019. [bibtex] 
  14. J. Chen, L. Song, M. J. Wainwright, M. I. Jordan, "L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data", International Conference on Learning Representations, May, 2019. [bibtex] 
  15. R. Heckel, M. Simchowitz, K. Ramchandran, M. J. Wainwright, "Approximate ranking from pairwise comparisons", AISTATS: Conference on AI and Statistics, vol. 84, pp. 1057–1066, 2018. [bibtex] 
  16. J. Chen, L. Song, M. J. Wainwright, M. I. Jordan, "Learning to Explain: An Information-Theoretic Perspective on Model Interpretation", ICML: International Conference on Machine Learning, 2018. [bibtex] 
  17. R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan, "Theoretical guarantees for the EM algorithm when applied to misspecified Gaussian mixture models", NeurIPS Conference, December, 2018. [bibtex] 
  18. C. Mao, A. Pananjady, M. J. Wainwright, "Breaking the $1/\sqrtn$-barrier: Faster rates for permutation-based models in polynomial time", Conference on Learning Theory (COLT), no. 75, pp. 2037––2042, July, 2018. [pdf]  [bibtex] 
  19. K. Khamaru, M. J. Wainwright, "Convergence guarantees for a class of non-convex and non-smooth optimization problems", ICML: International Conference on Machine Learning, 2018. [bibtex] 
  20. R. Dwivedi, Y. Chen, M. J. Wainwright, B. Yu, "Log-concave sampling: Metropolis-Hastings algorithms are fast.", COLT: Conference on Computational Learning Theory, 2018. [bibtex] 
  21. F. Yang, Y. Wei, M. J. Wainwright, "Early stopping for kernel boosting algorithms: A general analysis with localized complexities", NeurIPS (Neural Information Processing Systems), 2017. [bibtex] 
  22. J. Chen, M. Stern, M. J. Wainwright, M. I. Jordan, "Kernel Feature Selection via Conditional Covariance Minimization", NeurIPS: Advances in Neural Information Processing Systems, pp. 6949–6958, 2017. [bibtex] 
  23. A. Ramdas, J. Chen, M. J. Wainwright, M. I. Jordan, "QuTE: Decentralized multiple testing on sensor networks with false discovery rate control", 56th IEEE Conference on Decision and Control (CDC), 12, 2017. [bibtex] 
  24. A. Ramdas, F. Yang, M. J. Wainwright, M. I. Jordan, "Online control of false discovery rate with decaying memory", Neural Information Processing Systems, December, 2017. [bibtex] 
  25. F. Yang, A. Ramdas, K. Jamieson, M. J. Wainwright, "A framework for multi-armed bandit testing with online FDR control", Neural Information Processing Systems, December, 2017. [bibtex] 
  26. Y. Zhang, J. Lee, M. J. Wainwright, M. I. Jordan, "On the learnability of fully-connected neural networks", AISTATS, April, 2017. [bibtex] 
  27. A. Pananjady, M. J. Wainwright, T. Courtade, "Denoising linear models with permuted data", ISIT: IEEE International Symposium on Information Theory, 2017. [bibtex] 
  28. Y. Zhang, P. Liang, M. J. Wainwright, "Convexified Convolutional Neural Networks", ICML: 34th International Conference on Machine Learning, vol. 70, pp. 4044–4053, August, 2017. [pdf]  [bibtex] 
  29. Y. Wei, M. J. Wainwright, "Sharp minimax rates for testing monotone distributions", International Symposium on Information Theory, July, 2016. [bibtex] 
  30. C. Jin, S. Balakrishnan, M. J. Wainwright, M. I. Jordan, "Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences", NeurIPS Conference, December, 2016. [bibtex] 
  31. A. El Alaoui, X. Cheng, A. Ramdas, M. J. Wainwright, M. I. Jordan, "Asymptotic behavior of $\ell_p$-based Laplacian regularization in semi-supervised learning", COLT: Conference on Learning Theory, June, 2016. [bibtex] 
