Papers

Preprints

1. N.S.Pillai, A. Smith, A. Zaman. No Free Lunch for Stochastic Gradient Langevin Dynamics. [arXiv]
2. N.S. Pillai and A. Smith. Ergodicity of approximate MCMC chains with applications to large data sets. [arxiv]
3. Badrinarayanan S., Osoba O., Cheng M., Rogers R., Jain S.,Tandra R., N. S. Pill…. Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S. [arXiv]
4. A. Gupta, et. al. AlphaPO -- Reward shape matters for LLM alignment. [arXiv]
5. T.S. Liu, X.L. Meng and N.S.Pillai. A Heavily Right Strategy for Integrating Dependent Studies in Any Dimension. [arXiv]
6. M. Rischard., P. E. Jacob. Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation. [arXiv]

Publications

1. N.S. Pillai. Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions. Journal of Machine Learning Research, 2024. [arXiv]
2. B. Hsu, C. DiCiccio, N.S. Pillai, H. Namkoong. From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems. Algorithmic Fairness through the Lens of Metrics and Evaluation, Neurips 2024. [arXiv]
3. Zhao D., N. S. Pillai. Policy Gradients for Optimal Parallel Tempering MCMC. ICML 2024. [Workshop on Structured Probabilistic Inference & Generative Modeling]
4. T. Liu, P. Ghosal, K. Balasubramanian, N.S. Pillai. Towards Understanding the Dynamics of Gaussian--Stein Variational Gradient Descent. Neurips 2023. [arXiv]
5. A. Davis, Y. Marzouk, A. Smith and N. Pillai. Rate-optimal refinement strategies for local approximation MCMC. Statistics and Computing, 32 (4), 2022. [Stat. and Comp.]
6. V. Jain, N.S. Pillai, A. Sah, M. Sawhney and A. Smith. Fast and memory-optimal dimension reduction using Kac’s walk. The Annals of Applied Probability, 32 (5): 4038-4064, 2022. [AOAP]
7. Chaudhuri R., Jain V. and Pillai N.S. Universality and least singular values of random matrix products: A simplified approach. Bernoulli, 27 (4), 2519-2531, 2021. [Bernoulli]
8. Dai C., Chan D., Huybers P. and Pillai N.S. Late 19th-Century Navigational Uncertainties and Their Influence on Sea Surface Temperature Estimates. The Annals of Applied Statistics, 15 (1), 22-40, 2021. [AOAS]
9. N. Ho, Feller A., Greif E., Miratrix L. and Pillai N . Weak Separation in Mixture Models and Implications for Principal Stratification. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, 151, 5416-5458, 2022. [AIstats]
10. Mangoubi O., Pillai N.S. and Smith A. Simple conditions for metastability of continuous markov chains. Journal of Applied Probability, 58 (1), 83-105, 2021. [JAP]
11. Rigden A.J., Mueller N.D., Holbrook N.M., Pillai N.S. and Huybers P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nature (food), 1, 127–133, 2020. [Nature]
12. Bojinov I., Pillai N.S. and Rubin D.B. Diagnosing missing always at random in multivariate data. Biometrika, 107 (1), 246-253, 2020. [Biometrika]
13. Walker S. E., et. al. A Statistical Method for Associating Earthquakes with Their Source Faults in Southern California . Bulletin of the Seismological Society of America, 110 (1), 213–225, 2020. [Bulletin of SSA]
14. Jagadeesan R., Pillai N.S. and Volfovsky A. Designs for estimating the treatment effects in networks with interference. The Annals of Statistics, 48 (2), 679-712, 2020. [AOS]
15. Ottobre M., Pillai N.S. and Spiliopoulos K. Optimal scaling of MALA algorithm with irreversible proposals for Gaussian targets. Stochastics and Partial Differential Equations: Analysis and Computation, 8, 311–361, 2020. [SPDE]
16. Pillai N.S. and Smith A. Mixing times for a constrained Ising process on the two-dimensional torus at low density. Annales de l’Institut Henri Poincare (B) Probabilites and Statistique,  55(3), 1649-1678, 2019. [IHP]
17. Johndrow J.E., Pillai N.S., Smith A. and Dunson D.B. MCMC for imbalanced categorical data. Journal of the American Statistical Association, 114 (527), 1394-1403, 2019. [JASA]
