:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pensar, Johan, Nyman, Henrik, Niiranen, Juha, Corander, Jukka
Format:	Preprint
Published:	2014
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/1401.4988
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning pairwise Markov network structures using correlation neighborhoods
by: Kuronen, Juri, et al.
Published: (2019)

Path-Based Gradient Boosting for Graph-Level Prediction
by: Meggio, Claudio, et al.
Published: (2026)

Learning Bayesian and Markov Networks with an Unreliable Oracle
by: Harviainen, Juha, et al.
Published: (2026)

Uncertainty quantification in automated valuation models with spatially weighted conformal prediction
by: Hjort, Anders, et al.
Published: (2023)

Misspecification-robust likelihood-free inference in high dimensions
by: Thomas, Owen, et al.
Published: (2020)

Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation
by: Pesonen, Henri, et al.
Published: (2023)

Joint Model and Data Sparsification via the Marginal Likelihood
by: Timans, Alexander, et al.
Published: (2026)

A Correction of Pseudo Log-Likelihood Method
by: Feng, Shi, et al.
Published: (2024)

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
by: Dhahri, Rayen, et al.
Published: (2024)

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2024)

SLIME: Stabilized Likelihood Implicit Margin Enforcement for Preference Optimization
by: Afanasyev, Maksim, et al.
Published: (2026)

Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
by: Rozzio, Adam, et al.
Published: (2026)

Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
by: Yeh, Jia-Fong, et al.
Published: (2020)

Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation
by: Akyildiz, Ö. Deniz, et al.
Published: (2023)

Offline Preference Optimization via Maximum Marginal Likelihood Estimation
by: Najafi, Saeed, et al.
Published: (2025)

Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
by: Yang, Hanming, et al.
Published: (2024)

Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)

Least Squares and Marginal Log-Likelihood Model Predictive Control using Normalizing Flows
by: Cramer, Eike
Published: (2024)

Surrogate-based ABC matches generalized Bayesian inference under specific discrepancy and kernel choices
by: Järvenpää, Marko, et al.
Published: (2025)

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables
by: Morin, Sacha, et al.
Published: (2023)

Maximum Likelihood Reinforcement Learning
by: Tajwar, Fahim, et al.
Published: (2026)

Bayesian estimation of causal effects from observational categorical data
by: Kvisgaard, Vera, et al.
Published: (2025)

Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
by: Chang, Serina, et al.
Published: (2024)

Crash-Consistent Checkpointing for AI Training on macOS/APFS
by: Jeon, Juha
Published: (2025)

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
by: Silander, Tomi, et al.
Published: (2024)

Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
by: Alipourfard, Borzou, et al.
Published: (2018)

No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
by: Bayrooti, Jasmine, et al.
Published: (2025)

Incremental Learning of Affordances using Markov Logic Networks
by: Potter, George, et al.
Published: (2024)

Learning Likelihood Ratios with Neural Network Classifiers
by: Rizvi, Shahzar, et al.
Published: (2023)

Hidden Markov Neural Networks
by: Rimella, Lorenzo, et al.
Published: (2020)

Dependency-aware Maximum Likelihood Estimation for Active Learning
by: Kalkanli, Beyza, et al.
Published: (2025)

Maximum Likelihood Learning of Latent Dynamics Without Reconstruction
by: Hromadka, Samo, et al.
Published: (2025)

Learning Energy-Based Models by Self-normalising the Likelihood
by: Senetaire, Hugo, et al.
Published: (2025)

eMargin: Revisiting Contrastive Learning with Margin-Based Separation
by: Shamba, Abdul-Kazeem, et al.
Published: (2025)

Pathway-based Progressive Inference (PaPI) for Energy-Efficient Continual Learning
by: Gaurav, Suyash, et al.
Published: (2025)

Influence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models
by: Shinde, Aakash Ravindra, et al.
Published: (2025)

Bayesian network tool for analyzing the cost‐effectiveness of bulk forensic trace DNA profiling
by: Tuomas Korpinsalo, et al.
Published: (2026)

On Learning-Curve Monotonicity for Maximum Likelihood Estimators
by: Sellke, Mark, et al.
Published: (2025)

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
by: Zhu, Yaxuan, et al.
Published: (2023)

Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation
by: Chen, Xi, et al.
Published: (2026)