Submitted journal papers :
- Sudyanti, P.A. and Rao, V.A. (2019)
Flexible Mixture Modeling on Constrained Spaces
[arxiv:1809.09238]
Publications:
- Wang, Q., Rao, V.A. and Teh Y.W. (2020)
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Journal of Computational and Graphical Statistics (accepted)
[arxiv:1903.10659]
- Jaiswal, P., Rao, V.A. and Honnappa, H. (2020)
Asymptotic Consistency of α-Renyi-Approximate Posteriors
Journal of Machine Learning Research
[arxiv:1902.01902]
- Zhang, B. and Rao, V.A. (2020)
Efficient parameter sampling for Markov jump processes
Journal of Computational and Graphical Statistics
[arxiv:1704.02369]
- Jaiswal, P., Honnappa, H. and Rao V.A. (2019)
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Stat
[arxiv:1911.01288]
- Murphy, R.L., Srinivasan, B., Rao V.A. and Riberio, B. (2019)
Relational Pooling for Graph Representations
International Conference on Machine Learning (ICML 2019)
[pdf]
- Tang, B., Iyer, A., Rao, V.A. and Kong, N. (2019)
Group-Representative Functional Network Estimation from Multi-Subject fMRI Data via MRF-based Image Segmentation
Computer Methods and Programs in Biomedicine
[pdf]
- Yang, J., Rao, V.A., and Neville, J. (2019)
A Stein-Papangelou Goodness-of-Fit Test for Point Processes.
Artificial Intelligence and Statistics (AISTATS 2019) (oral)
[pdf]
- Murphy, R.L., Srinivasan, B., Rao V.A. and Riberio, B. (2019)
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
International Conference on Learning Representations (ICLR 2019)
[pdf]
- Gomes, G.M., Rao, V.A. and Neville, J. (2018)
Multi-level hypothesis testing for populations of heterogeneous networks
International Conference on Data Mining (ICDM 2018)
[extended version]
- Tan, X., Rao, V.A. and Neville, J. (2018)
The Indian Buffet Hawkes Process to Model Evolving Latent Influences.
Uncertainty in Artificial Intelligence (UAI 2018)
[pdf]
- Yang, J., Liu, Q., Rao, V.A., and Neville, J. (2018)
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy.
International Conference on Machine Learning (ICML 2018)
[pdf]
- Tan, X., Rao, V.A. and Neville, J. (2018)
Nested CRP with Hawkes-Gaussian Processes
Artificial Intelligence and Statistics (AISTATS 2018)
[pdf]
- Pan, J.*, Zhang, B.* and Rao, V.A. (2017)
Collapsed Variational Inference for Markov Jump Processes
Neural Information Processing Systems (NIPS 2017)
[pdf]
- Yang, J., Rao, V.A. and Neville, J. (2017)
Decoupling Homophily and Reciprocity with Latent Space Network Models
Uncertainty in Artificial Intelligence (UAI 2017)
[pdf]
- Lin, L., Rao, V.A., and Dunson, D.B. (2017)
Bayesian nonparametric inference on the Stiefel manifold
Statistica Sinica
[Statistica Sinica]
[arxiv:1311.0907]
[bibtex]
- Rao, V.A., Adams,R.P. and Dunson, D.B. (2016)
Bayesian inference for Matérn repulsive processes
Journal of the Royal Statistical Society, Series B
[arxiv:1308.1136]
[supplementary]
[bibtex]
- Tan, X., Naqvi, S.A., Qi, Y., Heller, K.A., and Rao, V.A. (2016)
Content-based modeling of reciprocal relationships using Hawkes and
Hawkes and Gaussian processes
Uncertainty in Artificial Intelligence (UAI 2016)
[pdf]
- Pan, J., Rao, V.A., Agarwal, P.K. and Gelfand, A.E. (2016)
Markov-modulated Poisson Processes for Check-in Data
International Conference on Machine Learning (ICML 2016)
[pdf]
- Rao, V.A., Lin, L. and Dunson, D.B. (2016)
Data augmentation for models based on rejection sampling
Biometrika
[Biometrika]
[arxiv:1406.6652]
[bibtex]
- Rao, V.A. (2015)
Dirichlet process mixtures and nonparametric Bayesian approaches to clustering,
Handbook of Cluster Analysis.
