Machine Learning Engineer: 6th May 2025

Hyperparameter Tuning is just a Resource Scheduling Problem (jchandra.com, 2025-05-02). Hyperparameter tuning involves resource scheduling to efficiently manage time and computational power, utilizing methods like ASHA, Hyperband, and Population-Based Training for optimal performance and resource allocation

Correlation-Based Clustering: Spectral Clustering Methods (portfoliooptimizer.io, 2025-05-01). Explore correlation-based spectral clustering methods like Blockbuster and SPONGE for grouping assets, leveraging affinity matrices, Laplacians, and eigenvectors to analyze market behaviors without external classifications

Some vector search basics (softwaredoug.com, 2025-05-03). Concepts of vector search, focusing on HNSW data structures. Key elements include embeddings, clustering, quantization, and graph traversal techniques for efficiently locating nearest neighbors in high-dimensional vector spaces

AI Anomaly Detection Systems: Architectures and Implementation (ataiva.com, 2025-05-05). Explore architectures and implementation strategies for AI-powered anomaly detection systems using Machine Learning, statistical methods, and data processing techniques for various operational and security applications

How to Fit Monotonic Smooths in JAX using Shape Constrained Additive Models (mattmills49.github.io, 2025-05-03). Utilize Shape Constrained Additive Models (SCAM) in JAX for monotonic trends like power usage and CO2 emissions over time, implementing penalized splines and B-splines for constrained modeling

Adventures in Imbalanced Learning and Class Weight (andersource.dev, 2025-05-05). An analysis of class weighting in binary classification shows that classic 'inverse proportion' weighting may not improve model performance and often leads to detrimental outcomes in imbalanced datasets

Model Diagnostics: Statistics vs Machine Learning (lorentzen.ch, 2025-05-01). This post contrasts statistical inference and predictive modeling using a linear model on Munich's rent data, utilizing tools like Generalized Linear Regressors and diagnostic methods like residual plots and reliability diagrams

Survival stacking: survival analysis translated as supervised classification in R and Python (thierrymoudiki.github.io, 2025-05-05). Survival stacking provides a method for using any classifier in survival analysis, utilizing the 'survivalist' package in R and Python for data transformations and model fitting with machine learning algorithms

Why I linkage (11011110.github.io, 2025-04-30). Linkage roundup exploring Roman dodecahedra, mathematical curiosities, AI-generated speech, academic conference challenges, and technology controversies across diverse scholarly domains

Beyond Glorified Curve Fitting: Exploring the Probabilistic Foundations of Machine Learning (towardsdatascience.com, 2025-05-01). Exploring probabilistic machine learning reveals its fundamental role in creating robust, explainable AI. Key concepts such as supervised learning, unsupervised learning, reinforcement learning, and the No Free Lunch Theorem are discussed

From a Point to L∞ (towardsdatascience.com, 2025-05-02). Explore how L¹, L², and L∞ norms differ in shaping AI models, including their roles in GANs and regularization techniques like Lasso and Ridge, impacting accuracy and generalization in machine learning

lskm-2007 - [Contributors] (leon.bottou.org, 2025-04-30). Large Scale Kernel Machines explores techniques for scalable learning algorithms that process large datasets efficiently, addressing statistical efficiency and theoretical grounding, specifically support vector machine technology and various optimization techniques

Optimal Transport on Categorical Data for Counterfactuals, at IJCAI’25 (freakonometrics.hypotheses.org, 2025-05-04). Innovative method for transporting categorical data using compositional techniques and probabilistic simplex, aimed at addressing counterfactual fairness challenges in machine learning algorithms

Bilateral Differentially Private Vertical Federated Boosted Decision Trees (arxiv:cs, 2025-04-30). MaskedXGBoost is a federated learning approach that incorporates bilateral differential privacy into vertical XGBoost, enhancing privacy protection with calibrated noise while maintaining high utility and lower computational overhead compared to encryption methods

Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis (arxiv:cs, 2025-05-01). Research evaluates six ML classifiers for osteoporosis risk prediction, with XGBoost achieving 91% accuracy. Integrates XAI techniques like SHAP and LIME, identifying age as a key risk factor

GPRat: Gaussian Process Regression with Asynchronous Tasks (arxiv:cs, 2025-04-30). GPRat is a parallel Gaussian process library utilizing asynchronous HPX C++ code bound to Python via pybind11, demonstrating significant speedups in training and prediction over GPyTorch and GPflow on AMD EPYC 7742 CPU

Hypothesis-free discovery from epidemiological data by automatic detection and local inference for tree-based nonlinearities and interactions (arxiv:cs, 2025-05-01). RuleSHAP is proposed for hypothesis-free discovery in epidemiology, integrating sparse Bayesian regression, tree ensembles, and Shapley values to detect complex patterns like nonlinear interactions among various health indicators

Empirical Evaluation of Progressive Coding for Sparse Autoencoders (arxiv:cs, 2025-04-30). Sparse autoencoders (SAEs) utilize dictionary learning for interpretability in unsupervised tasks. Comparison reveals Matryoshka SAEs outperform in reconstruction and language modeling, while pruned vanilla SAEs offer greater interpretability, highlighting a key trade-off

Unemployment Dynamics Forecasting with Machine Learning Regression Models (arxiv:econ, 2025-05-03). Explored seven machine learning models, including CatBoost and LSTM, for forecasting U.S. unemployment using macro indicators, labor data, and financial variables, with tree-based ensembles outperforming linear methods in accuracy

Dimension 126 Contains Twisted Shapes, Mathematicians Prove (quantamagazine.org, 2025-05-05). Mathematicians have proven that dimension 126 can host anomalous twisted shapes, utilizing techniques like surgery and Kervaire invariants, concluding a 65-year quest to classify strange manifolds in various dimensions