(1) Yang Y., Pati D. (2018+) Bayesian model selection consistency and oracle inequality with intractable marginal likelihood.
[link]

(2) Yang Y., Bhattacharya A., Pati D. (2018+) Frequentist coverage and sup-norm convergence rate
in Gaussian process regression.
[link]

(3) Bhattacharya A., Pati D. (2020+) Nonasymptotic Laplace approximation under model misspecification.
[link]

(4) Dhara K., Hupf B., Hajcak G., Pati D., Sinha D. (2020+) Frequentist and Bayesian Analysis of
Monotone Single-Index Models.

(5) Bhattacharya A., Pati D., Plummer S., Yang Y. (2020+) Evidence bounds in singular models: probabilistic and variational perspectives.
[link]

(6) Guha B., Zhou S., Pati D. (2021+) Adaptive posterior convergence in sparse high dimensional clipped
generalized linear models.
[link]

(7) Dasgupta S., Zhao P., Ghosh P., Pati D., Mallick B.K. (2021+) An approximate Bayesian approach to
covariate dependent graphical modeling.
[link]

(8) Lee S., Zhao P., Pati D., Mallick B.K. (2021+) Tail adaptive Bayesian shrinkage.
[link]

(11) Chuu E., Niu Y., Bhattacharya A, Pati D. (2022+) EPSOM-Hyb: a general purpose estimator
of log-marginal likelihoods with applications to probabilistic graphical models.

(12)  Chakraborty A., Bhattacharya A., Pati D. (2023+) Robust probabilistic inference via a constrained transport metric.
[link]

(15)  Bhattacharya A., Pati D., Yang Y. (2023+) On the Convergence of Coordinate Ascent Variational Inference.
[link]

(16)  Jacob P.M., Patel L., Bhattacharya A., Pati D. (2023+) Memory Efficient And Minimax Distribution Estimation Under Wasserstein Distance Using Bayesian Histograms.
[link]

(16)  Zhao P., Bhattacharya A., Pati D., Mallick B.K. (2024+) Factorized Fusion Shrinkage for Dynamic Relational Data.
[link]

