People

@article{DBLP:journals/corr/abs-2503-22454,

title = {A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination},

author = {Ayan Majumdar and Deborah Dormah Kanubala and Kavya Gupta and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2503.22454},

doi = {10.48550/ARXIV.2503.22454},

year  = {2026},

date = {2026-03-19},

urldate = {2026-03-19},

journal = {CoRR},

volume = {abs/2503.22454},

abstract = {Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or lending decisions, into binary classification tasks (e.g., approve or not approve). However, these approaches overlook that such decisions are not inherently binary; they also involve non-binary treatment decisions (e.g., loan or bail terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). We argue that treatment decisions are integral to the decision-making process and, therefore, should be central to fairness analyses. Consequently, we propose a causal framework that extends and complements existing fairness notions by explicitly distinguishing between decision-subjects’ covariates and the treatment decisions. Our framework leverages path-specific counterfactual reasoning to: (i) measure treatment disparity and its downstream effects in historical data; and (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Finally, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical loan approval data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.},

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pubstate = {published},

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}

@article{nokey,

title = {Bridging Fairness and Explainability: Can Input-Based Explanations Promote Fairness in Hate Speech Detection?},

author = {Yifan Wang, Mayank Jobanputra, Ji-Ung Lee, Soyoung Oh, Isabel Valera, Vera Demberg},

doi = { https://doi.org/10.48550/arXiv.2509.22291},

year  = {2026},

date = {2026-02-11},

urldate = {2026-02-11},

abstract = {Natural language processing (NLP) models often replicate or amplify social bias from training data, raising concerns about fairness. At the same time, their black-box nature makes it difficult for users to recognize biased predictions and for developers to effectively mitigate them. While some studies suggest that input-based explanations can help detect and mitigate bias, others question their reliability in ensuring fairness. Existing research on explainability in fair NLP has been predominantly qualitative, with limited large-scale quantitative analysis. In this work, we conduct the first systematic study of the relationship between explainability and fairness in hate speech detection, focusing on both encoder- and decoder-only models. We examine three key dimensions: (1) identifying biased predictions, (2) selecting fair models, and (3) mitigating bias during model training. Our findings show that input-based explanations can effectively detect biased predictions and serve as useful supervision for reducing bias during training, but they are unreliable for selecting fair models among this http URL http://candidates.our/ code is available at this URL https://github.com/Ewanwong/fairness_x_explainability},

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pubstate = {published},

tppubtype = {article}

}

@article{Uth2025PersonalizingEI,

title = {Personalizing explanations in AI-based decisions: The effects of personalization and (Mis)aligning with individual preferences},

author = {Richard Uth and Nelli Niemitz and Isabel Valera and Markus Langer},

url = {https://api.semanticscholar.org/CorpusID:283171660},

year  = {2026},

date = {2026-02-03},

urldate = {2025-01-01},

journal = {Computers in Human Behavior},

abstract = {The increasing reliance on AI-based decision-making in high-stakes contexts underscores the need for transparency and justice. Here, negative outcomes drive individuals affected by AI-based decisions to seek actionable explanations that enable them to realize what they can do to achieve a better future outcome. However, actionability is subjective, varying across individuals and contexts. Personalization of explanations has been proposed to address this variability, but insights on personalized explanation processes, their potential, and challenges are scarce. This paper investigates the impact of personalization and (mis)alignment with individual needs and preferences in explanations for AI-based decisions through an experimental online study simulating denied loan applications. In a within-participants design (N=255), participants ranked the actionability of decision-relevant features and experienced five explanation conditions: personalized directive explanations based on the most, second most, or least actionable feature (as ranked by participants); a non-personalized directive explanation highlighting a random feature; and no explanation. In line with justice theory, our results show that any explanation was better than none, and that personalized explanations led to more favorable reactions than non-personalized explanations, enhancing perceptions of justice and attractiveness of the bank. Closer alignment with preferences had only small positive effects, mainly for attractiveness. These findings highlight that even simple ranking-based approaches can make explanations more effective and accessible without requiring technical expertise while cautioning against offering superficial control. This study provides insights into the effects of ranking-based personalization, informing the design of explainability tailored to diverse user needs and addressing ethical and practical considerations in personalization.},

