Call for Presentations

CFP @ Trustworthy AI Workshop @ Deeplearning Indaba 2022

We’re looking for short presentations (10 to 15 minutes) related to:

  • Audit techniques in data and ML models.
  • Advances in algorithms and metrics for robust ML.
  • Uncertainity quantification techniques and Fairness studies.
  • Applications and research in data and model Privacy/Security.
  • Methodologies or case studies for explainable and transparent AI.

If you’re interested in presenting you work at TrustAI Workshop, please submit your response here before the 1st of August 2022.

CFP of Practical Machine Learning for Developing Countries workshop at ICLR 2022

Happy to announce the CFP of Practical Machine Learning for Developing Countries workshop at ICLR 2022. We encourage contributions that highlight challenges of learning in low resource environments that are typical in developing countries.

Deadline: February 25th 12:00 AM UTC.

Practical Machine Learning for Developing Countries (PML4DC) workshop is a full-day event that has been running regularly for the past 2 years in row at ICLR (past events include PML4DC 2020 and PML4DC 2021). PML4DC aims to foster collaborations and build a cross-domain community by featuring invited talks, panel discussions, contributed presentations (oral and poster) and round-table mixers.

Towards creativity characterization of generative models in the Activation Space

We’re going to be presenting some preliminary results in our work “Towards creativity characterization of generative models via group-based subset scanning” at Synthetic Data Generation Workshop at ICLR’21


Creativity is a process that provides novel and meaningful ideas. Current deep learning approaches open a new direction enabling the study of creativity from a knowledge acquisition perspective. Novelty generation using powerful deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been attempted. However, such models discourage out-of-distribution generation to avoid instability and decrease spurious sample generation, limiting their creative generation potential. We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models. Our experiments on original, typically decoded, and “creatively decoded” (Das et al., 2020) image datasets reveal that the proposed subset scores distribution is more useful for detecting creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. Also, the node activations highlighted during the creative decoding process are different from those responsible for normal sample generation.


Initial post


I plan to have a mixed english/español page to keep updated on latest projects (both research and personal :)), papers and presentations. Please note that is still under construction 🚧

For now you can see a short bio here, a list of the latest publications and presentations. If you like to chat you can find me over Twitter or cintas[dot]celia[at]gmail[dot]com