Image de couverture de livre

Approximating Heavy-Tailed Distributions with a Mixture of Bernstein Phase-Type and Hyperexponential Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ziani, Abdelhakim, Horváth, András, Ballarini, Paolo
Format:	Preprint
Publié:	2025
Sujets:	Performance Applications Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Phase-Type Variational Autoencoders for Heavy-Tailed Data
par: Ziani, Abdelhakim, et autres
Publié: (2026)

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models
par: Ziani, Abdelhakim, et autres
Publié: (2026)

Nonlinear and Heavy-Tailed Predictability in Transition-Energy Financial Markets
par: Gnandi, Kpante Emmanuel, et autres
Publié: (2026)

Bayesian Estimation of Extreme Quantiles and the Exceedance Distribution for Paretian Tails
par: Johnston, Douglas E.
Publié: (2025)

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
par: Chitty-Venkata, Krishna Teja, et autres
Publié: (2025)

Dispersion based Recurrent Neural Network Model for Methane Monitoring in Albertan Tailings Ponds
par: Saha, Esha, et autres
Publié: (2024)

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals
par: Arpogaus, Marcel, et autres
Publié: (2025)

Long-Tailed Data Classification by Increasing and Decreasing Neurons During Training
par: Sakai, Taigo, et autres
Publié: (2025)

Approximating Uniform Random Rotations by Two-Block Structured Hadamard Rotations in High Dimensions
par: Zilca, Tomer, et autres
Publié: (2026)

Neural Network-based Partial-Linear Single-Index Models for Environmental Mixtures Analysis
par: Do, Hyungrok, et autres
Publié: (2025)

Prediction-powered Inference by Mixture of Experts
par: Gu, Yanwu, et autres
Publié: (2026)

Leveraging Approximate Caching for Faster Retrieval-Augmented Generation
par: Bergman, Shai, et autres
Publié: (2025)

Combining Structural and Unstructured Data: A Topic-based Finite Mixture Model for Insurance Claim Prediction
par: Hou, Yanxi, et autres
Publié: (2024)

Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
par: Chu, Kexin, et autres
Publié: (2025)

Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networks
par: Iannario, Maria, et autres
Publié: (2025)

Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
par: Zhang, Chengyuan, et autres
Publié: (2024)

DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing
par: Wang, Liangyu, et autres
Publié: (2025)

Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data
par: Thakur, Debjoy
Publié: (2026)

Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
par: Fu, Kani, et autres
Publié: (2026)

A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling
par: Grosskopf, Michael, et autres
Publié: (2025)

Modeling Headway in Heterogeneous and Mixed Traffic Flow: A Statistical Distribution Based on a General Exponential Function
par: Leungbootnak, Natchaphon, et autres
Publié: (2025)

Summarizing Bayesian Nonparametric Mixture Posterior -- Sliced Optimal Transport Metrics for Gaussian Mixtures
par: Nguyen, Khai, et autres
Publié: (2024)

Benchmarking GPUs on SVBRDF Extractor Model
par: Kandel, Narayan, et autres
Publié: (2023)

Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
par: Zhou, Biying, et autres
Publié: (2025)

Biases in Edge Language Models: Detection, Analysis, and Mitigation
par: Sharma, Vinamra, et autres
Publié: (2025)

Memory Analysis on the Training Course of DeepSeek Models
par: Zhang, Ping, et autres
Publié: (2025)

An Interpretable Latency Model for Speculative Decoding in LLM Serving
par: Kong, Linghao, et autres
Publié: (2026)

Inductive Global and Local Manifold Approximation and Projection
par: Kim, Jungeum, et autres
Publié: (2024)

SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
par: Mozaffari, Mohammad, et autres
Publié: (2024)

lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
par: Wang, Haoxin, et autres
Publié: (2025)

Tail-adaptive Bayesian shrinkage
par: Lee, Se Yoon, et autres
Publié: (2020)

PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
par: An, Zihao, et autres
Publié: (2025)

CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting
par: Bonas, Matthew, et autres
Publié: (2024)

GPU-Accelerated INT8 Quantization for KV Cache Compression in Large Language Models
par: Taneja, Maanas, et autres
Publié: (2026)

GreenServ: Energy-Efficient Context-Aware Dynamic Routing for Multi-Model LLM Inference
par: Ziller, Thomas, et autres
Publié: (2026)

Unsupervised Latent Pattern Analysis for Estimating Type 2 Diabetes Risk in Undiagnosed Populations
par: Kumar, Praveen, et autres
Publié: (2025)

ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
par: Wang, Liangyu, et autres
Publié: (2025)

A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
par: Caruso, Camillo Maria, et autres
Publié: (2023)

NIST Post-Quantum Cryptography Standard Algorithms Based on Quantum Random Number Generators
par: Chen, Abel C. H.
Publié: (2025)

Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
par: Lansiaux, Edouard, et autres
Publié: (2025)