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#Sample Complexity

Showing: 35 courses
Stanford University

Information Theory of Deep Learning - Naftali Tishby

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Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.
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11 Lesons
1 hour 25 minutes
On-Demand
Free-Video
Simons Institute

Iterated Models: Expressive Power, Learning, and Chain of Thought

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Explore sequence-to-sequence models, focusing on transformers, their computational power, and learning complexities. Gain insights into iterated functions and chain of thought.
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1 Lesons
54 minutes
On-Demand
Free-Video
Simons Institute

On the Curses of Future and History in Off-policy Evaluation in Non-Markov Environments

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Delve into off-policy evaluation in non-Markov environments, exploring coverage concepts, future-dependent value functions, and novel estimators for partially observable MDPs with polynomial sample complexity guarantees.
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1 Lesons
56 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Reinforcement Learning: An Introduction and Key Results

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Découvrez les principes et applications du Reinforcement Learning, de la détection d'anomalies médicales à l'optimisation des processus décisionnels de Markov. Explorez les défis et solutions de cette approche d'apprentissage par interaction.
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8 Lesons
51 minutes
On-Demand
Free-Video
Instituto de Matemática Pura e Aplicada

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

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Explore o método de momentos multivariados, sua complexidade amostral e cálculos implícitos na matemática aplicada e industrial, com insights da teoria pura.
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1 Lesons
53 minutes
On-Demand
Free-Video
Fields Institute

Learning Linear Dynamical Systems with Hankel Nuclear Norm Regularization

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Explore finite-sample analysis and insights for learning low-order linear dynamical systems using Hankel nuclear norm regularization, with applications in control and reinforcement learning.
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1 Lesons
35 minutes
On-Demand
Free-Video
Simons Institute

Learning Probability Distributions: What Can and Can't Be Done

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Explore statistical learning, focusing on guaranteed generalizations from finite samples and characterizing learnable distribution families. Gain insights into modern paradigms in generalization.
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1 Lesons
1 hour 12 minutes
On-Demand
Free-Video
Simons Institute

Hypothesis Selection with Computational Constraints

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Explore optimal tradeoffs between memory, sample complexity, and time in hypothesis selection algorithms for distribution matching, with focus on achieving near-optimal accuracy efficiently.
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1 Lesons
32 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Proper vs Improper Quantum PAC Learning

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Explore quantum PAC learning theory through a comparative analysis of proper and improper learning methods, focusing on the Quantum Coupon Collector problem and its padded variant.
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1 Lesons
20 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Provable Advantage in Quantum PAC Learning

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Discover groundbreaking research on quantum PAC learning, exploring provable advantages over classical methods with square root improvements in sample complexity and theoretical quantum information science.
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1 Lesons
24 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Closed-form Analytic Expressions for Shadow Estimation with Brickwork Circuits

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Explore closed-form analytical expressions for quantum shadow estimation using brickwork circuits, focusing on two-layer parallel two-local Haar-random unitaries and their practical implementations.
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1 Lesons
13 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
UofU Data Science

Machine Learning: Positive and Negative Learnability Results - Lecture 16

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Explore key theoretical foundations of machine learning through positive and negative learnability results, enhancing your understanding of learning theory fundamentals.
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1 Lesons
53 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

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

PAC Learning in Machine Learning Theory - Lecture 13

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Explore the fundamental concepts of PAC (Probably Approximately Correct) learning theory and its applications in theoretical machine learning frameworks.
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1 Lesons
41 minutes
On-Demand
Free-Video
Simons Institute

Beyond Worst-Case Analysis - Panel Discussion

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Experts discuss advanced algorithmic analysis, exploring beyond worst-case scenarios to improve efficiency and understanding in computer science and machine learning applications.
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14 Lesons
1 hour 10 minutes
On-Demand
Free-Video
Alan Turing Institute

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

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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.
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15 Lesons
42 minutes
On-Demand
Free-Video
Simons Institute

