Statistical Rethinking 2022 - Gaussian Processes
Explore Gaussian processes, spatial confounding, and phylogenetic inference in statistical modeling. Learn to apply these concepts to real-world examples like oceanic covariance and primate phylogeny.
Algorithms for Heavy-Tailed Statistics - Regression, Covariance Estimation, and Beyond
Explore advanced statistical techniques for handling heavy-tailed data, including regression and covariance estimation, with a focus on SOS assumptions and median of means framework.
Computation in Very Wide Neural Networks
Explore infinite-width neural networks, their correspondence to Gaussian processes, and implications for deep learning performance and dynamics in parameter space.
Assisting 4D-STEM Data Processing by Machine Learning and Bayesian Optimization
Explore machine learning and Bayesian optimization techniques for processing 4D-STEM data, enhancing efficiency in materials analysis and electron ptychography reconstruction.
Methods for Scalable Probabilistic Inference - IPAM at UCLA
Explore scalable probabilistic inference methods for astrophysics, focusing on Gaussian Processes for time series analysis and open-source tools to accelerate Bayesian inference with large datasets.
Hyperparameter Optimization - This Tutorial Is All You Need
Explore various hyperparameter optimization techniques including Grid Search, Random Search, Bayesian Optimization, Hyperopt, and Optuna to fine-tune models and optimize functions effectively.
Learning What We Know and Knowing What We Learn - Gaussian Process Priors for Neural Data Analysis
Explore Gaussian process priors for neural data analysis, covering latent variable models, Bayesian inference, covariance kernels, and factor analysis. Gain insights into advanced techniques for understanding complex neural datasets.
Data is as Data Does - The Influence of Computation on Inference
Exploring the impact of computational approximations on probabilistic models in machine learning, with focus on Gaussian Process approximations and their applications in neurobiology.
Explorations in Exploration: Deep Learning Meets Value of Information for Sequential Experimental Design
Exploring machine learning's role in sequential experimental design, focusing on quantifying information value and improving flexibility in planning future experiments.
Some Thoughts on Gaussian Processes for Emulation of Deterministic Computer Models
Explore Gaussian Process emulators for complex computer models in uncertainty quantification, focusing on theoretical aspects and applications to climate, tsunami, and earthquake problems.
A Few Quantitative Thoughts on Parking in Marburg - RC3 - 2020
Analyse des données de stationnement à Marburg : tendances, modèles et prévisions pour optimiser l'utilisation des espaces de stationnement et améliorer la planification urbaine.
Deep Probabilistic Modelling with Pyro
Explore deep probabilistic modeling with Pyro, combining probabilistic methods and neural networks for better uncertainty handling and decision-making in machine learning applications.
ML-Enabled Genetic Association Studies of High-Content Phenotypes
Explore cutting-edge machine learning applications in genetic association studies, focusing on high-content phenotypes and innovative frameworks like HistoGWAS for analyzing tissue phenotypes in histology cohorts.
Functional Causal Bayesian Optimization - Lecture 3
Explore functional causal Bayesian optimization for optimizing interventions in causal graphs. Learn about extending CBO methods, modeling with Gaussian processes, and maximizing expected improvement.
Vanilla Bayesian Optimization Performs Great in High Dimensions
Discover how scaling Gaussian process lengthscale prior with dimensionality enables vanilla Bayesian optimization to excel in high-dimensional optimization problems, outperforming complex alternatives.
Probabilistic Richardson Extrapolation
Explore probabilistic Richardson extrapolation using Gaussian processes for accelerated convergence in numerical methods, with applications in cardiac modeling and statistical experimental design.
Robust Design Discovery and Exploration in Bayesian Optimization
Explore data-driven methods for robust design discovery and optimization, focusing on Bayesian techniques that efficiently handle uncertainties and adversarial scenarios in various scientific domains.
A Connection between Probability, Physics and Neural Networks
Explore the intersection of probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
Recent Progress on Grokking and Probabilistic Federated Learning
Explore recent advancements in grokking phenomenon and probabilistic federated learning. Gain insights into neural networks, Gaussian processes, and Bayesian inference for machine learning applications.
Geometry of Covariance Matrices, Covariance Operators, and Gaussian Processes
Explore geometric approaches to covariance matrices, operators, and Gaussian processes. Gain insights into non-Euclidean distances, Fisher-Rao distance, and Wasserstein distance in infinite-dimensional settings.
Bayesian Optimization: Exploiting Machine Learning Models, Physics, and Throughput Experiments
Explore new Bayesian Optimization paradigms leveraging machine learning, physics, and high-throughput experiments. Learn to accelerate searches, enable parallel exploration, and utilize large-scale models for various applications.