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#Statistical Learning Theory

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Broad Institute

The Challenges of Training Infinitely Large Neural Networks

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Explore challenges in training infinitely large neural networks with Mikhail Belkin. Gain insights into foundational advances in machine learning for biomedical applications.
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1 Lesons
1 hour 6 minutes
On-Demand
Free-Video
Simons Institute

Does Generalization Imply Accuracy on the Line? A New Look at Robust Generalization

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Explore the relationship between generalization and accuracy in machine learning, examining robust generalization through a fresh perspective.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
Simons Institute

Generalization from the Behavioral Perspective

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Dive into the behavioral perspective of generalization, exploring emerging settings and their implications in machine learning and data analysis.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
Institute for Advanced Study

Statistical Learning Theory for Modern Machine Learning - John Shawe-Taylor

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Explore statistical learning theory's role in modern machine learning, covering PAC-Bayes framework, risk measures, and deep network training experiments with John Shawe-Taylor.
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21 Lesons
1 hour 12 minutes
On-Demand
Free-Video
Institute for Advanced Study

Convergence of Nearest Neighbor Classification - Sanjoy Dasgupta

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Explore nearest neighbor classification's convergence, consistency, and accuracy rates in various spaces, with insights on smoothness conditions and adaptive classifiers.
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18 Lesons
49 minutes
On-Demand
Free-Video
ANITI Toulouse

ANITI DAYS 2022 - Artificial Intelligence Research Highlights

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Découvrez les avancées en apprentissage par renforcement et leur application dans les systèmes autonomes avec Sébastien Gerchinovitz d'ANITI Toulouse.
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1 Lesons
16 minutes
On-Demand
Free-Video
Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 1

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Explore regularization techniques using label permutation in mathematical statistics, focusing on innovative approaches and their applications.
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1 Lesons
52 minutes
On-Demand
Free-Video
Centre International de Rencontres Mathématiques

Regularization via Permutation of Labels - Lecture 2

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Explore advanced statistical techniques for regularization through label permutation, focusing on mathematical theory and practical applications.
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1 Lesons
43 minutes
On-Demand
Free-Video
Alan Turing Institute

Measuring the Leakage of a Black-Box Using Machine Learning - Giovanni Cherubin

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Explore machine learning techniques for measuring information leakage in black-box systems, focusing on privacy and anonymity protocols in website fingerprinting attacks.
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1 Lesons
54 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 1

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Explore techniques for learning from dependent data, focusing on probabilistic and optimization methods in data science. Gain insights into handling interconnected information effectively.
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1 Lesons
51 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Almost Optimal Variance-Constrained Best Arm Identification - Lecture 2

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Explores advanced techniques in variance-constrained best arm identification, focusing on optimal algorithms and theoretical analysis for efficient decision-making in multi-armed bandit problems.
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1 Lesons
46 minutes
On-Demand
Free-Video
Georgia Tech Research

Mathematics for Deep Neural Networks: Introduction - Lecture 1 of 5

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Explore mathematical foundations of deep neural networks, including algorithms, data fitting, and theoretical approaches in this comprehensive overview.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Part 1 of 4

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Dive into mathematical probability fundamentals for machine learning through Stanford expert Andrea Montanari's comprehensive exploration of key theoretical concepts and applications.
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1 Lesons
1 hour 45 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 3

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Delve into advanced mathematical probability concepts and their applications in machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration.
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1 Lesons
1 hour 59 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 2

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Delve into mathematical probability fundamentals and their applications in machine learning through advanced theoretical concepts and problem-solving approaches from Stanford expertise.
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1 Lesons
1 hour 54 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 4

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Delve into advanced mathematical probability concepts applied to machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration and analysis.
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1 Lesons
1 hour 47 minutes
On-Demand
Free-Video
Centre for Networked Intelligence, IISc

Adaptive Multi-armed Bandit Algorithms for Markovian and IID Rewards

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Explore adaptive multi-armed bandit algorithms and their applications in both Markovian and i.i.d reward scenarios, focusing on regret guarantees and novel algorithmic approaches.
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1 Lesons
55 minutes
On-Demand
Free-Video
UofU Data Science

Learning as Loss Minimization in Machine Learning - Lecture 23

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Delve into the fundamental concept of learning as optimization, exploring how empirical risk minimization forms the foundation of machine learning algorithms.
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1 Lesons
44 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding VC Dimensions and Computational Learning Theory - Lecture 18

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Delve into advanced computational learning theory concepts and VC dimensions, exploring fundamental principles that shape machine learning algorithm capabilities and limitations.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Shattering and VC Dimension - Lecture 17B

