parameter learning

Learning-Augmented Heuristics: Simple Yet Smart, Robust and Interpretable Cache Eviction OSDI 2026 Paths Not Taken: Structure-Based Pruning in PSDD Learning and Inference AAAI 2026 A Unified Framework for Human-Allied Learning of Probabilistic Circuits AAAI 2025 Behavior Synthesis via Contact-Aware Fisher Information Maximization RSS 2025 Generalizing Constraint Models in Constraint Acquisition AAAI 2025 Learnability of Parameter-Bounded Bayes Nets AAAI 2025 Orthogonal projection-based regularization for efficient model augmentation L4DC 2025 An Analysis of Elo Rating Systems via Markov Chains NeurIPS 2024 Knowledge Intensive Learning of Credal Networks UAI 2024 Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters AAAI 2024 Bayesian Structure Scores for Probabilistic Circuits AISTATS 2023 Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality JMLR 2023 Learning Fractals by Gradient Descent AAAI 2023 Sensitivity-Free Gradient Descent Algorithms JMLR 2023 Show Me How It’s Done: The Role of Explanations in Fine-Tuning Language Models ACML 2023 A Fourier Approach to Mixture Learning NeurIPS 2022 Beyond black box densities: Parameter learning for the deviated components NeurIPS 2022 Finite Sample Analysis Of Dynamic Regression Parameter Learning NeurIPS 2022 Inference and Learning with Model Uncertainty in Probabilistic Logic Programs AAAI 2022 Learning Tractable Probabilistic Models from Inconsistent Local Estimates NeurIPS 2022 The AIM and EM Algorithms for Learning from Coarse Data JMLR 2022 Interpreting Neural Networks as Quantitative Argumentation Frameworks AAAI 2021 Juice: A Julia Package for Logic and Probabilistic Circuits AAAI 2021 Learning the Parameters of Bayesian Networks from Uncertain Data AAAI 2021 Online Variational Filtering and Parameter Learning NeurIPS 2021