maximum likelihood estimation
PulseAugur coverage of maximum likelihood estimation — every cluster mentioning maximum likelihood estimation across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
-
Machine Learning Engineer Interview Prep: CI/CD, Kafka, Kubernetes
This item is a request for technical interview questions related to machine learning engineering roles, specifically focusing on CI/CD, Apache Kafka, and Kubernetes. The user is seeking advice on preparing for live stre…
-
Deep learning framework NeuroMem-FHP enhances parameter estimation for fractional Hawkes process
Researchers have developed NeuroMem-FHP, a deep learning framework designed to estimate parameters for the fractional Hawkes process (FHP). This framework utilizes Long Short-Term Memory (LSTM) and Transformer neural ne…
-
New framework unifies shrinkage and thresholding estimators in normal mean problems
Researchers have developed a new framework for approximate risk minimization in normal mean estimation problems, introducing an estimator called NOMAD. This framework unifies various shrinkage and thresholding rules, in…
-
Bayesian Optimization needs optimal initial points, study finds
A new paper on arXiv explores the optimal number of initial points required for Bayesian Optimization (BO). The research indicates that the total cost of finding a global optimum exhibits a U-shaped relationship with th…
-
New research links MLE and control variates in machine learning algorithms
Researchers have established a theoretical equivalence between Maximum Likelihood Estimation (MLE) and Control Variate Estimators (CVE) within sketching algorithms for machine learning. This equivalence, demonstrated un…
-
New gradient descent scheme improves MMD estimation
Researchers have introduced a new preconditioned gradient descent (PGD) scheme to address the poorly understood optimization problem in Minimum Maximum Mean Discrepancy (MMD) estimation. This novel approach establishes …
-
New Targeted Highly Adaptive Lasso method improves statistical estimation
Researchers have introduced a new statistical method called Targeted Highly Adaptive Lasso (Targeted HAL) for estimating non-pathwise differentiable functional parameters, such as dose-response curves. This method utili…
-
New Risk Alignment Framework Improves AI Model Calibration
Researchers have introduced Risk Alignment (RA), a new framework for selecting the optimal bandwidth in Kernel Density Estimation (KDE) for model calibration. Standard methods like Maximum Likelihood Estimation (MLE) of…
-
New Risk Alignment Framework Improves Deep Learning Model Calibration
Researchers have developed a new framework called Risk Alignment (RA) to improve the calibration of deep learning models, which is crucial for high-stakes applications. RA addresses the challenge of selecting the optima…
-
New XMSE-Aware Mixed Estimator Blends ML and Empirical Bayes
Researchers have developed a novel XMSE-aware mixed estimator that interpolates between maximum likelihood (ML) and Empirical Bayes (EB) shrinkage. This approach aims to improve upon existing EB estimators, which can un…
-
New attack reveals vulnerability in common AI ranking systems
Researchers have identified a significant vulnerability in Maximum Likelihood Estimation (MLE)-based ranking systems, such as the Bradley-Terry model, which are commonly used to aggregate preferences from pairwise compa…
-
New papers explore optimal transport for ML inference
Two new arXiv papers explore advanced inference techniques in machine learning. One paper benchmarks likelihood-free inference methods, evaluating their performance with heavy-tailed and discrete data. The other paper b…
-
Researchers detail exact recovery for community detection in dependent Gaussian mixture models
This paper investigates the problem of exact recovery for community detection within Gaussian mixture models. The research focuses on scenarios with dependent and heterogeneous Gaussian noise, where the noise covariance…
-
New 'Noisier' NCE method improves density-ratio estimation for AI models
Researchers have developed a modified Noise Contrastive Estimation (NCE) technique called "Noisier" NCE, which addresses limitations in estimating density ratios for complex datasets. By artificially increasing the nois…