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}

@article{DBLP:journals/corr/abs-2603-17544,

title = {Per-Domain Generalizing Policies: On Learning Efficient and Robust  Q-Value Functions (Extended Version with Technical Appendix)},

author = {Nicola J. Müller and Moritz Oster and Isabel Valera and Jörg Hoffmann and Timo P. Gros},

url = {https://doi.org/10.48550/arXiv.2603.17544},

doi = {10.48550/ARXIV.2603.17544},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {CoRR},

volume = {abs/2603.17544},

abstract = {Learning per-domain generalizing policies is a key challenge in learning for planning. Standard approaches learn state-value functions represented as graph neural networks using supervised learning on optimal plans generated by a teacher planner. In this work, we advocate for learning Q-value functions instead. Such policies are drastically cheaper to evaluate for a given state, as they need to process only the current state rather than every successor. Surprisingly, vanilla supervised learning of Q-values performs poorly as it does not learn to distinguish between the actions taken and those not taken by the teacher. We address this by using regularization terms that enforce this distinction, resulting in Q-value policies that consistently outperform state-value policies across a range of 10 domains and are competitive with the planner LAMA-first.},

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pubstate = {published},

tppubtype = {article}

}

@article{Eisenhut2025OnPG,

title = {On Picking Good Policies: Leveraging Action-Policy Testing in Policy Training},

author = {Jan Eisenhut and Daniel Fivser and Isabel Valera and J¨org Hoffmann},

url = {https://api.semanticscholar.org/CorpusID:281347744},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Proceedings of the International Conference on Automated Planning and Scheduling},

abstract = {Testing is a natural approach to assess the quality of learned action policies π. Prior work introduced policy testing in AI planning as searching for bugs in π, that is, states where π is sub-optimal with respect to a given testing objective. Beyond quality assurance, an obvious application of these methods is policy selection: given several π to choose from, we can use testing to select the "least buggy" one. Here, we integrate testing-based policy selection into the training process. This includes making more informed decisions when selecting the final policy after training, as well as choosing more promising intermediate policies during the training process. Our experiments with ASNets action policies show that integrating testing allows us to more reliably obtain good-quality policies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Eniser2025UsingAT,

title = {Using Action-Policy Testing in RL to Reduce the Number of Bugs},

author = {Hasan Ferit Eniser and Songtuan Lin and Nicola Müller and Anastasia Isychev and Valentin Wüstholz and Isabel Valera and J¨org Hoffmann and Maria Christakis},

url = {https://api.semanticscholar.org/CorpusID:280219509},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Proceedings of the International Symposium on Combinatorial Search},

abstract = {Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior work has devised action-policy testing methodology, that identifies so-called bug states where policy performance is sub-optimal. Here we show how to leverage this methodology during the RL process, using action-policy testing to find bugs and injecting those as alternate start states for the training runs. Running experiments across six 2D games, we find that our testing-guided training often achieves similar expected reward while reducing the number of bugs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.},

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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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2504-16580,

title = {Hyper-Transforming Latent Diffusion Models},

author = {Ignacio Peis and Batuhan Koyuncu and Isabel Valera and Jes Frellsen},

url = {https://doi.org/10.48550/arXiv.2504.16580},

doi = {10.48550/ARXIV.2504.16580},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2504.16580},

abstract = {We introduce a novel generative framework for functions by integrating Implicit Neural Representations (INRs) and Transformer-based hypernetworks into latent variable models. Unlike prior approaches that rely on MLP-based hypernetworks with scalability limitations, our method employs a Transformer-based decoder to generate INR parameters from latent variables, addressing both representation capacity and computational efficiency. Our framework extends latent diffusion models (LDMs) to INR generation by replacing standard decoders with a Transformer-based hypernetwork, which can be trained either from scratch or via hyper-transforming: a strategy that fine-tunes only the decoder while freezing the pre-trained latent space. This enables efficient adaptation of existing generative models to INR-based representations without requiring full retraining. We validate our approach across multiple modalities, demonstrating improved scalability, expressiveness, and generalization over existing INR-based generative models. Our findings establish a unified and flexible framework for learning structured function representations.},

