Page 8 - 300+ Top Institute for Advanced Study Online Courses [2024]

Representational Power of Graph Neural Networks - Stefanie Jegelka

Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin

Learning Representations Using Causal Invariance - Leon Bottou

From Classical Statistics to Modern ML - The Lessons of Deep Learning - Mikhail Belkin

On the Connection Between Neural Networks and Kernels: A Modern Perspective - Simon Du

Energy-Based Approaches to Representation Learning - Yann LeCun

Are All Features Created Equal? - Aleksander Madry

Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li

PAC-Bayesian Approaches to Understanding Generalization in Deep Learning - Gintare Dziugaite

Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora

Tightening Information-Theoretic Generalization Bounds with Data-Dependent Estimates with an Application to SGLD - Daniel Roy

Fixing GAN Optimization Through Competitive Gradient Descent - Anima Anandkumar

Emergent Linguistic Structure in Deep Contextual Neural Word Representations - Chris Manning

Entanglement Wedge Reconstruction and the Information Paradox - Geoff Penington

String Topology and the Intersection Product - Nathalie Wahl

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Institute for Advanced Study Courses

Representational Power of Graph Neural Networks - Stefanie Jegelka

Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin

Learning Representations Using Causal Invariance - Leon Bottou

From Classical Statistics to Modern ML - The Lessons of Deep Learning - Mikhail Belkin

On the Connection Between Neural Networks and Kernels: A Modern Perspective - Simon Du

Energy-Based Approaches to Representation Learning - Yann LeCun

Are All Features Created Equal? - Aleksander Madry

Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li

PAC-Bayesian Approaches to Understanding Generalization in Deep Learning - Gintare Dziugaite

Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora

Tightening Information-Theoretic Generalization Bounds with Data-Dependent Estimates with an Application to SGLD - Daniel Roy

Fixing GAN Optimization Through Competitive Gradient Descent - Anima Anandkumar

Emergent Linguistic Structure in Deep Contextual Neural Word Representations - Chris Manning

Entanglement Wedge Reconstruction and the Information Paradox - Geoff Penington

String Topology and the Intersection Product - Nathalie Wahl

Institute for Advanced Study Courses

Representational Power of Graph Neural Networks - Stefanie Jegelka

Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin

Learning Representations Using Causal Invariance - Leon Bottou

From Classical Statistics to Modern ML - The Lessons of Deep Learning - Mikhail Belkin

On the Connection Between Neural Networks and Kernels: A Modern Perspective - Simon Du

Energy-Based Approaches to Representation Learning - Yann LeCun

Are All Features Created Equal? - Aleksander Madry

Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li

PAC-Bayesian Approaches to Understanding Generalization in Deep Learning - Gintare Dziugaite

Is Optimization the Right Language to Understand Deep Learning? - Sanjeev Arora

Tightening Information-Theoretic Generalization Bounds with Data-Dependent Estimates with an Application to SGLD - Daniel Roy

Fixing GAN Optimization Through Competitive Gradient Descent - Anima Anandkumar

Emergent Linguistic Structure in Deep Contextual Neural Word Representations - Chris Manning

Entanglement Wedge Reconstruction and the Information Paradox - Geoff Penington

String Topology and the Intersection Product - Nathalie Wahl