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@article{DBLP:journals/corr/abs-2503-14321,

title = {COPA: Comparing the Incomparable to Explore the Pareto Front},

author = {Adrián Javaloy and Antonio Vergari and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2503.14321},

doi = {10.48550/ARXIV.2503.14321},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2503.14321},

abstract = {In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability. However, it is often unclear how to compare, aggregate and, ultimately, trade-off these objectives, making it a time-consuming task that requires expert knowledge, as objectives may be measured in different units and scales. In this work, we investigate how objectives can be automatically normalized and aggregated to systematically help the user navigate their Pareto front. To this end, we make incomparable objectives comparable using their cumulative functions, approximated by their relative rankings. As a result, our proposed approach, COPA, can aggregate them while matching user-specific preferences, allowing practitioners to meaningfully navigate and search for models in the Pareto front. We demonstrate the potential impact of COPA in both model selection and benchmarking tasks across diverse ML areas such as fair ML, domain generalization, AutoML and foundation models, where classical ways to normalize and aggregate objectives fall short.},

keywords = {adrian, isabel},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2503-15114,

title = {DeCaFlow: A Deconfounding Causal Generative Model},

author = {Alejandro Almodóvar and Adrián Javaloy and Juan Parras and Santiago Zazo and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2503.15114},

doi = {10.48550/ARXIV.2503.15114},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2503.15114},

abstract = {We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate causal inference on continuous variables under the presence of hidden confounders. Specifically, we extend previous results on causal estimation under hidden confounding to show that a single instance of DeCaFlow provides correct estimates for all causal queries identifiable with do-calculus, leveraging proxy variables to adjust for the causal effects when do-calculus alone is insufficient. Moreover, we show that counterfactual queries are identifiable as long as their interventional counterparts are identifiable, and thus are also correctly estimated by DeCaFlow. Our empirical results on diverse settings (including the Ecoli70 dataset, with 3 independent hidden confounders, tens of observed variables and hundreds of causal queries) show that DeCaFlow outperforms existing approaches, while demonstrating its out-of-the-box applicability to any given causal graph},

keywords = {adrian, isabel},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2306-05415,

title = {Causal normalizing flows: from theory to practice},

author = {Adrián Javaloy and Pablo Sánchez-Mart'ın and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2306.05415},

doi = {10.48550/arXiv.2306.05415},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {CoRR},

volume = {abs/2306.05415},

abstract = {In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically, we first leverage recent results on non-linear ICA to show that causal models are identifiable from observational data given a causal ordering, and thus can be recovered using autoregressive normalizing flows (NFs). Second, we analyze different design and learning choices for causal normalizing flows to capture the underlying causal data-generating process. Third, we describe how to implement the do-operator in causal NFs, and thus, how to answer interventional and counterfactual questions. Finally, in our experiments, we validate our design and training choices through a comprehensive ablation study; compare causal NFs to other approaches for approximating causal models; and empirically demonstrate that causal NFs can be used to address real-world problems—where the presence of mixed discrete-continuous data and partial knowledge on the causal graph is the norm. The code for this work can be found at https://github.com/psanch21/causal-flows.},

keywords = {adrian, isabel, pablo},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{DBLP:journals/corr/abs-2002-11369,

title = {Lipschitz standardization for robust multivariate learning},

author = {Adrián Javaloy and Isabel Valera},

url = {https://arxiv.org/abs/2002.11369},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {CoRR},

volume = {abs/2002.11369},

abstract = {Probabilistic learning is increasingly being tackled as an optimization problem, with gradient-based approaches as predominant methods. When modelling multivariate likelihoods, a usual but undesirable outcome is that the learned model fits only a subset of the observed variables, overlooking the rest. In this work, we study this problem through the lens of multitask learning (MTL), where similar effects have been broadly studied. While MTL solutions do not directly apply in the probabilistic setting (as they cannot handle the likelihood constraints) we show that similar ideas may be leveraged during data preprocessing. First, we show that data standardization often helps under common continuous likelihoods, but it is not enough in the general case, specially under mixed continuous and discrete likelihood models. In order for balance multivariate learning, we then propose a novel data preprocessing, Lipschitz standardization, which balances the local Lipschitz smoothness across variables. Our experiments on real-world datasets show that Lipschitz standardization leads to more accurate multivariate models than the ones learned using existing data preprocessing techniques. The models and datasets employed in the experiments can be found in this URL https://github.com/adrianjav/lipschitz-standardization},

keywords = {adrian, isabel},

pubstate = {published},

tppubtype = {article}

}