*Current Graduate Student*

Rishit Sheth is in his fourth year as a CS PhD candidate researching variational inference for Bayesian latent variable models. He holds a master's in EE from the University of Illinois at Urbana-Champaign and a bachelor's in EE from the University of Delaware.

Research-related software available at Github

**Current location: **196 Boston Ave.

**CV: **rshethcv.pdf

- R. Sheth and R. Khardon, Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models,
*Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS)*, 2017 [+]

**Authors:**R. Sheth and R. KhardonProceedings of the Annual Conference on Neural Information Processing Systems (NIPS)

**Year:**2017**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth and R. Khardon, A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models,
*The 19th International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2016 [+]

**Authors:**R. Sheth and R. KhardonThe 19th International Conference on Artificial Intelligence and Statistics (AISTATS)

**Year:**2016**Url:**http://www.cs.tufts.edu/~roni/PUB/aistats16-fixedpoint.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth and R. Khardon, Monte Carlo Structured SVI for Non-Conjugate Models,
*arXiv*, 1612.03957, 2016 [+]

**Authors:**R. Sheth and R. KhardonarXiv

1612.03957**Year:**2016**Url:**http://arxiv.org/abs/1612.03957**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth, Y. Wang and R. Khardon , Sparse Variational Inference for Generalized Gaussian Process Models,
*Proceedings of the International Conference on Machine Learning (ICML)*, 2015 [+]

**Authors:**R. Sheth, Y. Wang and R. KhardonProceedings of the International Conference on Machine Learning (ICML)

**Year:**2015**Url:**http://www.cs.tufts.edu/~roni/PUB/icml15sparseFPGP.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**

**Current Research Topics:**

- Graphical Models: Theory, Algorithms and Applications [+]

**Description:**Our work is done in the context of expressive Bayesian probabilistic models (a.k.a graphical models), developing inference algorithms for them, developing a learning theory that explains why these algorithms work and applying them in interesting applications. Our theoretical results provide distribution-free guarantees on the risk of approximate Bayesian inference algorithms. Recent models include constrained clustering, multi-task learning, sparse Gaussian processes, mixture of expert models for label discretization, matrix facorization and topic models. Recent applications include land-cover clustering and classification, analysis of time series from Astronomy, and predicting contamination level in environmental engineering.

This work is partly supported by NSF grants IIS-1714440 and IIS-0803409

**Associated Data/Software:**

- Variational inference in Bayesian latent Gaussian models [+]

**Description:**Code for efficient supervised learning in the extended latent Gaussian model family via variational inference is provided through Rishit Sheth's Github site.

For context and description please see: Sheth, R. and Khardon R., A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models, AISTATS 2016.**Associated People:****Associated Research:****Download File:**https://github.com/rsheth80 - Sparse variational inference in generalized Gaussian process models [+]

**Description:**Code for efficient supervised learning with Gaussian Process models with arbitrary likelihood functions, through a sparse variational approximation is provided through Rishit Sheth's Github site.

For context and description please see: Sheth, R., Wang, Y., Khardon R., Sparse variational inference for generalized Gaussian process models, ICML 2015.**Associated People:****Associated Research:****Download File:**https://github.com/rsheth80