COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
Optuna: A next-generation hyperparameter optimization framework
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
IDOBE compiles over 10,000 epidemiological outbreaks into a public benchmark and shows that MLP-based models deliver the most robust short-term forecasts while statistical methods hold a slight edge pre-peak.
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.
EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.
Fine-tuned e5_large LLM reaches 0.866 F1_micro on ICD classification of 145k Spanish psychiatric texts, outperforming BoW, TF-IDF, and other transformers.
citing papers explorer
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Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
COCOCO is a conformal framework for NeSy-CBMs that jointly conformalizes concepts and labels, reconciles them via deduction-abduction revision, and satisfies consistency, coverage, and conciseness while retaining distribution-free guarantees.
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When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
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IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem
IDOBE compiles over 10,000 epidemiological outbreaks into a public benchmark and shows that MLP-based models deliver the most robust short-term forecasts while statistical methods hold a slight edge pre-peak.
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Tabular Foundation Model for Generative Modelling
TabFORGE generates high-quality synthetic tabular data by leveraging pretrained causality-aware representations in a two-stage diffusion-decoder architecture that mitigates latent distribution shifts.
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EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.
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Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models
Fine-tuned e5_large LLM reaches 0.866 F1_micro on ICD classification of 145k Spanish psychiatric texts, outperforming BoW, TF-IDF, and other transformers.