18. Pillai N.S. and Smith A. On the Mixing Time of Kac’s Walk and Other High Dimensional Gibbs Samplers with Constraints. The Annals of Probability, 46 (4), 2345-2399, 2018. [AOP]
19. Beskos A., Roberts G., Thiery A.H. and Pillai N.S. Asymptotic analysis of the random walk Metropolis algorithm on ridged densities. Annals of Applied Probability, 28 (5), 2966-3001, 2018. [AOAP]
20. Conrad P., Davis A., Marzouk M., Pillai N.S. and Smith A. Parallel local approximation MCMC for expensive models . SIAM Journal of Uncertainty Quantification,  6(1), 339–373, 2018. [SIAM Journal of UQ]
21. Pillai N.S. and Smith A. Elementary bounds on mixing times for decomposable chains. Stochastic Processes and Applications, 127 (9), 3068-3109, 2017. [SPA]
22. Pillai N.S. and Smith A. Kac’s walk on n-sphere mixes in nlog(n) steps. Annals of Applied Probability, 27 (1), 631-650, 2017. [AOAP]
23. Bhattacharya A., Dunson D.B., Pati D. and Pillai N.S. Sub-optimality of some continuous shrinkage priors. Stochastic Processes and Applications: Special issue in Memoriam Prof. Evarist Gine, 126 (12), 3828-3842, 2016. [SPA]
24. Basse G., Pillai N.S. and Smith A. Parallel Markov Chain Monte Carlo via Spectral Clustering. AI-Stats, Accepted for Oral Presentation, Proceedings of the 19th International Conference on Artificial Intelligence, 1318-1327, 2016. [AISTATS]
25. Lee J.J., Forastiere L., Miratrix L. and Pillai N.S. More Powerful Multiple Testing in Randomized Experiments with Non-Compliance. Statistica Sinica, 27 (3), 1319-1345, 2017. [Stat. Sinica]
26. Lysy M., Pillai N.S. et. al. Model comparison for single particle tracking in biological fluids . Journal of the American Statistical Association: Applications and Case Studies, 111 (516), 1413-1426, 2016. [JASA]
27. Mellnik W.R. et. al . Single particle passive microrheology in biological fluids with drift. Journal of Rheology, 60 (3), 379-392, 2016. [Journal of Rheology]
28. Bornn L., Pillai N.S., Smith A. and Woodard D. The use of a single pseudo-sample in approximate Bayes computation. Statistics and Computing, 27 (3), 583-590, 2017. [Stat. and Comp.]
29. Pillai N.S. and Smith A. Mixing times for a constrained Ising process on the torus at low density. The Annals of Probability, 45 (2), 1003-1070, 2017. [AOP]
30. McGoff K., Mukherjee S. and Pillai N.S. Statistical Inference for dynamical systems: a review . Statistics Surveys, 9, 209-252, 2015. [Stat. Surveys]
31. Pillai N.S. and Meng, X-L. An unexpected encounter with Cauchy and Levy. The Annals of Statistics 44 (5), 2089-97, 2017. [AOS]
32. Conrad P.R., Marzouk Y.M., Pillai N.S. and Smith A. Asymptotically exact MCMC algorithms via local approximations for computationally intensive models. Journal of the American Statistical Association, 111, 1591-1607, 2017. [JASA]
33. Si Y., Pillai N.S. and Gelman A. Bayesian nonparametric weighted sampling inference. Bayesian Analysis, 10 (3), 605-625, 2015. [Bayesian Analysis]
34. Mukherjee R., Pillai N.S. and Lin X. Hypothesis testing for sparse binary regression. The Annals of Statistics, 43 (1), 352-381, 2015. [AOS]
35. Bhattacharya A., Pati D., Pillai N.S. and Dunson, D.B. Dirichlet-Laplace priors for optimal shrinkage. Journal of the American Statistical Association, 110 (52), 1479-1490, 2015. This paper contains some ideas from an older paper titled Bayesian Shrinkage. Also see here. [JASA]
36. McGoff K., Mukherjee S., Nobel A. and Pillai N.S. Consistency of maximum likelihood estimation for some dynamical systems. The Annals of Statistics, 43 (1), 2014, 1-29, 2015. [AOS]
37. Dasgupta, T., Pillai N.S. and Rubin D.B. Causal inference from 2^k factorial designs using the potential outcomes. Journal of the Royal Statistical Society, Series-B, 77 (4), 727-753, 2015. [JRSSB]