1. Pati, D., Reich, B.J., Dunson, D.B. (2011). Biometrika, 98 (1): 35-48.
   Bayesian geostatistical modeling with informative sampling locations.
   [link]
2. Pati, D., Dunson, D.B., Tokdar, S.T. (2013). Journal of Multivariate Analysis, 116: 456-472.
   Posterior consistency in conditional distribution estimation.
   [link]
3. Pati, D., & Dunson, D.B. (2014). Annals of the Institute for Statistical Mathematics, 66 (1): 1-31.
   Bayesian nonparametric regression with varying residual density.
   [link]
4. Bhattacharya, A., Pati, D., & Dunson, D.B. (2014). Annals of Statistics, 32 (1): 352-381.
   Anisotropic function estimation with multi-bandwidth Gaussian process.
   [link]
5. Pati, D., Bhattacharya, A., Pillai, N.S. & Dunson, D.B. (2014). Annals of Statistics, 42 (3): 1102-1130.
   Posterior contraction in sparse Bayesian factor models for massive covariance matrices.
   [link]
6. Sarkar, A., Mallick, B.K., Staudenmayer, J., Pati, D. & Carroll, R.J. (2014) Journal of Computational and Graphical Statistics , 24 (3): 1101-1125.
   Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors.
   [link]
7. Cervone, D., Pillai, N.S., Pati, D., Berbecko, R., & Lewis, J.H. (2014). Annals of Applied Statistics, 8 (3): 1341-1371.
   A location-mixture autoregressive model for online forecasting of lung-tumor motion.
   [link]
8. Gu, K., Pati, D. & Dunson, D.B. (2014). Journal of the American Statistical Association, 109 (508): 1481-1494.
   Bayesian Multiscale Modeling of Closed Curves in Point Clouds.
   [link]
9. Bhattacharya, A., Pati, D., Pillai, N.S. & Dunson, D.B. (2015). Journal of the American Statistical Association, 110 (512): 1479-1489.
   Dirichlet Laplace priors for optimal shrinkage.
   [link] [code]
10. Tang, Y., Sinha, D., Pati, D., Lipsitz, S. & Lipshultz, S. (2015). Biostatistics, 16 (3): 441-453.
    Bayesian Partial Linear Model for Skewed Longitudinal Data.
    [link]
11. Zhang, Z., Pati, D. & Srivastava, A. (2015). Journal of Statistical Planning and Inference, 166: 171-186.
    Bayesian Clustering of Shapes of Curves.
    [link]
12. Pati, D. & Bhattacharya, A. (2015). Statistics and Probability Letters, 103: 100-104.
    Adaptive Bayesian inference in the Gaussian sequence model using exponential-variance priors.
    [link]
13. Pati, D., Bhattacharya, A. & Cheng G. (2015). Journal of Machine Learning Research, 16: 2837-2851.
    Optimal Bayesian estimation in random covariate design with a rescaled Gaussian process prior.
    [link]
14. Bhattacharya, A., Pati, D., Pillai, N.S. & Dunson, D.B. (2016). Stochastic Processes and their Applications, 26 (12): 3828-3842.
    Sub-optimality of some continuous shrinkage priors.
    [link]
15. Norets, A. & Pati, D. (2017). Econometric Theory, 33 (4): 980-1012.
    Adaptive Bayesian estimation of conditional densities.
    [link]
16. Hanning, L. & Pati, D. (2017). Computational Statistics & Data Analysis, 107: 107-119.
    Variable selection using shrinkage priors.
    [link] [code]
17. Sarkar, A., Pati, D., Chakrabarty, A., Mallick, B., Carroll, R.J. (2017). Journal of the American Statistical Association, 113 (521) 401-416.
    Bayesian semiparametric multivariate density deconvolution.
    [link]
18. Bhattacharya, A., Pati, D. (2017). Information and Inference, 6: 416-440.
    Posterior contraction in Gaussian process regression using Wasserstein approximations.
    [link]
19. Vo, G., Pati, D. (2017) Open Journal of Statistics, 7: 567-588.
    Sparse additive Gaussian process with soft interactions.
    [link]
20. Pati, D., Bhattacharya, A., Yang, Y. (2018). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS).
    On the Statistical optimality of variational Bayes.
    [link]
21. Geng, J., Bhattacharya, A., Pati, D. (2018). Journal of the American Statistical Association, 114 (526) 893-905.
    Probabilistic community detection with unknown number of communities.
    [link] [code]
22. Dasgupta S., Pati D., Jermyn I, Srivastava S. (2018) 2018 IEEE Statistical Signal Processing Workshop (SSP).
    Shape-Constrained and Unconstrained Density Estimation Using Geometric Exploration.
    [link]
23. Bhingare A., Sinha D., Pati D., Bandyopadhyay, D., Lipsitz S.R. (2018) Biometrics, 75 (2) 528-538.
    Semiparametric Bayesian latent variable regression for skewed multivariate data.
    [link]
24. Sabnis G., Pati D., Bhattacharya A. (2019) Sankhya (Series A), 81 466-481.
    Compressed covariance estimation with automated dimension learning.
    [link]
25. Dasgupta S., Pati D., Srivastava A. (2019) Quarterly of Applied Mathematics, 77 399-422.
    Bayesian Shape-Constrained Density Estimation.
    [link]
26. Dasgupta S., Pati D., Srivastava A. (2019) Statistica Sinica.
    A two-step geometric framework For density modeling.
    [link] [Density-estimation-code] [Density-regression-code]
27. Bhattacharya A., Pati D., Yang Y (2019) The Annals of Statistics, 47 (1): 39-66.
    Bayesian fractional posteriors.
    [link]
28. Zhou, S., Giuliani, P., Piekarewicz, J., Bhattacharya, A., Pati, D. (2019) Physical Review C, 99 (5): 055202.
    Revisiting the proton-radius problem using constrained Gaussian processes.
    [link] [code]
29. Kumar P., Mukherjee T., Pati D., Xu L., Blasch, E. (2019) Big Data Mining and Analytics (IEEE Explore), 2 (4): 319-348.