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pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2506-01544,

title = {Temporal Variational Implicit Neural Representations},

author = {Batuhan Koyuncu and Rachael DeVries and Ole Winther and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2506.01544},

doi = {10.48550/ARXIV.2506.01544},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2506.01544},

abstract = {We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time series that enables efficient individualized imputation and forecasting. By integrating implicit neural representations with latent variable models, TV-INRs learn distributions over time-continuous generator functions conditioned on signal-specific covariates. Unlike existing approaches that require extensive training, fine-tuning or meta-learning, our method achieves accurate individualized predictions through a single forward pass. Our experiments demonstrate that with a single TV-INRs instance, we can accurately solve diverse imputation and forecasting tasks, offering a computationally efficient and scalable solution for real-world applications. TV-INRs excel especially in low-data regimes, where it outperforms existing methods by an order of magnitude in mean squared error for imputation task.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2506-05014,

title = {Towards Reasonable Concept Bottleneck Models},

author = {Nektarios Kalampalikis and Kavya Gupta and Georgi Vitanov and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2506.05014},

doi = {10.48550/ARXIV.2506.05014},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2506.05014},

abstract = {In this paper, we propose textbf{C}oncept textbf{REA}soning textbf{M}odels (CREAM), a novel family of Concept Bottleneck Models (CBMs) that: (i) explicitly encodes concept-concept ({texttt{C-C}}) and concept-task ({texttt{C$rightarrow$Y}}) relationships to enforce a desired model reasoning; and (ii) use a regularized side-channel to achieve competitive task performance, while keeping high concept importance. Specifically, CREAM architecturally embeds (bi)directed concept-concept, and concept to task relationships specified by a human expert, while severing undesired information flows (e.g., to handle mutually exclusive concepts). Moreover, CREAM integrates a black-box side-channel that is regularized to encourage task predictions to be grounded in the relevant concepts, thereby utilizing the side-channel only when necessary to enhance performance. Our experiments show that: (i) CREAM mainly relies on concepts while achieving task performance on par with black-box models; and (ii) the embedded {texttt{C-C}} and {texttt{C$rightarrow$Y}} relationships ease model interventions and mitigate concept leakage.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2508-21512,

title = {Accept or Deny? Evaluating LLM Fairness and Performance in Loan  Approval across Table-to-Text Serialization Approaches},

author = {Israel Abebe Azime and Deborah Dormah Kanubala and Tejumade Afonja and Mario Fritz and Isabel Valera and Dietrich Klakow and Philipp Slusallek},

url = {https://doi.org/10.48550/arXiv.2508.21512},

doi = {10.48550/ARXIV.2508.21512},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2508.21512},

abstract = {Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle to process tabular data, ensuring fairness and delivering reliable predictions. In this work, we assess the performance and fairness of LLMs on serialized loan approval datasets from three geographically distinct regions: Ghana, Germany, and the United States. Our evaluation focuses on the model’s zero-shot and in-context learning (ICL) capabilities. Our results reveal that the choice of serialization format significantly affects both performance and fairness in LLMs, with certain formats such as GReaT and LIFT yielding higher F1 scores but exacerbating fairness disparities. Notably, while ICL improved model performance by 4.9-59.6% relative to zero-shot baselines, its effect on fairness varied considerably across datasets. Our work underscores the importance of effective tabular data representation methods and fairness-aware models to improve the reliability of LLMs in financial decision-making.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2505-00439,

title = {Per-Domain Generalizing Policies: On Validation Instances and Scaling  Behavior},

author = {Timo P. Gros and Nicola J. Müller and Daniel Fiser and Isabel Valera and Verena Wolf and Jörg Hoffmann},

url = {https://doi.org/10.48550/arXiv.2505.00439},

doi = {10.48550/ARXIV.2505.00439},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {CoRR},

volume = {abs/2505.00439},

abstract = {Recent work has shown that successful per-domain generalizing action policies can be learned. Scaling behavior, from small training instances to large test instances, is the key objective; and the use of validation instances larger than training instances is one key to achieve it. Prior work has used fixed validation sets. Here, we introduce a method generating the validation set dynamically, on the fly, increasing instance size so long as informative and this http://feasible.we/ also introduce refined methodology for evaluating scaling behavior, generating test instances systematically to guarantee a given confidence in coverage performance for each instance size. In experiments, dynamic validation improves scaling behavior of GNN policies in all 9 domains used.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kanubala2025OnTM,

