Orthogonal Machine Learning: Power and Limitations

Channel:

Subscribers:

68,700

Published on October 30, 2018 7:53:41 PM ● Video Link: https://www.youtube.com/watch?v=l8JTEGFs7_E

Duration: 43:37

2,147 views

Other Videos By Simons Institute for the Theory of Computing

2018-11-01	Sub-Gaussian Mean Estimation in Polynomial Time
2018-11-01	Robust Sparse Covariance Estimation by Thresholding Tyler's M-estimator
2018-11-01	Simons Institute Polylogues: Algorithmic Fairness and Quantum Computation
2018-10-31	A Flexible Framework for Hypothesis Testing in High-Dimensions
2018-10-31	The Robustness of Controlling the False Discovery Rate
2018-10-31	Controlling FDR While Highlighting Selected Discoveries
2018-10-30	Robustness in High-Dimensional Inference Tasks
2018-10-30	A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression
2018-10-30	TAP Free Energy, Spin Glasses, and Variational Inference
2018-10-30	Some Problems of Robust High-dimensional Statistics
2018-10-30	Orthogonal Machine Learning: Power and Limitations
2018-10-30	Generalization Bounds for Uniformly Stable Algorithms
2018-10-30	Invariance, Causality and Novel Robustness
2018-10-30	Mixture Models, Robustness, and Sum of Squares Proofs
2018-10-29	The Importance of Better Models in Stochastic Optimization...
2018-10-29	On Using Graph Distances to Estimate Euclidean and Related Distances
2018-10-29	Detecting Weak Signals via Hypothesis Testing
2018-10-29	Efficient Algorithms for Outlier-Robust Regression
2018-10-29	Computational-Statistical Tradeoffs in Robust High-Dimensional Estimation
2018-10-29	Robust List Decoding of Spherical Gaussians
2018-10-24	Mathematical Theories of Communication: Old and New

Tags:

Simons Institute

Theory of Computing

Theory of Computation

Theoretical Computer Science

Computer Science

UC Berkeley

Robust and High-Dimensional Statistics

Lester Mackey