    Large scale FM signal strength map estimation for passive approximate localization.
    [link]
30. Dhara K., Pati D., Sinha D., Lipsitz S.R. (2019) Bayesian Analysis, 15 (3): 759-780.
    A New Bayesian Single Index Model with or without Covariates Missing at Random.
    [link]
31. Ghosh P., Pati D., Bhattacharya A. (2019) Sankhya (Series A); invited for special volume in honor of Prof. J.K. Ghosh, to appear.
    Posterior Contraction Rates for Stochastic Block Models.
    [link]
32. Ray, P., Pati, D., Bhattacharya, A. (2020) Statistics and Computing, 30: 839–853.
    Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors.
    [link]
33. Yang Y., Pati D., Bhattacharya A. (2020) The Annals of Statistics, 48 (2): 886–905.
    α-variational inference with statistical guarantees.
    [link]
34. Sarkar A., Pati D., Mallick B., Carroll R.J. (2020) Journal of the American Statistical Association (Applications & Case Studies), to appear.
    Bayesian Copula Density Deconvolution for Zero-Inflated Data with Applications in Nutritional Epidemiology.
    [link]
35. Binette O., Pati D., Dunson D.B. (2020) Journal of Machine Learning Research, 21 (119): 1–26.
    Bayesian fitting of closed surfaces through tensor products.
    [link]
36. Niu Y., Pati D., Mallick, B. (2020) Bernoulli, to appear.
    Bayesian Graph Selection Consistency For Decomposable Graphs.
    [link]
37. Plummer S., Pati D., Bhattacharya A. (2020) Entropy, to appear.
    Dynamics of coordinate ascent variational inference: A case study in 2D Ising models.
    [link]
38. Dasgupta S., Pati D., Jermyn, I. Srivastava A. (2020) Technometrics, to appear.
    Shape-Constrained Univariate Density Estimation.
    [link] [code]
39. Plummer S., Zhou S., Bhattacharya A., Dunson D.B., Pati D. (2021) AISTATS 2021, to appear.
    Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference.
    [link]
40. Chuu E., Pati D., Bhattacharya A. (2021) AISTATS 2021, to appear.
    A Hybrid Approximation to the Marginal Likelihood.
    [link]
41. Lim Y., Bhattacharya A., Holt J. W., Pati D. (2021) Letter in Physical Review C, to appear.
    Radius and equation of state constraints from massive neutron stars and GW190814.
    [link]
42. Guha B., Bhattacharya A., Pati D. (2021) IEEE-ICTAI.
    Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting.
    [link]
43. Lei B., Tanner Q. K., Bhattacharya A.,Pati D., Qian X., Arroyave R., Mallick B.K. (2021) npj Computational Materials, to appear.  Bayesian Optimization with Adaptive Surrogate Models for Automated Experimental Design.
    [link]
44. Bandyopadhyay D., Hilden P., Pati D., Fernandes J., Russell S. L., Fellows J. L., Nagarajan R. (2021) Modern Approaches in Dentistry and Oral Health Care, to appear. Correlated tooth-level caries status in a Type-2 diabetic Gullah population.
    [link]
45. Ghosh I., Bhattacharya A., Pati D. (2022) Journal of Machine Learning Research, to appear. Statistical optimality and stability of tangent transform algorithms in logit models.
    [link]
46. Wang H., Bhattacharya A., Pati D., Yang Y. (2022) AISTATS 2022, to appear. Structured Variational Inference in Bayesian State-Space Models.
    [link]
47. Zhou S., Wang T. Pati D., Yang Y., Carroll R.J. (2022) Journal of Machine Learning Research.. Gaussian processes with Error in Variables: Theory and Computation.
    [link]
48. Zhou S., Ray P., Pati D., Bhattacharya A. (2022) Journal of the American Statistical Association, to appear. Mass-shifting phenomenon of truncated multivariate normal priors.
    [link]
49. Acharyya S., Zhang Z., Bhattacharya A., Pati D. (2022) Stat, to appear. Bayesian Hierarchical Modeling on Covariance Valued Data.
    [link]
50. Acharyya S., Pati D., Bandyopadhyay D., Sun S (2023) Journal of Applied Statistics, to appear. A monotone single index model for missing-at-random longitudinal proportion data.
    [link]
51. Niu Y., Ni Y., Pati D., Mallick B.K. (2023) Journal of the American Statistical Association, to appear. Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.
    [link]
52. Karwa V., Pati D., Petrovic S., Solus L. et al (2023) Journal of the Royal Statistical Society, Series B, to appear. Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels.
    [link]
53. Chakraborty A., Bhattacharya A., Pati D. (2024) Entropy. Constrained Reweighting of Distributions: An Optimal Transport Approach.
    [link]
54. Chakraborty A., Bhattacharya A., Pati D. (2024) Entropy. A Gibbs Posterior Framework for Fair Clustering.
    [link]
55. Chuu E., Niu Y., Bhattacharya A, Pati D. (2024) Algorithms. EPSOM-Hyb: a general purpose estimator of log-marginal likelihoods with applications to probabilistic graphical models.
    [link]
56. Helwig J., Dasgupta S., Zhao P., Mallick B.K.,Pati D. (2024) ACM Transactions on Mathematical Software . covdepGE: a Covariate-Dependent Approach to Gaussian Graphical Modeling in R.
    [link]
57. Das S, Niu Y., Ni Y., Mallick B.K, Pati D. (2024) Journal of Computational & Graphical Statistics . Blocked Gibbs sampler for hierarchical Dirichlet processes.
    [link]
58. Zhao P., Bhattacharya A., Pati D., Mallick B.K. (2024) Journal of Machine Learning Research, accepted pending minor revisions. Structured Optimal Variational Inference for Dynamic Latent Space Models.
    [link]