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Delve into computational learning theory, focusing on shattering concepts and VC dimension fundamentals in machine learning through theoretical frameworks and practical applications.
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1 Lesons
30 minutes
On-Demand
Free-Video
Data Science Courses

PAC Learnability in Deep Learning - Lecture 20

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Delve into statistical machine learning fundamentals, exploring PAC learnability concepts and their application in deep learning, with focus on generalization theories and practical challenges.
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1 Lesons
44 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Vapnik-Chervonenkis Theorem and Statistical Learning Theory

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Dive into the fundamental Vapnik-Chervonenkis theorem, exploring how VC-dimension relates to machine learning generalization and error rates in hypothesis spaces.
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1 Lesons
2 hours 35 minutes
On-Demand
Free-Video
UofU Data Science

Support Vector Machines and Maximizing Margins in Machine Learning - Lecture 20

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Dive into support vector machines, exploring margin maximization, linear classifiers, and regularized risk minimization for advanced machine learning applications.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
UofU Data Science

Supervised Learning: The Setup and Hypothesis Space Selection - Lecture 3

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Delve into supervised learning concepts, focusing on hypothesis space selection and its crucial role in building effective machine learning models.
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1 Lesons
44 minutes
On-Demand
Free-Video
UofU Data Science

How Good is a Learning Algorithm? Quantifying the Learning Process - Lecture 7B

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Explore the quantitative aspects of machine learning by examining how many examples are needed to effectively discover and validate hypotheses.
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1 Lesons
27 minutes
On-Demand
Free-Video
UofU Data Science

Boosting and Ensemble Methods in Machine Learning - Lecture 18

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Dive into boosting algorithms and learn how to combine weak learning rules to create powerful predictive models in machine learning applications.
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1 Lesons
35 minutes
On-Demand
Free-Video
UofU Data Science

The VC Dimension in Machine Learning - Lecture 18

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Delve into the fundamental concept of VC dimension in machine learning theory, exploring its relationship with shattering and its implications for learning algorithms.
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1 Lesons
41 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17

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Delve into the concept of shattering and its application in analyzing infinite hypothesis spaces within machine learning theory and computational learning frameworks.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
Simons Institute

Overview of Statistical Learning Theory - Part 2

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Explore fundamental concepts in statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theory and modern applications.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
Simons Institute

Overview of Statistical Learning Theory - Part 1

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Explore fundamental concepts of statistical learning theory, including capacity control, generalization, and optimization techniques. Gain insights into classic theories and their relevance to modern machine learning paradigms.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Simons Institute

Reconsidering Overfitting in the Age of Overparameterized Models

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Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.
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1 Lesons
1 hour 18 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 2

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Explores techniques for analyzing and learning from dependent data, focusing on probabilistic and optimization methods in data science.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
MITCBMM

Regularized Least Squares

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Explore statistical learning theory with Lorenzo Rosasco, focusing on regularized least squares, optimization techniques, and key concepts in machine learning algorithms.
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8 Lesons
1 hour 20 minutes
On-Demand
Free-Video
Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

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Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.
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14 Lesons
1 hour 38 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Generalization Theory in Machine Learning

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Explore generalization theory in machine learning, covering traditional methods, deep learning, and statistical learning theory. Gain insights into overcoming the curse of dimensionality and understanding learning bounds.
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24 Lesons
1 hour 11 minutes
On-Demand
Free-Video
Simons Institute

Statistical Learning Theory and Neural Networks II

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Explores advanced concepts in statistical learning theory and neural networks, focusing on optimization perspectives, gradient descent analysis, and the neural tangent kernel regime.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Simons Institute

Statistical Learning Theory and Neural Networks

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Explore statistical learning theory for deep neural networks, covering uniform laws, Rademacher complexity, and optimization perspectives including gradient descent and neural tangent kernel regime.
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1 Lesons
59 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Advances in Applied Probability II

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Explore cutting-edge research in applied probability, covering statistical learning, high-dimensional computation, Monte Carlo methods, discrete probability, percolation, and empirical methods. Gain insights from leading experts.
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1 Lesons
3 hours 2 minutes
On-Demand
Free-Video
MITCBMM

Interpolation and Learning With Scale Dependent Kernels

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Explore scale-dependent kernels in machine learning, covering overfitting, statistical theory, and practical applications in regression and interpolation.
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11 Lesons
1 hour 4 minutes
On-Demand
Free-Video
MITCBMM

