People
Pablo Sánchez
E: sanchez@cs.uni-saarland.de
About me
I started my PhD in September 2019, supervised by Prof. Valera & Prof. Sonja Utz (from Leibniz-Institut für Wissensmedien). I am also part of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) PhD program.
Broadly speaking, my research is focused on building machine learning methods for the analysis of real world data. I am specially focused on (social) network data, such as Twitter. Apart from handling real world data, I also want these model to give predictions that i) people can understand and ii) are are able to work under unexpected settings. Thus, interpretability and robustness are two research topics I am very interested about.
Publications
2022
Javaloy, Adrián; Meghdadi, Maryam; Valera, Isabel
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization Journal Article Spotlight
In: CoRR, vol. abs/2206.04496, 2022.
Abstract | Links | BibTeX | Tags: adrian, isabel, maryam, project-robustgenerative, spotlight, variational autoencoder
@article{DBLP:journals/corr/abs-2206-04496,
title = {Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization},
author = {Adrián Javaloy and Maryam Meghdadi and Isabel Valera},
url = {https://doi.org/10.48550/arXiv.2206.04496},
doi = {10.48550/arXiv.2206.04496},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {CoRR},
volume = {abs/2206.04496},
abstract = {A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, multimodal VAEs tend to focus solely on a subset of the modalities, e.g., by fitting the image while neglecting the caption. We refer to this limitation as modality collapse. In this work, we argue that this effect is a consequence of conflicting gradients during multimodal VAE training. We show how to detect the sub-graphs in the computational graphs where gradients conflict (impartiality blocks), as well as how to leverage existing gradient-conflict solutions from multitask learning to mitigate modality collapse. That is, to ensure impartial optimization across modalities. We apply our training framework to several multimodal VAE models, losses and datasets from the literature, and empirically show that our framework significantly improves the reconstruction performance, conditional generation, and coherence of the latent space across modalities.},
keywords = {adrian, isabel, maryam, project-robustgenerative, spotlight, variational autoencoder},
pubstate = {published},
tppubtype = {article}
}
A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, multimodal VAEs tend to focus solely on a subset of the modalities, e.g., by fitting the image while neglecting the caption. We refer to this limitation as modality collapse. In this work, we argue that this effect is a consequence of conflicting gradients during multimodal VAE training. We show how to detect the sub-graphs in the computational graphs where gradients conflict (impartiality blocks), as well as how to leverage existing gradient-conflict solutions from multitask learning to mitigate modality collapse. That is, to ensure impartial optimization across modalities. We apply our training framework to several multimodal VAE models, losses and datasets from the literature, and empirically show that our framework significantly improves the reconstruction performance, conditional generation, and coherence of the latent space across modalities.
2021
Javaloy, Adrián; Meghdadi, Maryam; Valera, Isabel
Boosting heterogeneous VAEs via multi-objective optimization Workshop
2021.
Abstract | Links | BibTeX | Tags: adrian, isabel, maryam, project-robustgenerative
@workshop{nokey,
title = {Boosting heterogeneous VAEs via multi-objective optimization},
author = {Adrián Javaloy and Maryam Meghdadi and Isabel Valera},
url = {http://adrian.javaloy.com/publication/mo-vae/},
year = {2021},
date = {2021-12-01},
urldate = {2021-12-01},
abstract = {A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, multimodal VAEs tend to focus solely on a subset of the modalities, e.g., by fitting the image while neglecting the caption. We refer to this limitation as modality collapse. In this work, we argue that this effect is a consequence of conflicting gradients during multimodal VAE training. We show how to detect the sub-graphs in the computational graphs where gradients conflict (impartiality blocks), as well as how to leverage existing gradient-conflict solutions from multitask learning to mitigate modality collapse. That is, to ensure impartial optimization across modalities. We apply our training framework to several multimodal VAE models, losses and datasets from the literature, and empirically show that our framework significantly improves the reconstruction performance, conditional generation, and coherence of the latent space across modalities.},
keywords = {adrian, isabel, maryam, project-robustgenerative},
pubstate = {published},
tppubtype = {workshop}
}
A number of variational autoencoders (VAEs) have recently emerged with the aim of modeling multimodal data, e.g., to jointly model images and their corresponding captions. Still, multimodal VAEs tend to focus solely on a subset of the modalities, e.g., by fitting the image while neglecting the caption. We refer to this limitation as modality collapse. In this work, we argue that this effect is a consequence of conflicting gradients during multimodal VAE training. We show how to detect the sub-graphs in the computational graphs where gradients conflict (impartiality blocks), as well as how to leverage existing gradient-conflict solutions from multitask learning to mitigate modality collapse. That is, to ensure impartial optimization across modalities. We apply our training framework to several multimodal VAE models, losses and datasets from the literature, and empirically show that our framework significantly improves the reconstruction performance, conditional generation, and coherence of the latent space across modalities.