title = {On the Misalignment Between Legal Notions and Statistical Metrics of Intersectional Fairness},

author = {Deborah Dormah Kanubala and Isabel Valera},

url = {https://api.semanticscholar.org/CorpusID:282175621},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},

abstract = {Intersectional (un)fairness, as conceptualized in legal and social theory, emphasizes the non-additive and structurally complex nature of discrimination against individuals at the intersection of multiple sensitive attributes (such as race, gender, etc). Recent works have proposed statistical metrics for intersectional fairness by estimating disparities across groups of individuals sharing two or more sensitive attributes. However, it is unclear if these metrics detect uniquely intersectional discrimination. We therefore pose the following question, Do current statistical intersectional metrics detect the non-additive discrimination highlighted by intersectionality theory? More specifically, to answer this, we run controlled synthetic data experiments that explicitly allow us to control for single, multiple, intersectional, and compounded forms of discrimination. Our analyses show that current statistical metrics for intersectional fairness behave more like multi-attribute disparity measures. Specifically, they respond more strongly to additive or compounded biases than to non-additive interaction effects. While they effectively capture disparities across multiple sensitive attributes, they often fail to detect uniquely intersectional discrimination. These findings reveal a fundamental misalignment between existing intersectional fairness metrics and the legal and theoretical foundations of intersectionality. We argue that if intersectional fairness metrics are to be deemed truly intersectional, they must be explicitly designed to account for the structural, non-additive nature of intersectional discrimination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2403-17776,

title = {Exploring the Boundaries of Ambient Awareness in Twitter},

author = {Pablo Sánchez-Martin and Sonja Utz and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2403.17776},

doi = {10.48550/ARXIV.2403.17776},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {CoRR},

volume = {abs/2403.17776},

abstract = {Ambient awareness refers to the ability of social media users to obtain knowledge about who knows what (i.e., users' expertise) in their network, by simply being exposed to other users' content (e.g, tweets on Twitter). Previous work, based on user surveys, reveals that individuals self-report ambient awareness only for parts of their networks. However, it is unclear whether it is their limited cognitive capacity or the limited exposure to diagnostic tweets (i.e., online content) that prevents people from developing ambient awareness for their complete network. In this work, we focus on in-wall ambient awareness (IWAA) in Twitter and conduct a two-step data-driven analysis, that allows us to explore to which extent IWAA is likely, or even possible. First, we rely on reactions (e.g., likes), as strong evidence of users being aware of experts in Twitter. Unfortunately, such strong evidence can be only measured for active users, which represent the minority in the network. Thus to study the boundaries of IWAA to a larger extent, in the second part of our analysis, we instead focus on the passive exposure to content generated by other users -- which we refer to as in-wall visibility. This analysis shows that (in line with citet{levordashka2016ambient}) only for a subset of users IWAA is plausible, while for the majority it is unlikely, if even possible, to develop IWAA. We hope that our methodology paves the way for the emergence of data-driven approaches for the study of ambient awareness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2404-12356,

title = {Improving the interpretability of GNN predictions through conformal-based  graph sparsification},

author = {Pablo Sánchez-Martin and Kinaan Aamir Khan and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2404.12356},

doi = {10.48550/ARXIV.2404.12356},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {CoRR},

volume = {abs/2404.12356},

abstract = {Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. However, most GNN architectures aggregate information from all nodes and edges in a graph, regardless of their relevance to the task at hand, thus hindering the interpretability of their predictions. In contrast to prior work, in this paper we propose a GNN emph{training} approach that jointly i) finds the most predictive subgraph by removing edges and/or nodes -- -emph{without making assumptions about the subgraph structure} -- while ii) optimizing the performance of the graph classification task. To that end, we rely on reinforcement learning to solve the resulting bi-level optimization with a reward function based on conformal predictions to account for the current in-training uncertainty of the classifier. Our empirical results on nine different graph classification datasets show that our method competes in performance with baselines while relying on significantly sparser subgraphs, leading to more interpretable GNN-based predictions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ploscb/WuVSEEQ24,

title = {Probabilistic neural transfer function estimation with Bayesian system  identification},

author = {Nan Wu and Isabel Valera and Fabian H. Sinz and Alexander S. Ecker and Thomas Euler and Yongrong Qiu},

