Search Results for author: Alfredo Cuesta-Infante

Found 10 papers, 6 papers with code

R&R: Metric-guided Adversarial Sentence Generation

1 code implementation17 Apr 2021 Lei Xu, Alfredo Cuesta-Infante, Laure Berti-Equille, Kalyan Veeramachaneni

It improves the quality of adversarial examples by optimizing a critique score which combines the fluency, similarity, and misclassification metrics.

Adversarial Attack General Classification +5

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

5 code implementations16 Sep 2020 Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.

Benchmarking Time Series +2

Adversarially learned anomaly detection for time series data

no code implementations25 Sep 2019 Alexander Geiger, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

Based on the recent developments in adversarially learned models, we propose a new approach for anomaly detection in time series data.

Anomaly Detection Time Series +1

Robust Invisible Video Watermarking with Attention

2 code implementations3 Sep 2019 Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

The goal of video watermarking is to embed a message within a video file in a way such that it minimally impacts the viewing experience but can be recovered even if the video is redistributed and modified, allowing media producers to assert ownership over their content.

Towards Reducing Biases in Combining Multiple Experts Online

no code implementations19 Aug 2019 Yi Sun, Ivan Ramirez, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person's fitness for a particular opportunity.

Decision Making Fairness

Learning Vine Copula Models For Synthetic Data Generation

no code implementations4 Dec 2018 Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

Model Selection Reinforcement Learning (RL) +1

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