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University of Liège

Academic institutioneurope · be
Official website
Research library97linked papers
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Selected work

Representative Papers

Nonfundamentalness or missing information ? Evidence from causal-noncausal VARs in macro-finance

Jul 30, 2026

This study investigates whether the noncausal dynamics observed in macroeconomic VAR models stem from genuine non-fundamentalness or from omitted common information that is available to economic agents but unobserved by econometricians. To address this, the paper proposes a hybrid causal–noncausal VARX framework integrated with factor filtering and employs the generalized covariance (GCov) estimator to effectively identify and correct noncausal components. Empirical application to the Stock–Watson monetary policy SVAR demonstrates that the proposed approach substantially attenuates spurious noncausal signals, yielding impulse responses that align more closely with theoretical priors and notably alleviating the “price puzzle.” This refinement enables a more accurate recovery of the underlying causal structure of the economy.

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Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance

Jul 28, 2026

This work addresses the challenge of effectively leveraging privileged state information to improve observation representation learning in model-based reinforcement learning, particularly under asymmetric observation settings. Building upon the Dreamer framework, the authors propose a novel asymmetric world model training approach that introduces a latent guidance mechanism and a lightweight asymmetric representation learning objective. This design enhances the model’s capacity to exploit privileged information without requiring complex architectural modifications. Experimental results demonstrate that the proposed method consistently outperforms both the original Dreamer and existing asymmetric approaches across multiple benchmark tasks, achieving significant and stable performance gains.

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Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

Jul 28, 2026

This work addresses the challenge of eddy current–induced distortions in diffusion MRI, which cause misalignment across multi-shell images and compromise the accuracy of microstructural analysis. The authors propose the first end-to-end deep learning framework for joint correction of eddy currents and subject motion. In the first stage, a supervised image translation network harmonizes image contrast across shells; in the second stage, an unsupervised registration module incorporating physical constraints simultaneously estimates distortion and motion parameters, enabling full correction in a single forward pass. By circumventing conventional iterative optimization, the method achieves correction accuracy comparable to FSL Eddy while offering substantially faster inference. Trained on UK Biobank data, the approach is well-suited for large-scale studies and clinical deployment.

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Recent publications

Latest Papers

Nonfundamentalness or missing information ? Evidence from causal-noncausal VARs in macro-finance

Jul 30, 2026

This study investigates whether the noncausal dynamics observed in macroeconomic VAR models stem from genuine non-fundamentalness or from omitted common information that is available to economic agents but unobserved by econometricians. To address this, the paper proposes a hybrid causal–noncausal VARX framework integrated with factor filtering and employs the generalized covariance (GCov) estimator to effectively identify and correct noncausal components. Empirical application to the Stock–Watson monetary policy SVAR demonstrates that the proposed approach substantially attenuates spurious noncausal signals, yielding impulse responses that align more closely with theoretical priors and notably alleviating the “price puzzle.” This refinement enables a more accurate recovery of the underlying causal structure of the economy.

0 citationsRead paper

Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance

Jul 28, 2026

This work addresses the challenge of effectively leveraging privileged state information to improve observation representation learning in model-based reinforcement learning, particularly under asymmetric observation settings. Building upon the Dreamer framework, the authors propose a novel asymmetric world model training approach that introduces a latent guidance mechanism and a lightweight asymmetric representation learning objective. This design enhances the model’s capacity to exploit privileged information without requiring complex architectural modifications. Experimental results demonstrate that the proposed method consistently outperforms both the original Dreamer and existing asymmetric approaches across multiple benchmark tasks, achieving significant and stable performance gains.

0 citationsRead paper

Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI

Jul 28, 2026

This work addresses the challenge of eddy current–induced distortions in diffusion MRI, which cause misalignment across multi-shell images and compromise the accuracy of microstructural analysis. The authors propose the first end-to-end deep learning framework for joint correction of eddy currents and subject motion. In the first stage, a supervised image translation network harmonizes image contrast across shells; in the second stage, an unsupervised registration module incorporating physical constraints simultaneously estimates distortion and motion parameters, enabling full correction in a single forward pass. By circumventing conventional iterative optimization, the method achieves correction accuracy comparable to FSL Eddy while offering substantially faster inference. Trained on UK Biobank data, the approach is well-suited for large-scale studies and clinical deployment.

0 citationsRead paper