2020
Valera, Isabel; Pradier, Melanie F.; Lomeli, Maria; Ghahramani, Zoubin
General Latent Feature Models for Heterogeneous Datasets Journal Article
In: J. Mach. Learn. Res., vol. 21, pp. 100:1–100:49, 2020.
Abstract | Links | BibTeX | Tags: isabel, project-robustgenerative
@article{DBLP:journals/jmlr/ValeraPLG20,
title = {General Latent Feature Models for Heterogeneous Datasets},
author = {Isabel Valera and Melanie F. Pradier and Maria Lomeli and Zoubin Ghahramani},
url = {http://jmlr.org/papers/v21/17-328.html},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {J. Mach. Learn. Res.},
volume = {21},
pages = {100:1--100:49},
abstract = {Latent variable models allow capturing the hidden structure underlying the data. In particular, feature allocation models represent each observation by a linear combination of latent variables. These models are often used to make predictions either for new observations or for missing information in the original data, as well as to perform exploratory data analysis. Although there is an extensive literature on latent feature allocation models for homogeneous datasets, where all the attributes that describe each object are of the same (continuous or discrete) type, there is no general framework for practical latent feature modeling for heterogeneous datasets. In this paper, we introduce a general Bayesian nonparametric latent feature allocation model suitable for heterogeneous datasets, where the attributes describing each object can be arbitrary combinations of real-valued, positive real-valued, categorical, ordinal and count variables. The proposed model presents several important properties. First, it is suitable for heterogeneous data while keeping the properties of conjugate models, which enables us to develop an inference algorithm that presents linear complexity with respect to the number of objects and attributes per MCMC iteration. Second, the Bayesian nonparametric component allows us to place a prior distribution on the number of features required to capture the latent structure in the data. Third, the latent features in the model are binary-valued, which facilitates the interpretability of the obtained latent features in exploratory data analysis. Finally, a software package, called GLFM toolbox, is made publicly available for other researchers to use and extend. It is available at https://ivaleram.github.io/GLFM/. We show the flexibility of the proposed model by solving both prediction and data analysis tasks on several real-world datasets.},
keywords = {isabel, project-robustgenerative},
pubstate = {published},
tppubtype = {article}
}
Latent variable models allow capturing the hidden structure underlying the data. In particular, feature allocation models represent each observation by a linear combination of latent variables. These models are often used to make predictions either for new observations or for missing information in the original data, as well as to perform exploratory data analysis. Although there is an extensive literature on latent feature allocation models for homogeneous datasets, where all the attributes that describe each object are of the same (continuous or discrete) type, there is no general framework for practical latent feature modeling for heterogeneous datasets. In this paper, we introduce a general Bayesian nonparametric latent feature allocation model suitable for heterogeneous datasets, where the attributes describing each object can be arbitrary combinations of real-valued, positive real-valued, categorical, ordinal and count variables. The proposed model presents several important properties. First, it is suitable for heterogeneous data while keeping the properties of conjugate models, which enables us to develop an inference algorithm that presents linear complexity with respect to the number of objects and attributes per MCMC iteration. Second, the Bayesian nonparametric component allows us to place a prior distribution on the number of features required to capture the latent structure in the data. Third, the latent features in the model are binary-valued, which facilitates the interpretability of the obtained latent features in exploratory data analysis. Finally, a software package, called GLFM toolbox, is made publicly available for other researchers to use and extend. It is available at https://ivaleram.github.io/GLFM/. We show the flexibility of the proposed model by solving both prediction and data analysis tasks on several real-world datasets.
2019
Vergari, Antonio; Molina, Alejandro; Peharz, Robert; Ghahramani, Zoubin; Kersting, Kristian; Valera, Isabel
Automatic Bayesian Density Analysis Proceedings Article
In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp. 5207–5215, AAAI Press, 2019.
Abstract | Links | BibTeX | Tags: isabel, project-robustgenerative
@inproceedings{DBLP:conf/aaai/Vergari0PGKV19,
title = {Automatic Bayesian Density Analysis},
author = {Antonio Vergari and Alejandro Molina and Robert Peharz and Zoubin Ghahramani and Kristian Kersting and Isabel Valera},
url = {https://doi.org/10.1609/aaai.v33i01.33015207},
doi = {10.1609/aaai.v33i01.33015207},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI
2019, The Thirty-First Innovative Applications of Artificial Intelligence
Conference, IAAI 2019, The Ninth AAAI Symposium on Educational
Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii,
USA, January 27 - February 1, 2019},
pages = {5207--5215},
publisher = {AAAI Press},
abstract = {Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for exploratory data analysis are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.},
keywords = {isabel, project-robustgenerative},
pubstate = {published},
tppubtype = {inproceedings}
}
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for exploratory data analysis are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.