Cover Image

Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Suzuki, Atsushi, Fukuzawa, Kota, Yamanishi, Kenji
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory Information Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Normalized Maximum Likelihood Code-Length on Riemannian Data Spaces
by: Fukuzawa, Kota, et al.
Published: (2025)

Overfitting has a limitation: a model-independent generalization gap bound based on Rényi entropy
by: Suzuki, Atsushi, et al.
Published: (2025)

Graph Community Augmentation with GMM-based Modeling in Latent Space
by: Fukushima, Shintaro, et al.
Published: (2024)

Minimax Optimality of the Probability Flow ODE for Diffusion Models
by: Cai, Changxiao, et al.
Published: (2025)

Online Clustering of Data Sequences with Bandit Information
by: Chandran, G Dhinesh, et al.
Published: (2025)

Fixed-Confidence Best Arm Identification with Decreasing Variance
by: Roychowdhury, Tamojeet, et al.
Published: (2025)

Clustering Change Sign Detection by Fusing Mixture Complexity
by: Urano, Kento, et al.
Published: (2024)

A Method For Bounding Tail Probabilities
by: Zlatanov, Nikola
Published: (2024)

Generalization of LiNGAM that allows confounding
by: Suzuki, Joe, et al.
Published: (2024)

Variations on the Expectation due to Changes in the Probability Measure
by: Perlaza, Samir M., et al.
Published: (2025)

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

Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
by: Queric, Simon, et al.
Published: (2026)

Spectral Clustering for Directed Graphs via Likelihood Estimation on Stochastic Block Models
by: Zhang, Ning, et al.
Published: (2024)

Maximum entropy based testing in network models: ERGMs and constrained optimization
by: Ghosh, Subhro, et al.
Published: (2026)

MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
by: Tohme, Tony, et al.
Published: (2023)

Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood
by: Aragam, Bryon, et al.
Published: (2024)

Adaptation to Intrinsic Dependence in Diffusion Language Models
by: Zhao, Yunxiao, et al.
Published: (2026)

Minimax Hypothesis Testing for the Bradley-Terry-Luce Model
by: Makur, Anuran, et al.
Published: (2024)

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
by: Dmitriev, Daniil, et al.
Published: (2026)

Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models
by: Zhang, Yihan, et al.
Published: (2022)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
by: Li, Gen, et al.
Published: (2023)

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
by: Shi, Laixi, et al.
Published: (2023)

Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
by: Wu, Jingda, et al.
Published: (2026)

Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
by: Li, Gen, et al.
Published: (2025)

Consistency of Maximum Likelihood for Continuous-Space Network Models I
by: Shalizi, Cosma Rohilla, et al.
Published: (2017)

Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions
by: Zhang, Kaihong, et al.
Published: (2024)

Orthogonal Approximate Message Passing with Optimal Spectral Initializations for Rectangular Spiked Matrix Models
by: Chen, Haohua, et al.
Published: (2025)

Orthogonal Approximate Message Passing Algorithms for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
by: Chen, Haohua, et al.
Published: (2026)

Besting Good--Turing: Optimality of Non-Parametric Maximum Likelihood for Distribution Estimation
by: Han, Yanjun, et al.
Published: (2025)

Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
by: Zhu, Linglingzhi, et al.
Published: (2025)

Unifying AMP Algorithms for Rotationally-Invariant Models
by: Liu, Songbin, et al.
Published: (2024)

Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
by: Kim, Gilhan
Published: (2026)

Fine-Grained Uncertainty Quantification via Collisions
by: Friedbaum, Jesse, et al.
Published: (2024)

Efficient Unbiased Sparsification
by: Barnes, Leighton, et al.
Published: (2024)

Top-$K$ ranking with a monotone adversary
by: Yang, Yuepeng, et al.
Published: (2024)

Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of $Φ$-Mutual Information
by: Liang, Jiaming, et al.
Published: (2024)

Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos
by: Sosa-Cabrera, Gustavo
Published: (2024)

On the best approximation by finite Gaussian mixtures
by: Ma, Yun, et al.
Published: (2024)

Fast Convergence of $Φ$-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler
by: Mitra, Siddharth, et al.
Published: (2024)

Sparse Max-Affine Regression
by: Kanj, Haitham, et al.
Published: (2024)