Research
I'm interested in probabilistic deep learning with a focus on developing
reliable and robust deep learning models for realworld settings.


Efficient Feature Transformations for Discriminative and Generative Incremental Learning
V. Verma, K. Liang, N. Mehta, P. Rai, & L. Carin
CVPR, 2021
A taskspecific feature map transformation strategy for continual learning, which adds minimal parameters to the base architecture while outerperformaing previous methods in discriminative and generative tasks.


WAFFLe: Weight Anonymized Factors for Federated Learning
W. Hao, N. Mehta, K. Liang, P. Cheng, M. Khamy & L. Carin
arXiv, 2020
A Bayesian nonparametric framework using shared rank1 weight factors for
federated learning.


Bayesian Nonparametric Weight Factorization for Continual Learning
with Neural Networks
N. Mehta, K. Liang, V. Verma, & L. Carin
AISTATS, 2021
Continual Learning using Bayesian Nonparametrics and layerwise dictionary of
weight factors.


Counterfactual Representation Learning with Balancing Weights
S. Assaad, S. Zeng, C. Tao, S. Datta, N. Mehta, R. Henao, F. Li, & L. Carin
AISTATS, 2021
Counterfactual Representation Learning with Balancing Weights


Graph Representation Learning via Ladder Gamma VAE.
A. Sarkar^{*}, N. Mehta^{*}, & P. Rai
AAAI, 2020. Early version appeared at Graph Representation Learning
Workshop (NeurIPS).
A Gamma Ladder Variational Autoencoder for graphstructured data that brings
together the interpretability in hierarchical, multilayer latent variable models
and the strong representational power of graph encoders.


Stochastic Blockmodels meet Graph Neural Networks
N. Mehta, L. Carin & P. Rai
ICML, 2019
A deep generative framework for overlapping community discovery and link
prediction combining the interpretability of stochastic blockmodels, such as the
latent feature relational model, with the
modeling power of deep generative models.


Estimation and Sampling of
Unnormalized Statistical Models with Stein Score Matching
N. Mehta^{*}, Jiachang Liu^{*}, Chenyang Tao & L.
Carin,
Workshop on Steinâ€™s Method. ICML, 2019
We propose using Stein's Method to estimate the parameters of a
distribution based on given samples. We also estimate the score function of the
empirical distribution and propose a new generative model combining Stein
score matching with the Langevin flow.


Deep Topic Models for MultiLabel Learning
R. Panda, A. Pensia, N. Mehta, M.Zhou & P. Rai
AISTATS, 2019
We present a probabilistic framework for multilabel learning based on a deep
generative model for the binary label vector associated with each observation.


Survival Cluster Analysis
P. Chapfuwa, C. Li, N. Mehta, L. Carin and R. Henao
CHIL, 2019
A Bayesian nonparametric model for jointly inferring the timetoevent and the
individualized riskbased cluster assignments.


Robust and Fast 3D Scan Alignment using Mutual
Information
N. Mehta, J. McBride & G. Pandey
ICRA, 2018
A mutual information based algorithm for the estimation of full
6degreeoffreedom (DOF) rigid body transformation between two overlapping
point clouds.