C. Hennig, M. Meila, F. Murtagh, R. Rocci (eds.).
Chapman & Hall/CRC Handbooks of Modern Statistical Methods
[pdf]
- Lian, W., Henao, R., Rao, V.A., Lucas, J. and Carin, L. (2015)
A Multitask Point Process Predictive Model
International Conference on Machine Learning (ICML 2015)
[pdf]
- Yuan, X., Rao, V.A., Han, S., and Carin, L. (2014)
Hierarchical Infinite Divisibility for Multiscale Shrinkage
IEEE Transactions on Signal Processing
[pdf]
[supplementary]
- Lian, W., Rao, V.A., Eriksson,B. and Carin, L. (2014)
Modeling correlated arrival events with latent semi-Markov processes
International Conference on Machine Learning (ICML 2014)
[pdf]
[JMLR]
[bibtex]
- Rao, V.A. and Teh, Y.W. (2013)
Fast MCMC sampling for Markov jump processes and extensions
Journal of Machine Learning Research 14:3295−3320, 2013
[pdf]
[JMLR]
[bibtex]
- Carlson, D., Rao, V.A., Vogelstein, J., and Carin L. (2013)
Real-time inference for a Gamma process model of neural spiking
Advances in Neural Information Processing Systems 26 (NIPS 2013)
[pdf]
[bibtex]
[code]
- Chen, C., Rao, V.A., Buntine,W. and Teh, Y.W. (2013)
Dependent normalized random measures (Oral presentation)
International Conference on Machine Learning (ICML 2013)
[pdf]
[supplementary]
[bibtex]
- Rao, V.A. and Teh, Y.W. (2012)
MCMC for continuous-time discrete-state systems
Advances in Neural Information Processing Systems 25 (NIPS 2012)
[pdf]
[supplementary]
[bibtex]
- Petralia, F., Rao, V.A. and Dunson, D.B. (2012)
Repulsive mixtures
Advances in Neural Information Processing Systems 25 (NIPS 2012)
[pdf]
[bibtex]
- Rao, V.A. and Teh, Y.W. (2011)
Gaussian process modulated renewal processes
Advances in Neural Information Processing Systems 24 (NIPS 2011)
[pdf]
[supplementary]
[bibtex]
- Rao, V.A. and Teh, Y.W. (2011)
Fast MCMC inference for Markov jump processes and continuous time Bayesian networks
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
[pdf]
[bibtex]
- Rao, V.A. and Teh, Y.W. (2009)
Spatial normalized Gamma processes (Spotlight presentation)
Advances in Neural Information Processing Systems 22 (NIPS 2009)
[pdf]
[bibtex]
- Howard,M.W., Jing,B., Rao, V.A., Provyn, J.P. and Datey,A.V. (2009)
Bridging the gap: Transitive associations between items presented in similar temporal contexts
Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol 35(2)
[pdf]
[bibtex]
- Rao, V.A. and Howard,M.W. (2007)
Retrieved context and the discovery of semantic structure (Spotlight presentation)
Advances in Neural Information Processing Systems 20 (NIPS 2007)
[pdf]
[bibtex]
Workshop proceedings :
- Rao, V.A., Sudderth, E. and Teh, Y.W. (2014)
Expectation propagation for Dirichlet process mixture models
Advances in Variational Inference, NIPS 2014
[pdf]
[bibtex]
PhD Thesis :
- Markov chain Monte Carlo for continuous-time discrete-state systems
PhD thesis, University College London
Supervisor: Yee Whye Teh
[pdf]
[bibtex]