Statistical Learning Theory and Applications - Class 16

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Explore statistical learning theory, focusing on complexity, dual norm equality, and calligraphic G. Gain insights into advanced concepts and their applications in machine learning.
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6 Lesons
1 hour 20 minutes
On-Demand
Free-Video
MITCBMM

Statistical Learning Theory and Applications - Class 6

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Explore statistical learning theory with focus on Newton's method, recursive computation, and online methods for efficient problem-solving and data analysis.
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8 Lesons
1 hour 33 minutes
On-Demand
Free-Video
MITCBMM

Statistical Learning Theory and Applications - Class 7

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Explore statistical learning theory, covering least squares, gradient descent, regularization, and induction, with insights into historical context and practical applications.
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8 Lesons
1 hour 27 minutes
On-Demand
Free-Video
MITCBMM

Generalization Error and Stability

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Explore statistical learning theory, focusing on generalization error, stability, and empirical risk minimization. Gain insights into universal consistency and the law of large numbers.
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8 Lesons
1 hour 21 minutes
On-Demand
Free-Video
IEEE

Understanding Deep Learning - Challenges for Statistical Learning Theory

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Explore statistical learning theory challenges in deep learning, focusing on key concepts and recent developments in this complex field.
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1 Lesons
1 hour 28 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Learning from Dependent Data - Lecture 1

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Explore techniques for learning from dependent data, focusing on probabilistic and optimization methods in data science. Gain insights into handling interconnected information effectively.
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1 Lesons
47 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Quantum Statistical Query Learning II of II - IPAM at UCLA

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Explores quantum statistical query learning, comparing it to quantum PAC models. Discusses lower bounds, separation results, and potential applications in quantum learning theory.
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1 Lesons
44 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

On the Variance and Admissibility of Empirical Risk Minimization on Convex Classes

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Explores empirical risk minimization's variance and admissibility in function estimation, proving its minimax optimal variance and admissibility using concentration of measure techniques in convex geometry.
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1 Lesons
51 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Robust Learning of a Single Neuron - Bridging Computational Gaps Using Optimization

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Explore robust learning of a single neuron with ReLU activation, focusing on optimization techniques to achieve near-optimal mean square loss in adversarial settings. Discover novel approaches for various activation functions.
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1 Lesons
50 minutes
On-Demand
Free-Video
Simons Institute

Testing Assumptions of Learning Algorithms

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Explore how property testing can mitigate reliance on distributional assumptions and no distribution shift in supervised learning algorithms, ensuring more reliable theoretical guarantees.
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1 Lesons
59 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Random Tessellation Forests: Overcoming the Curse of Dimensionality

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Explore random tessellation forests in machine learning, overcoming dimensionality challenges through innovative partitioning methods and directional distributions for improved statistical performance.
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1 Lesons
58 minutes
On-Demand
Free-Video
IEEE

Learning to Reconstruct - Statistical Learning Theory and Encrypted Database Attacks

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Explores the connection between statistical learning theory and encrypted database attacks, presenting novel approaches for database reconstruction from access pattern leakage with improved efficiency and broader applicability.
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1 Lesons
21 minutes
On-Demand
Free-Video
UofU Data Science

Boosting and Ensembles in Machine Learning - Lecture 18b

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Discover how to transform weak learning algorithms into powerful predictive models through boosting and ensemble techniques in machine learning.
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1 Lesons
14 minutes
On-Demand
Free-Video
Conference GSI

Can Generalized Divergences Help for Invariant Neural Networks?

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Explore the potential of generalised divergences in enhancing invariant neural networks, examining their impact on network performance and robustness.
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1 Lesons
21 minutes
On-Demand
Free-Video
HUJI Machine Learning Club

Local Glivenko-Cantelli: Estimating the Mean in Infinite Dimensions

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Delve into advanced statistical theory exploring mean estimation in infinite dimensions, focusing on Local Glivenko-Cantelli bounds and maximal deviation analysis for Boolean cube distributions.
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1 Lesons
59 minutes
On-Demand
Free-Video
HUJI Machine Learning Club

Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models

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Delve into high-dimensional mean estimation in binary Markov Gaussian mixture models, exploring how memory in data affects estimation error and the interplay between sample size, dimension, and signal strength.
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1 Lesons
59 minutes
On-Demand
Free-Video
UofU Data Science

Supervised Learning: The Setup and Hypothesis Space - Lecture 3

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Delve into supervised learning concepts, focusing on hypothesis space selection and its crucial role in machine learning model development and performance optimization.
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6 Lesons
33 minutes
On-Demand
Free-Video
Georgia Tech Research

