I am leading the Probabilistic Learning Group in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen. My research turns around the development of general machine learning methods that leverage the availability of data to solve real-world problems. In particular, I have three main goals in my research: i) capturing complex real-world phenomena, which often involve being able to ii) handle time-dependent, unstructured and heterogeneous data, and iii) provide interpretable results that allow us to better understand these phenomena, i.e., to include humans in the loop. Please, find more information on my research and publications on my personal website
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Valera, I., Singla, A., Gomez Rodriguez, M.
Enhancing the Accuracy and Fairness of Human Decision Making
32th Annual Conference on Neural Information Processing Systems (NIPS), 2018 (conference) Accepted
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Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.
Boosting Black Box Variational Inference
32th Annual Conference on Neural Information Processing Systems (NIPS), 2018, *equal contribution (conference) Accepted
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Farajtabar, M., Du, N., Gomez Rodriguez, M., Valera, I., Zha, H., Song, L.
Shaping Social Activity by Incentivizing Users
In Advances in Neural Information Processing Systems 27, (Editors: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, ND., and Weinberger, KQ.), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)
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