Learning Probability Distributions - What Can, What Can't Be Done - Shai Ben-David
Explore probability distribution learning, its possibilities and limitations. Shai Ben-David discusses density estimation, sample compression schemes, and challenges in statistical learning theory.
Meta-Learning - Why It’s Hard and What We Can Do - Ke Li
Explore meta-learning challenges and solutions with Ke Li, covering optimization, model selection, and program induction in this theoretical machine learning seminar.
Introduction to High Dimensional Expanders - Irit Dinur
Explore high dimensional expanders with Irit Dinur, covering random walks, spectral expansion, and hypergraphs. Gain insights into this advanced topic in discrete mathematics and computer science.
Localization Schemes - A Framework for Proving Mixing Bounds for Markov Chains
Explore a unified framework for proving mixing bounds in Markov chains, connecting Spectral Independence and Stochastic Localization techniques through the concept of localization schemes.
An Introduction to Determinantal Point Processes - John C Urschel
Explore determinantal point processes: definition, examples, and applications in random matrix theory, combinatorics, and machine learning. Survey key results and open questions.
Neural SDEs - Deep Generative Models in the Diffusion Limit - Maxim Raginsky
Explore deep generative models through stochastic control, diffusion limits, and neural nets. Learn about efficient sampling, expressiveness quantification, and unbiased simulation techniques.
Generative Modeling by Estimating Gradients of the Data Distribution - Stefano Ermon
Explore generative modeling through gradient estimation of data distributions, covering deep energy-based models, score matching, and innovative sampling techniques for improved AI generation.
Towards Structural Risk Minimization for RL - Emma Brunskill
Explore structural risk minimization in reinforcement learning, focusing on efficient learning of risk-sensitive policies using conditional value at risk and optimistic exploration techniques.
The General Case? - Amie Wilkinson
Explore ergodicity, Boltzmann's ideas, and recent developments in dynamical systems with mathematician Amie Wilkinson in this engaging seminar on the concept of "the general case".
Everything You Wanted to Know About Machine Learning but Didn't Know Whom to Ask - Sanjeev Arora
Explore machine learning fundamentals, from text classification to image recognition, with insights on optimization, unsupervised learning, and language models in this comprehensive seminar.
Simple Explanation of the Birthday Paradox - Understanding Probability and Mathematics
Uncover the surprising mathematics behind the Birthday Paradox and learn why only 23 people are needed for a likely birthday match, with clear explanations and intuitive reframing of probabilities.
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit
Explore risk-aware restless multi-arm bandits, indexability conditions, and Thompson sampling for bounded regret in resource distribution across Markov decision processes.
Probability Inspired by Non-equilibrium Statistical Physics
Explore probability theory through non-equilibrium statistical physics with insights from renowned mathematician Tadahisa Funaki's research on interacting systems and stochastic PDEs.
A Moment-Based Approach for the Analysis of the Infinitesimal Model
Explore a moment-based approach for analyzing the infinitesimal model in non-local branching processes with mathematician Sepideh Mirrahimi.
Naïve Bayes Algorithm in Hindi - Naïve Bayes Classifier Explained - Machine Learning
हिंदी में नाइव बेयस एल्गोरिथम की व्याख्या, जो मशीन लर्निंग में वर्गीकरण के लिए उपयोग किया जाता है। यह स्पैम फ़िल्टरिंग, टेक्स्ट विश्लेषण और चिकित्सा निदान जैसे अनुप्रयोगों पर केंद्रित है।
Constructing Physics-Consistent Neural Networks Using Probability Theory
Explore the link between probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
Boolean Models in Hyperbolic Space
Explore Boolean models in hyperbolic space, examining geometric functionals and asymptotic formulas for expectations, variances, and covariances, with insights into new phenomena compared to Euclidean cases.
Asymptotics of Optimal Constants in the Khinchine-Kahane Inequality
Explore the asymptotics of optimal constants in Khinchine-Kahane inequality, examining classical moment inequalities for Rademacher sums and recent developments in this century-old field of study.
Un Petit Aperçu des Matrices de Wigner
Explorez les matrices de Wigner et leurs applications en mathématiques avancées avec Sandrine Dallaporta de l'Université de Poitiers.
Statistical Estimation of Optimal Transport Potentials
Explorez les méthodes statistiques pour estimer les potentiels de transport optimal et leurs applications en analyse de données.
Barycenters for Probability Distributions Based on Optimal Weak Mass Transport
Explore optimal weak mass transport for probability distributions, focusing on barycenters and their applications in mathematical analysis.