38. Pillai N.S., Stuart A.M. and Thiéry A.H. Noisy gradient flow from a random walk in Hilbert space. Stochastic Partial Differential Equations and Applications, 2, 196–232, 2014. [SPDE]
39. Cervone D., Pillai N.S., Pati D., Berbeco R. and Lewis J.H.. A location-mixture autoregressive model for online prediction of lung tumors. The Annals of Applied Statistics, 8 (3), 1341-1371, 2014. [AOAS]
40. Ottobre M., Pillai N.S., Pinski F.J. and Stuart, A.M. A Function Space HMC algorithm with second order Langevin diffusion limit. Bernoulli, 22 (1), 60-106, 2016. [Bernoulli]
41. Pati D., Bhattacharya A., Pillai N.S. and Dunson, D.B. Posterior contraction in sparse Bayesian factor models for massive covariance matrices. The Annals of Statistics, 42 (3), 1102-1130, 2014. [AOS]
42. Marin J.-M., Pillai N.S., Robert C.P. and Rousseau J. Relevant statistics for Bayesian model choice. Journal of the Royal Statistical Society, Series-B, 76 (5), 833-859, 2014. [JRSSB]
43. Pillai N.S. and Yin J. Universality of covariance matrices. The Annals of Applied Probability, 24 (3), 935-1001, 2014. [AOAP]
44. Wang, H and Pillai N.S. On a class of shrinkage priors for covariance estimation. Journal of Computational and Graphical Statistics, 22 (3), 689-707, 2013. [JCGS]
45. Hairer M. and Pillai N.S. Regularity of Laws and Ergodicity of Hypoelliptic SDEs driven by Rough paths. The Annals of Probability, 41 (4), 2544-2598, July 2013. [AOP]
46. Pillai N.S. and Yin, J. Edge universality of Correlation matrices. The Annals of  Statistics, 40 (3), 1737-1763, 2012. [AOS]
47. Pillai N.S., Stuart A.M. and Thiery, A.H. Optimal Scaling and Diffusion Limits for the Langevin Algorithm in High Dimensions. The Annals of Applied Probability, 22 (6) , 2320-2356, 2012. [AOAP]
48. Beskos A., Pillai N.S., Roberts G.O., Sanz-serna J.M. and Stuart A.M. Optimal tuning of the Hybrid Monte Carlo Algorithm. Bernoulli, 19, 5(A), 1501-1534, 2013. [Bernoulli]
49. Mattingly J.C., Mckinley, S.A. and Pillai N.S. Geometric ergodicity of a bead-spring system with stochastic Stokes forcing. Stochastic Processes and Applications, 122 (12), 3593-3979, 2012. [SPA]
50. Mattingly J.C., Pillai N.S. and Stuart, A.M. Diffusion limits for the Random Walk Metropolis Algorithm in High Dimensions. The Annals of Applied Probability, 22 (3): 881-930, 2012. [AOAP]
51. Robert C.P., Cornuet J.M., Marin J.M. and Pillai N.S. Lack of trust in (Approximate Bayes Computation) ABC model choice . Proceedings of the National Academy of Sciences (PNAS), 108 (37), 15112-15117, 2011. See also blog entry HERE, HERE, HERE and HERE . [PNAS]
52. Hairer M. and Pillai N.S. Ergodicity of hypoelliptic SDEs driven by fractional Brownian motion. Annales de l’Institut Henri Poincare (B) Probabilites and Statistique, 47 (2), 602-628, 2011. [IHP]
53. Li W.V., Pillai N.S. and Wolpert, R.L. On the supremum of certain families of stochastic processes. Statistics and Probability letters, 80, 916-921, 2010. [S&PL]
54. Pillai N.S. and Roberts, G.O. Discussion of Riemannian Hybrid Monte Carlo (by Girolami M. and Calderhead B.). Journal of the Royal Statistical Society, Series-B, 2011. [JRSSB]
55. Beskos A., Pillai N.S., Roberts G.O., Stuart A.M. and Sanzserna J.M. The acceptance probability of the Hybrid Monte-Carlo algorithm in High Dimensional problems. American Institute for Physics, Conference proceedings, 1281, 23-27, 2010. [AIP]
56. Dunson D.B., Pillai N.S. and Park J.H. Bayesian density regression. Journal of the Royal Statistical Society, Series B, 69, 163-183, 2007. [JRSSB]
57. Pillai N.S., Wu Q., Liang F., Mukherjee S. and Wolpert R.L. Characterizing the Function Space for Bayesian Kernel Models. Journal of Machine Learning, 8, 1769-1797, 2007. [JMLR]