  32. C. Jin, S. Balakrishnan, M. J. Wainwright, M. I. Jordan, "Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences", NeurIPS Conference, December, 2016. [bibtex] 
  33. M. J. Wainwright, "Structured regularizers for high-dimensional problems: Statistical and computational issues", Annual Review of Statistics and its Applications, vol. 1, pp. 233–253, January, 2014. [bibtex] 
  34. M. J. Wainwright, "Constrained forms of statistical minimax: Computation, communication and privacy", Proceedings of the International Congress of Mathematicians, 2014. [bibtex] 
  35. Y. Zhang, M. J. Wainwright, M. I. Jordan, "Lower bounds on the performance of polynomial-time algorithms for sparse linear regression", Conference on Computational Learning Theory, June, 2014. [bibtex] 
  36. J. C. Duchi, M. J. Wainwright, M. I. Jordan, "Local privacy and statistical minimax rates", Foundations of Computer Science (FOCS) Conference, 2014. [bibtex] 
  37. Y. Zhang, J. C. Duchi, and M. I. Jordan, M. J. Wainwright, "Information-theoretic lower bounds for distributed statistical estimation with communication constraints", NeurIPS: Neural Information Processing Systems Conference, 2013. [bibtex] 
  38. Y. Zhang, J. C. Duchi, M. J. Wainwright, "Divide and Conquer Kernel Ridge Regression", Computational Learning Theory (COLT) Conference, July, 2013. [bibtex] 
  39. P. Loh, M. J. Wainwright, "No voodoo here! Learning discrete graphical models via inverse covariance estimation", Neural Information Processing Systems (NeurIPS), December, 2012. [bibtex] 
  40. J. C. Duchi, M. J. Wainwright, M. I. Jordan, "Privacy-aware learning", Neural Information Processing Systems (NeurIPS), December, 2012. [bibtex] 
  41. J. C. Duchi, A. Wibisono, M. J. Wainwright and M. I. Jordan, "Finite sample convergence rates of zero-order stochastic optimization methods", Neural Information Processing Systems (NeurIPS), December, 2012. [bibtex] 
  42. Y. Zhang, J. C. Duchi, M. J. Wainwright, "Communication-efficient algorithms for statistical optimization", Neural Information Processing Systems (NeurIPS), December, 2012. [bibtex] 
  43. A. Agarwal, S. Negahban, M. J. Wainwright, "Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions", Neural Information Processing Systems (NeurIPS), December, 2012. [bibtex] 
  44. P. Loh, M. J. Wainwright, "High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity", NeurIPS Conference, December, 2011. [bibtex] 
  45. S. Negahban, M. J. Wainwright, "Estimation of (near) low-rank matrices with noise and high-dimensional scaling", Proceedings of the ICML Conference, June, 2010. [bibtex] 
  46. W. Wang, M. J. Wainwright, K. Ramchandran, "Information-theoretic bounds on model selection for Gaussian Markov random fields", IEEE International Symposium on Information Theory, 2010. [bibtex] 
  47. J. Duchi, A. Agarwal, M. J. Wainwright, "Distributed dual averaging in networks", NeurIPS Conference, December, 2009. [bibtex] 
  48. N. P. Santhanam, M. J. Wainwright, "Information-theoretic limits of high-dimensional model selection", International Symposium on Information Theory, July, 2008. [bibtex] 
  49. S. Negahban, M. J. Wainwright, "Benefits and perils of block regularization in high dimensions", Neural Information Processing Systems (NeurIPS), December, 2008. [bibtex] 
  50. P. Lee, L. Dolecek, Z. Zhang, V. Anantharam, B. Nikolic, M. J. Wainwright, "Error Floors in LDPC Codes: Fast Simulation, Bounds and Hardware Emulation", IEEE Int. Symp. Info. Theory, July, 2008. [bibtex] 
