Papers
Publications
1.
From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems.
Algorithmic Fairness through the Lens of Metrics and Evaluation, Neurips 2024.
[arXiv]
2.
Policy Gradients for Optimal Parallel Tempering MCMC.
ICML 2024.
[Workshop on Structured Probabilistic Inference & Generative Modeling]
3.
Towards Understanding the Dynamics of Gaussian--Stein Variational Gradient Descent.
Neurips 2023.
[arXiv]
4.
Rate-optimal refinement strategies for local approximation MCMC.
Statistics and Computing, 32 (4), 2022.
[Stat. and Comp.]
5.
Fast and memory-optimal dimension reduction using Kac’s walk.
The Annals of Applied Probability, 32 (5): 4038-4064, 2022.
[AOAP]
6.
Universality and least singular values of random matrix products: A simplified approach.
Bernoulli, 27 (4), 2519-2531, 2021.
[Bernoulli]
7.
Late 19th-Century Navigational Uncertainties and Their Influence on Sea Surface Temperature Estimates.
The Annals of Applied Statistics, 15 (1), 22-40, 2021.
[AOAS]
8.
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]
9.
Simple conditions for metastability of continuous markov chains.
Journal of Applied Probability, 58 (1), 83-105, 2021.
[JAP]
10.
Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields.
Nature (food), 1, 127–133, 2020.
[Nature]
11.
Diagnosing missing always at random in multivariate data.
Biometrika, 107 (1), 246-253, 2020.
[Biometrika]
12.
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]
13.
Designs for estimating the treatment effects in networks with interference.
The Annals of Statistics, 48 (2), 679-712, 2020.
[AOS]
14.
Optimal scaling of MALA algorithm with irreversible proposals for Gaussian targets.
Stochastics and Partial Differential Equations: Analysis and Computation, 8, 311–361, 2020.
[SPDE]
15.
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]
16.
MCMC for imbalanced categorical data.
Journal of the American Statistical Association, 114 (527), 1394-1403, 2019.
[JASA]
17.
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]
18.
Asymptotic analysis of the random walk Metropolis algorithm on ridged densities.
Annals of Applied Probability, 28 (5), 2966-3001, 2018.
[AOAP]
19.
Parallel local approximation MCMC for expensive models .
SIAM Journal of Uncertainty Quantification, 6(1), 339–373, 2018.
[SIAM Journal of UQ]
20.
Elementary bounds on mixing times for decomposable chains.
Stochastic Processes and Applications, 127 (9), 3068-3109, 2017.
[SPA]
21.
Kac’s walk on n-sphere mixes in nlog(n) steps.
Annals of Applied Probability, 27 (1), 631-650, 2017.
[AOAP]
22.
Sub-optimality of some continuous shrinkage priors.
Stochastic Processes and Applications: Special issue in Memoriam Prof. Evarist Gine, 126 (12), 3828-3842, 2016.
[SPA]
23.
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]
24.
More Powerful Multiple Testing in Randomized Experiments with Non-Compliance.
Statistica Sinica, 27 (3), 1319-1345, 2017.
[Stat. Sinica]
25.
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]
26.
Single particle passive microrheology in biological fluids with drift.
Journal of Rheology, 60 (3), 379-392, 2016.
[Journal of Rheology]
27.
The use of a single pseudo-sample in approximate Bayes computation.
Statistics and Computing, 27 (3), 583-590, 2017.
[Stat. and Comp.]
28.
Mixing times for a constrained Ising process on the torus at low density.
The Annals of Probability, 45 (2), 1003-1070, 2017.
[AOP]
29.
Statistical Inference for dynamical systems: a review .
Statistics Surveys, 9, 209-252, 2015.
[Stat. Surveys]
30.
An unexpected encounter with Cauchy and Levy.
The Annals of Statistics 44 (5), 2089-97, 2017.
[AOS]
31.
Asymptotically exact MCMC algorithms via local approximations for computationally intensive models.
Journal of the American Statistical Association, 111, 1591-1607, 2017.
[JASA]
32.
Bayesian nonparametric weighted sampling inference.
Bayesian Analysis, 10 (3), 605-625, 2015.
[Bayesian Analysis]
33.
Hypothesis testing for sparse binary regression.
The Annals of Statistics, 43 (1), 352-381, 2015.
[AOS]
34.
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]
35.
Consistency of maximum likelihood estimation for some dynamical systems.
The Annals of Statistics, 43 (1), 2014, 1-29, 2015.
[AOS]
36.
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]
37.
Noisy gradient flow from a random walk in Hilbert space.
Stochastic Partial Differential Equations and Applications, 2, 196–232, 2014.
[SPDE]
38.
A location-mixture autoregressive model for online prediction of lung tumors.
The Annals of Applied Statistics, 8 (3), 1341-1371, 2014.
[AOAS]
39.
A Function Space HMC algorithm with second order Langevin diffusion limit.
Bernoulli, 22 (1), 60-106, 2016.
[Bernoulli]
40.
Posterior contraction in sparse Bayesian factor models for massive covariance matrices.
The Annals of Statistics, 42 (3), 1102-1130, 2014.
[AOS]
41.
Relevant statistics for Bayesian model choice.
Journal of the Royal Statistical Society, Series-B, 76 (5), 833-859, 2014.
[JRSSB]
42.
Universality of covariance matrices.
The Annals of Applied Probability, 24 (3), 935-1001, 2014.
[AOAP]
43.
On a class of shrinkage priors for covariance estimation.
Journal of Computational and Graphical Statistics, 22 (3), 689-707, 2013.
[JCGS]
44.
Regularity of Laws and Ergodicity of Hypoelliptic SDEs driven by Rough paths.
The Annals of Probability, 41 (4), 2544-2598, July 2013.
[AOP]
45.
Edge universality of Correlation matrices.
The Annals of Statistics, 40 (3), 1737-1763, 2012.
[AOS]
46.
Optimal Scaling and Diffusion Limits for the Langevin Algorithm in High Dimensions.
The Annals of Applied Probability, 22 (6) , 2320-2356, 2012.
[AOAP]
47.
Optimal tuning of the Hybrid Monte Carlo Algorithm.
Bernoulli, 19, 5(A), 1501-1534, 2013.
[Bernoulli]
48.
Geometric ergodicity of a bead-spring system with stochastic Stokes forcing.
Stochastic Processes and Applications, 122 (12), 3593-3979, 2012.
[SPA]
49.
Diffusion limits for the Random Walk Metropolis Algorithm in High Dimensions.
The Annals of Applied Probability, 22 (3): 881-930, 2012.
[AOAP]
50.
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]
51.
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]
52.
On the supremum of certain families of stochastic processes.
Statistics and Probability letters, 80, 916-921, 2010.
[S&PL]
53.
Discussion of Riemannian Hybrid Monte Carlo (by Girolami M. and Calderhead B.).
Journal of the Royal Statistical Society, Series-B, 2011.
[JRSSB]
54.
The acceptance probability of the Hybrid Monte-Carlo algorithm in High Dimensional problems.
American Institute for Physics, Conference proceedings, 1281, 23-27, 2010.
[AIP]
55.
Bayesian density regression.
Journal of the Royal Statistical Society, Series B, 69, 163-183, 2007.
[JRSSB]
56.
Characterizing the Function Space for Bayesian Kernel Models.
Journal of Machine Learning, 8, 1769-1797, 2007.
[JMLR]