Sample Complexity Courses and Certifications

TheIACR

Pseudorandom Knapsacks and the Sample Complexity of LWE Search-to-Decision Reductions

Explore pseudorandom knapsacks and LWE search-to-decision reductions, delving into their sample complexity and implications for cryptographic systems.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Google TechTalks

Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

Explore optimal algorithms and sample complexity for learning hierarchical tree representations from labeled data, covering PAC learning, online learning, and efficient tree classifier construction.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Sample Complexity of Estimation in Logistic Regression - Lecture

Explore sample complexity in logistic regression, examining parameter estimation across temperature regimes. Gain insights into non-asymptotic analysis and critical points in the complexity curve.

• YouTube
• 56 minutes
• On-Demand
• Free Video

BIMSA

Achieving Instance-Dependent Sample Complexity for Constrained Markov Decision Process

Explore advanced reinforcement learning for constrained Markov decision processes with optimal problem-dependent guarantees and novel primal-space algorithms.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Institute for Advanced Study

The Sample Complexity of Smooth Boosting and the Tightness of the Hardcore Theorem

Delve into the sample complexity of smooth boosting algorithms and their connection to Impagliazzo's hardcore theorem, exploring optimal parameters and necessary size loss in circuit complexity.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

HUJI Machine Learning Club

Mitigating the Curse of Detail - A Heuristic for Feature Learning and Sample Complexity

Discover a heuristic framework that bypasses analytical complexity in deep learning theory by focusing on scaling arguments to predict feature learning patterns.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Simons Institute

Sample-Complexity of Estimating Convolutional and Recurrent Neural Networks

Explore sample-complexity in estimating neural networks, focusing on CNN and RNN architectures. Learn about generative models, minimax analysis, and key results through theoretical proofs and practical experiments.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Information Theory of Deep Learning - Naftali Tishby

Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.

• YouTube
• 1 hour 25 minutes
• On-Demand
• Free Video

USC Probability and Statistics Seminar

Non-convex Matrix Sensing: Breaking the Quadratic Rank Barrier in Sample Complexity

Explore groundbreaking research on non-convex matrix sensing, focusing on improved algorithms for reconstructing low-rank matrices using fewer samples through innovative probabilistic decoupling methods.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Instituto de Matemática Pura e Aplicada

Multivariate Method of Moments: From Sample Complexity to Implicit Computations - 2023

Explore o método de momentos multivariados, sua complexidade amostral e cálculos implícitos na matemática aplicada e industrial, com insights da teoria pura.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Can Diffusion Model Disentangle? A Theoretical Perspective

Explore a theoretical framework for understanding how diffusion models learn disentangled representations, with identifiability conditions, training dynamics analysis, and experimental validation across diverse tasks and modalities.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Alan Turing Institute

Optimal Transport for Machine Learning - Gabriel Peyre, Ecole Normale Superieure

Explore optimal transport in machine learning, covering Kantorovitch's formulation, entropic regularization, and applications in generative models and GANs. Gain insights into cutting-edge advances in data science.

• YouTube
• 42 minutes
• On-Demand
• Free Video

International Mathematical Union

Learning Theoretic Foundations of Data-Driven Algorithm Design

Explore machine learning techniques for data-driven algorithm design, focusing on combinatorial problems and online optimization. Learn to adapt algorithms to specific domains for improved performance.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Simons Institute

Reinforcement Learning in Feature Space: Complexity and Regret

Explore reinforcement learning in feature space, focusing on complexity, regret analysis, and innovative approaches like MatrixRL. Learn about state feature mapping and its applications in real-world scenarios.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Simons Institute

Beyond Worst-Case Analysis - Panel Discussion

Experts discuss advanced algorithmic analysis, exploring beyond worst-case scenarios to improve efficiency and understanding in computer science and machine learning applications.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video