  51. Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam and M. J. Wainwright, "Lowering LDPC error floors by post-processing", Proc. IEEE GLOBECOM, September, 2008. [bibtex] 
  52. E. B. Sudderth, M. J. Wainwright, A. S. Willsky, "Loop series and Bethe variational bounds for attractive graphical models", NeurIPS 21, 2007. [bibtex] 
  53. C. Daskalakis, A. G. Dimakis, R. M. Karp, M. J. Wainwright, "Probabilistic Analysis of Linear Programming Decoding", Proceedings of the 18th Annual Symposium on Discrete Algorithms (SODA), January, 2007. [bibtex] 
  54. L. Dolecek, Z. Zhang, V. Anantharam, M. J. Wainwright, B. Nikolic, "Analysis of absorbing sets for array-based LDPC codes", IEEE Int. Conf. Communications (ICC), June, 2007. [bibtex] 
  55. Z. Zhang, L. Dolecek, V. Anantharam, M. J. Wainwright, B. Nikolic, "Quantization effects in low-density parity-check decoders", IEEE Int. Conf. Communications (ICC), pp. 6321–6237, June, 2007. [bibtex] 
  56. L. Dolecek, Z. Zhang, M. J. Wainwright, V. Anantharam, M. J. Wainwright, "Evaluation of the low frame error rate performance of LDPC codes using importance sampling", Information Theory Workshop (ITW), September, 2007. [bibtex] 
  57. M. J. Wainwright, P. Ravikumar, J. D. Lafferty, "High-dimensional graph selection using $\ell_1$-regularized logistic regression", NeurIPS Conference, December, 2006. [bibtex] 
  58. X. Nguyen, M. J. Wainwright, M. I. Jordan, "On optimal quantization rules for some sequential decision problems", International Symposium on Information Theory, July, 2006. [bibtex]  (Available at arxiv:math.ST/0608556) 
  59. A. G. Dimakis, A. Sarwate, M. J. Wainwright, "Geographic Gossip: Efficient aggregation in sensor networks", Information Processing in Sensor Networks, March, 2006. [bibtex] 
  60. R. Rajagopal, M. J. Wainwright, P. Varaiya, "Universal quantile estimation with feedback in the communication-constrained setting", International Symposium on Information Theory, July, 2006. [bibtex] 
  61. A. G. Dimakis, M. J. Wainwright, "Guessing Facets: Improved LP decoding and Polytope Structure", International Symposium on Information Theory, July, 2006. [bibtex] 
  62. Z. Zhang, L. Dolecek, B. Nikolic, V. Anantharam, M. J. Wainwright, "Investigation of error floors of structured low-density parity check codes by hardware emulation", Proceedings of IEEE Globecom, November, 2006. [bibtex] 
  63. E. Martinian, M. J. Wainwright, "Low density codes achieve the rate-distortion bound", Data Compression Conference, vol. 1, pp. 153–162, March, 2006. [bibtex]  (Available at arxiv:cs.IT/061123) 
  64. E. Martinian, M. J. Wainwright, "Analysis of LDGM and compound codes for lossy compression and binning", Workshop on Information Theory and Applications (ITA), pp. 229–233, February, 2006. [bibtex]  (Available at arxiv:cs.IT/0602046) 
  65. E. Martinian, M. J. Wainwright, "Low density codes can achieve the Wyner-Ziv and Gelfand-Pinsker bounds", International Symposium on Information Theory, pp. 484–488, July, 2006. [bibtex]  (Available at arxiv:cs.IT/0605091) 
  66. M. J. Wainwright, E. Maneva, "Lossy source coding by message-passing and decimation over generalized codewords of LDGM codes", International Symposium on Information Theory, September, 2005. [bibtex]  (Available at arxiv:cs.IT/0508068) 
  67. E. Maneva, E. Mossel, M. J. Wainwright, "A New Look at Survey Propagation and its Generalizations", Proceedings of the 16th Annual Symposium on Discrete Algorithms (SODA), pp. 1089–1098, 2005. [bibtex] 
  68. V. Kolmogorov, M. J. Wainwright, "On optimality properties of tree-reweighted message-passing", Uncertainty in Artificial Intelligence, July, 2005. [bibtex] 
