Jovana was a DPhil student supervised by Dino Sejdinovic and Yee Whye Teh. Her research interests encompass kernel methods and deep learning. She worked on kernel-based summary statistics construction, inference methods for generative adversarial networks and weight initialization methods for neural networks.
Jovana graduated in 2019 with the thesis entitled Representation Learning with Kernel Methods and is now a Senior Research Scientist at DeepMind.
Publications
2018
J. Mitrovic
,
D. Sejdinovic
,
Y. Teh
,
Causal Inference via Kernel Deviance Measures, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery method based on purely observational data. From a novel interpretation of the notion of asymmetry between cause and effect, we derive a corresponding asymmetry measure using the framework of reproducing kernel Hilbert spaces. Based on this, we propose three decision rules for causal discovery. We demonstrate the wide applicability of our method across a range of diverse synthetic datasets. Furthermore, we test our method on real-world time series data and the real-world benchmark dataset Tubingen Cause-Effect Pairs where we outperform existing state-of-the-art methods.
@inproceedings{MitSejTeh2018,
author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y.W.},
title = {{{Causal Inference via Kernel Deviance Measures}}},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2018},
month = dec
}
2017
J. Mitrovic
,
D. Sejdinovic
,
Y. W. Teh
,
Deep Kernel Machines via the Kernel Reparametrization Trick, in International Conference on Learning Representations (ICLR) Workshop Track, 2017.
While deep neural networks have achieved state-of-the-art performance on many tasks across varied domains, they still remain black boxes whose inner workings are hard to interpret and understand. In this paper, we develop a novel method for efficiently capturing the behaviour of deep neural networks using kernels. In particular, we construct a hierarchy of increasingly complex kernels that encode individual hidden layers of the network. Furthermore, we discuss how our framework motivates a novel supervised weight initialization method that discovers highly discriminative features already at initialization.
@inproceedings{MitSejTeh2017,
author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y. W.},
booktitle = {International Conference on Learning Representations (ICLR) Workshop Track},
title = {{Deep Kernel Machines via the Kernel Reparametrization Trick}},
year = {2017},
bdsk-url-1 = {https://openreview.net/forum?id=Bkiqt3Ntg¬eId=Bkiqt3Ntg}
}
2016
J. Mitrovic
,
D. Sejdinovic
,
Y. W. Teh
,
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression, in International Conference on Machine Learning (ICML), 2016, 1482–1491.
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics. Although the choice of appropriate problem-specific summary statistics crucially influences the quality of the likelihood approximation and hence also the quality of the posterior sample in ABC, there are only few principled general-purpose approaches to the selection or construction of such summary statistics. In this paper, we develop a novel framework for this task using kernel-based distribution regression. We model the functional relationship between data distributions and the optimal choice (with respect to a loss function) of summary statistics using kernel-based distribution regression. We show that our approach can be implemented in a computationally and statistically efficient way using the random Fourier features framework for large-scale kernel learning. In addition to that, our framework shows superior performance when compared to related methods on toy and real-world problems.
@inproceedings{MitSejTeh2016,
author = {Mitrovic, J. and Sejdinovic, D. and Teh, Y. W.},
booktitle = {International Conference on Machine Learning (ICML)},
pages = {1482--1491},
title = {{DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression}},
year = {2016},
bdsk-url-1 = {http://jmlr.org/proceedings/papers/v48/mitrovic16.html}
}
