You are all cordially invited to the AMLab seminar on **Thursday 12th September **at **14:00** in **C4.174**, where **Marco Federici **will give a talk titled **“Towards Robust Representations by Exploiting Multiple Data Views”**. There are the usual drinks and snacks!

**Abstract: **The problem of creating data representations can be
formulated as the definition of an encoding function which maps
observations into a predefined code space. Whenever the encoding is used
as an intermediate step for a predictive task, among the possible
encodings, we are generally interested in the ones that retain the
desired target information. Furthermore, recent literature has shown
that discarding irrelevant factors of variation in the data (minimality)
yield robustness and invariance to nuisances of the task. Following
these two general guidelines, in this work, we introduce an
information-theoretical method that exploits some known properties of
the predictive task to create robust data representations without
requiring direct supervision signals. By exploiting pairs of joint
observations, our model learns representations that are as
discriminative as the original data for the predictive task while being
more robust than the raw-signal. The proposed theory builds upon
well-known self-supervised algorithms (such as Contrastive Predictive
Coding and the InfoMax principle), bridging the gap between information
bottleneck and probabilistic invariance. Empirical evidence shows the
applicability of our model for both multi-view and single-view datasets.