  69. X. Nguyen, M. J. Wainwright, M. I. Jordan, "Divergence measures, surrogate loss functions and experimental design", Advances in Neural Information Processing Systems, 2005. [bibtex] 
  70. M. J. Wainwright, M. I. Jordan, "Variational inference in graphical models: The view from the marginal polytope", Proceedings of the Allerton Conference on Communication, Control and Computing, October, 2003. [bibtex] 
  71. L. Chen, M. J. Wainwright, M. Cetin, A. Willsky, "Multitarget-multisensor data association using the tree-reweighted \ max-product algorithm", SPIE Aerosense Conference, April, 2003. [bibtex] 
  72. J. Feldman, D. R. Karger, M. J. Wainwright, "Using linear programming to decode LDPC codes", Conference on Information Science and Systems, March, 2003. [bibtex] 
  73. M. J. Wainwright, T. S. Jaakkola, A. S. Willsky, "Tree-based reparameterization for approximate inference on loopy graphs", NeurIPS 14, 2002. [bibtex] 
  74. M. J. Wainwright, T. S. Jaakkola, A. S. Willsky, "A new class of upper bounds on the log partition function", Uncertainty in Artificial Intelligence, vol. 18, August, 2002. [bibtex] 
  75. M. J. Wainwright, T. S. Jaakkola, A. S. Willsky, "Exact MAP estimates by (hyper)tree agreement", NeurIPS, vol. 15, December, 2002. [bibtex] 
  76. M. J. Wainwright, E. B. Sudderth, A. S. Willsky, "Tree-based modeling and estimation of Gaussian processes on graphs with cycles", NeurIPS 13, pp. 661–667, 2001. [bibtex] 
  77. J. Portilla, V. Strela, E. Simoncelli, M. J. Wainwright, "Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain", IEEE Int. Conf. Image Proc., September, 2001. [bibtex] 
  78. M. J. Wainwright, E. P. Simoncelli, "Scale mixtures of Gaussians and the statistics of natural images", Neural Information Processing Systems 12, vol. 12, pp. 855–861, December, 1999. [bibtex] 
  79. M. J. Wainwright, E. P. Simoncelli, "Explaining adaptation in V1 neurons with a statistically-optimized normalization model", Invest. Opthamology and Visual Science (Supplement), pp. 3017, 1999. [bibtex] 

Research Reports

  1. Y. Yan, M. J. Wainwright, "Entrywise Inference for Causal Panel Data: A Simple and Instance-Optimal Approach", Tech. Report, arXiv: 2401.1366, January, 2024. [pdf]  [www]  [bibtex] 
  2. Y. Duan, M. J. Wainwright, "Taming data-hungry reinforcement learning? Stability in continuous state-action spaces", Tech. Report, 2401.05233, January, 2024. [pdf]  [www]  [bibtex] 
  3. J. Cai, R. Chen, M. J. Wainwright, L. Zhao, "Doubly high-dimensional contextual bandits: An interpretable model for joint assortment-pricing", Tech. Report, 1, September, 2023. [www]  [bibtex] 
  4. F. Su, W. Mou, P. Ding, M. J. Wainwright, "A decorrelation method for general regression adjustment in randomized experiments", Tech. Report, arXiv:2311.10076, November, 2023. [bibtex] 
  5. F. Su, W. Mou, P. Ding, M. J. Wainwright, "When is the estimated propensity score better? High-dimensional analysis and bias correction", Tech. Report, 2303.17102, March, 2023. [bibtex] 
  6. M. Celentano, M. J. Wainwright, "Challenges of the inconsistency regime: Novel debiasing methods for missing data models", Tech. Report, arXiv:2309.01362, September, 2023. [bibtex] 
  7. L. Lin, K. Khamaru, M. J. Wainwright, "Semi-parametric inference based on adaptively collected data", Tech. Report, arXiv:2303.02534, March, 2023. [bibtex] 
  8. W. Mou, P. Ding, P. L. Bartlett, M. J. Wainwright, "Kernel-based off-policy estimation with overlap: Instance-optimality beyond semi-parametric efficiency", Tech. Report, 1, January, 2023. [bibtex] 
