junier barbaro oliva

about me

Hello, I'm Junier, a 5th year Ph.D. student at Carnegie Mellon's Machine Learning Department co-advised by Barnabás Póczos and Jeff Schneider.

Currently I'm working on performing machine learning tasks when inputs, and possibly outputs, are complex objects like sets, distributions, or functions. Moreover, I'm interested in exporting concepts from learning on distributional and functional inputs to modern techniques in deep learning. Furthermore, I'm interested in analyzing massive datasets, both in terms of instances and covariates. This work will help us solve problems like predicting whether a Twitter trending topic will go viral, or predicting the risk of disease given a person's functional brain data, or predicting the future distribution of dark matter particles.

I have preference for simple estimators that make few assumptions; in particular I'm interested in (frequentist) non-parametric methods. One very interesting challenge is scaling up non-parametric methods to huge datasets and high dimensions.

Prior to beginning my Ph.D. program, I received my B.S. and M.S. in Computer Science from Carnegie Mellon University. I also spent a year as a software engineer for Yahoo!, and a summer as a machine learning intern at Uber ATC.

preprint papers

Deep Mean Maps

Junier B Oliva*, Dougal J Sutherland*, Barnabás Póczos, Jeff Schneider

*Indicates equal authorship.

Finding Representative Objects with Sparse Modeling

JB Oliva, DJ Sutherland, Y Ma

conference papers

Nonparametric risk and stability analysis for multi-task learning problems

X Wang, JB Oliva, J Schneider, B Póczos

International Joint Conference on Artificial Intelligence (IJCAI), 2016.

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabás Póczos

Advances in Neural Information Processing Systems (NIPS), 2016.

Estimating cosmological parameters from the dark matter distribution

Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C Price, Shirley Ho, Jeff Schneider, Barnabás Póczos

International Conference on Machine Learning (ICML), 2016

Bayesian Nonparametric Kernel-Learning

Junier B Oliva*, Avinava Dubey*, A. Willson, Barnabas Poczos, Jeff Schneider, Eric P Xing

International Conference on AI and Statistics (AISTATS), 2016.

*Indicates equal authorship.

Linear-Time Learning on Distributions with Approximate Kernel Embeddings

DJ Sutherland*, JB Oliva*, B Póczos, J Schneider

AAAI Conference on Artificial Intelligence, 2015.

*Indicates equal authorship.

Fast Function to Function Regression

J Oliva, W Neiswanger, B Poczos, E Xing, J Schneider

International Conference on AI and Statistics (AISTATS), 2015.

FuSSO: Functional Shrinkage and Selection Operator.

Oliva, J., Póczos, B., Verstynen, T., Singh, A., Schneider, J., Yeh, F., Tseng, W.

International Conference on AI and Statistics (AISTATS), 2014.

Fast Distribution to Real Regression.

Oliva, J., Neiswanger, W., Póczos, B., Schneider, J., & Xing, E.

International Conference on AI and Statistics (AISTATS), 2014.

Distribution to Distribution Regression.

Oliva, J., Póczos, B., & Schneider, J.

International Conference on Machine Learning (ICML), 2013.

workshop papers

Sparse Functional Regression.

Oliva, J., Póczos, B., Singh, A., Schneider, J., & Verstynen, T.

2013. NIPS Workshop on Modern Nonparametric Methods in Machine Learning.


Anomaly Detection and Modeling of Trajectories.

Oliva, J.

Carnegie Mellon University, Pittsburgh, Pennsylvania, 2012. MS Thesis