Journal submissions (out of date, please look at google scholar:)

  • Sudyanti, P.A. and Rao, V.A. (2019)
    Flexible Mixture Modeling on Constrained Spaces
    [arxiv:1809.09238]

    • Publications (out of date, please look at google scholar:):

  • 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]