  9. R. Pathak, M. J. Wainwright, L. Xiao, "Noisy recovery from random linear observations: Sharp minimax rates under elliptical constraints", Tech. Report, arxiv:2303.12613, March, 2023. [pdf]  [bibtex] 
  10. W. Mou, M. J. Wainwright, P. L. Bartlett, "Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiency", Tech. Report, 2209.13075, September, 2022. [bibtex]  (arxiv:2209.13075) 
  11. Y. Duan, M. J. Wainwright, "Policy evaluation from a single path: Multi-step methods, mixing and mis-specification", Tech. Report, arXiv:2211.03899, November, 2022. [bibtex] 
  12. W. Mou, K. Khamaru, M. J. Wainwright, P. L. Bartlett, M. I. Jordan, "Optimal variance-reduced stochastic approximation in Banach spaces", Tech. Report, 1, January, 2022. [bibtex] 
  13. K. Khamaru, Y. Deshpande, L. Mackey, M. J. Wainwright, "Near-optimal inference in adaptive linear regression", Tech. Report, 2107.02266, July, 2021. [bibtex] 
  14. M. Rabinovich, M. I. Jordan, M. J. Wainwright, "Lower bounds in multiple testing: A framework based on derandomized proxies", Tech. Report, 2005.03725, May, 2020. [bibtex] 
  15. M. J. Wainwright, "Stochastic approximation with cone-contractive operators: Sharp $\ell_\infty$-bounds for Q-learning", Tech. Report, arxiv:1905.06265, May, 2019. [bibtex] 
  16. M. J. Wainwright, "Variance-reduced $Q$-learning is minimax optimal", Tech. Report, arxiv:1906.04697, June, 2019. [bibtex] 
  17. W. Mou, N. Ho, M. J. Wainwright, P. Bartlett, M. I. Jordan, "Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing", Tech. Report, 1, December, 2019. [bibtex] 
  18. R. Dwivedi, N. Ho, K. Khamaru, M. J. Wainwright, M. I. Jordan, B. Yu, "Challenges with EM in application to weakly identifiable mixture models", Tech. Report, arXiv:1902.00194, January, 2019. [bibtex] 
  19. N. B. Shah, S. Balakrishnan, M. J. Wainwright, "Low permutation-rank matrices: Structural properties and noisy completion", Tech. Report, 1, September, 2017. [bibtex] 
  20. Y. Chen, M. J. Wainwright, "Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees", Tech. Report, arxiv:1509.03025, September, 2015. [bibtex] 
  21. Y. Zhang, J. Lee, M. J. Wainwright, M. I. Jordan, "Learning halfspaces and neural networks with random initialization", Tech. Report, arXiv:1511.07948, November, 2015. [bibtex] 
  22. J. C. Duchi, M. I. Jordan, M. J. Wainwright, Y. Zhang, "Optimality guarantees for distributed statistical estimation", Tech. Report, 1, June, 2014. [bibtex] 
  23. J. C. Duchi, M. J. Wainwright, "Distance-based and continuum Fano inequalities with applications to statistical estimation", Tech. Report, arXiv:1311.2669, 2013. [bibtex] 
  24. M. J. Wainwright, M. I. Jordan, "Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations", Tech. Report, 1, September, 2004. [bibtex] 

Theses

  1. M. J. Wainwright, "Stochastic processes on graphs with cycles: geometric and variational approaches", PhD thesis, MIT, January, 2002. [bibtex] 

Miscellaneous

  1. M. J. Wainwright, "Graphical models and message-passing algorithms: some introductory lectures", Mathematical foundations of complex networked information system, vol. 2141, 2015. [bibtex] 
  2. M. J. Wainwright, M. I. Jordan, "A variational principle for graphical models", New Directions in Statistical Signal Processing, October, 2006. [bibtex] 
  3. M. J. Wainwright, O. Schwartz, E. P. Simoncelli, "Natural image statistics and divisive normalization: Modeling nonlinearities and adaptation in cortical neurons", Statistical Theories of the Brain, 2002. [bibtex]