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#Trustworthy Machine Learning

Showing: 16 courses
Simons Institute

Contraction of Markov Kernels and Differential Privacy - Part I

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Explore Markov kernel contraction and its connection to differential privacy in machine learning, focusing on information-theoretic approaches for trustworthy algorithms.
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1 Lesons
35 minutes
On-Demand
Free-Video
Simons Institute

Differential Privacy & Variants

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Explore differential privacy and its variants, focusing on information-theoretic methods for enhancing trustworthiness in machine learning algorithms and applications.
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1 Lesons
56 minutes
On-Demand
Free-Video
Simons Institute

Learning Classification Metrics from Preference Feedback

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Explore techniques for learning classification metrics using preference feedback, focusing on information-theoretic approaches for trustworthy machine learning.
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1 Lesons
36 minutes
On-Demand
Free-Video
Simons Institute

Privacy and Fairness in Collaborative AI

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Explore information-theoretic approaches to enhance privacy and fairness in collaborative AI systems, focusing on trustworthy machine learning techniques.
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1 Lesons
33 minutes
On-Demand
Free-Video
Simons Institute

Decision Making with Information-Theoretic Constraints

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Explore information-theoretic constraints in decision-making processes, focusing on applications in trustworthy machine learning and their impact on algorithmic outcomes.
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1 Lesons
39 minutes
On-Demand
Free-Video
Simons Institute

Variational Formulations and Distributed Convex Optimization Methods for...

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Explore variational formulations and distributed convex optimization methods in information-theoretic approaches to trustworthy machine learning.
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1 Lesons
1 hour
On-Demand
Free-Video
Simons Institute

Learning Under Data Poisoning

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Explore data poisoning's impact on machine learning, its detection methods, and strategies for building robust models against adversarial attacks in this informative talk.
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1 Lesons
44 minutes
On-Demand
Free-Video
Simons Institute

Information-Theoretic Methods for Fair Risk Minimization

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Explore information-theoretic approaches to fair risk minimization in machine learning, focusing on trustworthy AI and ethical considerations in algorithm design.
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1 Lesons
32 minutes
On-Demand
Free-Video
Simons Institute

Differentially Quantized Gradient Methods

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Explore advanced gradient quantization techniques for machine learning, focusing on differential methods and their impact on algorithm efficiency and performance.
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1 Lesons
34 minutes
On-Demand
Free-Video
Simons Institute

Information Constrained Optimal Transport

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Explore information-constrained optimal transport theory and its applications in trustworthy machine learning with Stanford researcher Ayfer Ozgur.
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1 Lesons
59 minutes
On-Demand
Free-Video
Simons Institute

Data Valuation: A Perturbation-Aware Approach

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Explore perturbation-aware data valuation techniques for machine learning, focusing on information-theoretic methods to enhance trustworthiness and reliability in AI systems.
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1 Lesons
33 minutes
On-Demand
Free-Video
Simons Institute

Information-Theoretic Foundations of Generative Adversarial Models

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Explore information-theoretic methods for trustworthy machine learning, focusing on generative adversarial models and their foundations for enhanced reliability and performance.
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1 Lesons
35 minutes
On-Demand
Free-Video
Simons Institute

Generalization Bounds for Neural Network Based Decoders

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Explore generalization bounds for neural network decoders, examining their implications for information-theoretic approaches in trustworthy machine learning.
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1 Lesons
36 minutes
On-Demand
Free-Video
Simons Institute

Optimal Neural Network Compressors and the Manifold Hypothesis

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Explore the intersection of neural network compression and the manifold hypothesis, examining optimal compressors and their implications for machine learning efficiency and performance.
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1 Lesons
36 minutes
On-Demand
Free-Video
Simons Institute

Improving Accuracy-Privacy Tradeoff via Model Reprogramming

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Explore innovative techniques for enhancing the balance between model accuracy and data privacy through advanced model reprogramming strategies.
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1 Lesons
36 minutes
On-Demand
Free-Video
Simons Institute

Fundamental Trade-Offs in FL-FA - Sparsity - DP - Communication Constraints

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Explore trade-offs in federated learning, focusing on sparsity, differential privacy, and communication constraints. Gain insights into balancing these factors for effective machine learning.
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1 Lesons
37 minutes
On-Demand
Free-Video