Compatibility and the Lasso - Lecture 2

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Explore compatibility and the Lasso in statistical learning with Sara van de Geer from ETH Zurich in this distinguished lecture on advanced machine learning techniques.
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1 Lesons
57 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Overfitting: Benign, Tempered and Harmful - Lecture on Machine Learning Regularization

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Explore the nuanced understanding of overfitting in neural networks, examining benign, tempered, and harmful effects on model performance based on data properties and training conditions.
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1 Lesons
51 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Part 2 of 4

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Explore mathematical foundations of machine learning with Andrea Montanari, focusing on key problems and theoretical concepts in the field.
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1 Lesons
54 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Part 1 of 4

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Explore mathematical foundations of machine learning with Andrea Montanari, focusing on key problems and theoretical concepts in the field.
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1 Lesons
1 hour 5 minutes
On-Demand
Free-Video
VinAI

Benign Overfitting in Linear Regression and Deep Learning

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Explore benign overfitting in deep learning with Peter Bartlett. Uncover insights on linear regression, effective rank, and overparameterization in neural networks for optimal prediction accuracy.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
Simons Institute

Modern Paradigms of Generalization - The Heliocentric Model of Aristarchus

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Explore modern generalization paradigms and historical scientific models in this insightful lecture on machine learning theory and its historical parallels.
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1 Lesons
1 hour 10 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Exploiting Sparsity and Structure in Parametric and Nonparametric Estimation - 2007

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Explore advanced techniques in sparse estimation for graphical models and nonparametric regression, including l1 regularization and derivative regularization for high-dimensional data analysis.
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1 Lesons
1 hour 5 minutes
On-Demand
Free-Video
Simons Institute

Robust Optimization and Generalization

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Explore robust optimization's history and its modern machine learning applications, focusing on performance certification and practical challenges in ML systems.
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1 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

The Elusive Generalization and Easy Optimization in Machine Learning - Part 1

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Explore generalization in machine learning and optimization by gradient descent, focusing on recent developments and empirical findings in neural networks that challenge traditional theoretical foundations.
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1 Lesons
1 hour 21 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

The Elusive Generalization and Easy Optimization - Part 2

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Explore generalization in machine learning and optimization of non-convex systems through gradient descent, focusing on recent developments and empirical findings in neural networks.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
MITCBMM

Open Problems in the Theory of Deep Learning - Panel Discussion

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Explore cutting-edge perspectives on deep learning theory with leading experts, addressing unresolved questions and future research directions in this dynamic field.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
MITCBMM

Towards Understanding the Implicit Regularization Effect of SGD

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Explore the implicit regularization effect of Stochastic Gradient Descent, uncovering its impact on optimization and generalization in machine learning models.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 4/4

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Explore advanced mathematical concepts in machine learning, focusing on complex problem-solving techniques and theoretical foundations.
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1 Lesons
2 hours 6 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 3/4

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Explore mathematical challenges in machine learning with Andrea Montanari, delving into advanced concepts and problem-solving techniques.
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1 Lesons
2 hours 12 minutes
On-Demand
Free-Video
IPhT-TV

Mathematical Problems in Machine Learning - Lecture 2/4

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Explore mathematical challenges in machine learning, focusing on advanced concepts and problem-solving techniques in this comprehensive lecture series.
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1 Lesons
2 hours 17 minutes
On-Demand
Free-Video
Centre International de Rencontres Mathématiques

High-Dimensional Classification with Deep Neural Networks: Decision Boundaries

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Explore high-dimensional classification theory, focusing on decision boundaries, noise, and margin conditions in neural networks, with applications to efficient approximation and learning rates.
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1 Lesons
45 minutes
On-Demand
Free-Video
Simons Institute

Transfer Learning via Local Convergence Rates of the Nonparametric Least Squares Estimator

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Explore nonparametric least squares estimator's local convergence properties and their implications for transfer learning under covariate shift, focusing on weighted uniform norm optimization.
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1 Lesons
41 minutes
On-Demand
Free-Video
Simons Institute

Label Noise and Machine Learning: Why Ignorance is Bliss

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Explore theoretical frameworks for handling multi-class label noise in machine learning, focusing on relative signal strength and empirical risk minimization principles.
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1 Lesons
42 minutes
On-Demand
Free-Video
Simons Institute

A Discrepancy-Based Theory of Domain Adaptation

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Delve into domain adaptation theory through discrepancy concepts, exploring sample reweighting techniques and algorithmic applications for robust machine learning solutions.
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1 Lesons
54 minutes
On-Demand
Free-Video