Reinforcement Learning in Feature Space: Complexity and Regret

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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.
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23 Lesons
45 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

Exploration with Limited Memory - Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits

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Explore streaming algorithms for coin tossing, noisy comparisons, and multi-armed bandits, focusing on limited memory scenarios and efficient problem-solving techniques.
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16 Lesons
24 minutes
On-Demand
Free-Video
Simons Institute

Reinforcement Learning - Part I

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Explore reinforcement learning fundamentals, including credit assignment, dynamic programming, and exploration algorithms. Gain insights into algorithm design and sample complexity in this comprehensive introduction.
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18 Lesons
1 hour 12 minutes
On-Demand
Free-Video
International Mathematical Union

Learning Theoretic Foundations of Data-Driven Algorithm Design

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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.
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11 Lesons
45 minutes
On-Demand
Free-Video
TheIACR

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

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Explore pseudorandom knapsacks and LWE search-to-decision reductions, delving into their sample complexity and implications for cryptographic systems.
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1 Lesons
38 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Sample Complexity of Estimation in Logistic Regression - Lecture

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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.
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1 Lesons
56 minutes
On-Demand
Free-Video
APS Physics

Learning Quantum with Generative Models

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Explore generative models for quantum state learning, covering neural networks, RBMs, and applications to GHZ states and local Hamiltonians.
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11 Lesons
49 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Optimal Algorithms for Learning Quantum Phase States

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Delve into optimal algorithms and complexity analysis for learning quantum phase states, focusing on sample complexity bounds, measurement techniques, and applications in quantum computation.
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11 Lesons
26 minutes
On-Demand
Free-Video
Institute for Advanced Study

Characterizations of PAC Learnability

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Explore key characterizations of PAC learnability in machine learning theory with insights from postdoctoral researcher Nataly Brukhim.
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1 Lesons
15 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Learning with Volterra Series - Convolutional Networks without Activation Functions

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Explore Volterra Series in machine learning, reducing sample and model complexity while maintaining high performance in inference problems.
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1 Lesons
29 minutes
On-Demand
Free-Video
Uncertainty in Artificial Intelligence

Provably Efficient Adversarial Imitation Learning with Unknown Transitions - Oral Session 4

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Explore provably efficient adversarial imitation learning with unknown transitions. Discover a framework connecting reward-free exploration and AIL, and learn about the MB-TAIL algorithm's optimal performance and generalization ability.
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1 Lesons
27 minutes
On-Demand
Free-Video
Fields Institute

Privacy, Stability, and Online Learning

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Explore the intersection of privacy, stability, and online learning, focusing on sample complexity, private learnability, and mistake-bounded learning in classification algorithms.
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10 Lesons
37 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Towards Fast Autonomous Learners: Advances in Reinforcement Learning - 2015

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Explore reinforcement learning for AI agents interacting with humans, focusing on sample efficiency and transfer learning to improve educational tools and decision-making systems.
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16 Lesons
57 minutes
On-Demand
Free-Video
Google TechTalks

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

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Explore optimal algorithms and sample complexity for learning hierarchical tree representations from labeled data, covering PAC learning, online learning, and efficient tree classifier construction.
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19 Lesons
35 minutes
On-Demand
Free-Video
USC Probability and Statistics Seminar

Clustering a Mixture of Gaussians with Unknown Covariance - Lecture

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Explore clustering with Gaussian mixtures, focusing on maximum likelihood estimation, Max-Cut solutions, and efficient algorithms. Investigate statistical-computational gaps and challenges in high-dimensional settings.
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22 Lesons
46 minutes
On-Demand
Free-Video
Simons Institute

Learning From Contrastive Examples in Machine Learning

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Explores how contrastive training examples impact active learning algorithms, focusing on sample complexity for geometric concepts and Boolean functions, with connections to self-directed learning.
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1 Lesons
37 minutes
On-Demand
Free-Video