url = {https://doi.org/10.1371/journal.pcbi.1012354},

doi = {10.1371/JOURNAL.PCBI.1012354},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {PLoS Comput. Biol.},

volume = {20},

number = {7},

pages = {1012354},

abstract = {Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually require a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as most exciting inputs (MEIs), from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. At the same time, our variational method provides us with an effectively infinite ensemble, avoiding the idiosyncrasy of any single model, to generate MEIs. This allows us to estimate the uncertainty of stimulus-response function, which we have found to be negatively correlated with the predictive performance at model level and may serve to evaluate models. Furthermore, our approach enables us to identify response properties with credible intervals and to determine whether the inferred features are meaningful by performing statistical tests on MEIs. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models in the limited-data regime.},

keywords = {},

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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2302-06223,

title = {Variational Mixture of HyperGenerators for Learning Distributions  Over Functions},

author = {Batuhan Koyuncu and Pablo Sánchez-Mart'ın and Ignacio Peis and Pablo M. Olmos and Isabel Valera},

url = {https://doi.org/10.48550/arXiv.2302.06223},

doi = {10.48550/arXiv.2302.06223},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {CoRR},

volume = {abs/2302.06223},

abstract = {Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with inference tasks, such as missing data imputation, or directly cannot tackle them. In this work, we propose a novel deep generative model, named VAMoH. VAMoH combines the capabilities of modeling continuous functions using INRs and the inference capabilities of Variational Autoencoders (VAEs). In addition, VAMoH relies on a normalizing flow to define the prior, and a mixture of hypernetworks to parametrize the data log-likelihood. This gives VAMoH a high expressive capability and interpretability. Through experiments on a diverse range of data types, such as images, voxels, and climate data, we show that VAMoH can effectively learn rich distributions over continuous functions. Furthermore, it can perform inference-related tasks, such as conditional super-resolution generation and in-painting, as well or better than previous approaches, while being less computationally demanding.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/csur/KarimiBSV23,

title = {A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential  Recommendations},

author = {Amir-Hossein Karimi and Gilles Barthe and Bernhard Schölkopf and Isabel Valera},

url = {https://doi.org/10.1145/3527848},

doi = {10.1145/3527848},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {ACM Comput. Surv.},

volume = {55},

number = {5},

pages = {95:1–95:29},

abstract = {Machine learning is increasingly used to inform decision making in sensitive situations where decisions have consequential effects on individuals’ lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role in the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavorably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions toward which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.},

keywords = {},

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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2103-02631,

title = {Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning},

author = {Adrián Javaloy and Isabel Valera},

url = {https://arxiv.org/abs/2103.02631},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {CoRR},

volume = {abs/2103.02631},

abstract = {Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negative transfer. Previous works have tracked down this issue to the disparities in gradient magnitudes and directions across tasks, when optimizing the shared network parameters. While recent work has acknowledged that negative transfer is a two-fold problem, existing approaches fall short as they only focus on either homogenizing the gradient magnitude across tasks; or greedily change the gradient directions, overlooking future conflicts. In this work, we introduce RotoGrad, an algorithm that tackles negative transfer as a whole: it jointly homogenizes gradient magnitudes and directions, while ensuring training convergence. We show that RotoGrad outperforms competing methods in complex problems, including multi-label classification in CelebA and computer vision tasks in the NYUv2 dataset. A Pytorch implementation can be found in this https://github.com/adrianjav/rotograd},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2110-14690,

title = {VACA: Design of Variational Graph Autoencoders for Interventional  and Counterfactual Queries},

author = {Pablo Sánchez-Mart'ın and Miriam Rateike and Isabel Valera},

url = {https://arxiv.org/abs/2110.14690},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {CoRR},

volume = {abs/2110.14690},

abstract = {In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs. Finally, we apply VACA to evaluate counterfactual fairness in fair classification problems, as well as to learn fair classifiers without compromising performance.},

keywords = {},

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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2010-04050,

title = {A survey of algorithmic recourse: definitions, formulations, solutions,  and prospects},

author = {Amir-Hossein Karimi and Gilles Barthe and Bernhard Schölkopf and Isabel Valera},

url = {https://arxiv.org/abs/2010.04050},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {CoRR},

volume = {abs/2010.04050},

abstract = {Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

pubstate = {published},

tppubtype = {article}

}