Semi-Random Models and Robustness in Unsupervised Learning

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=unRra6BrsR0


Duration: 44:51
478 views
3

Aravindan Vijayaraghavan (Northwestern University)
https://simons.berkeley.edu/talks/semi-random-models-and-robustness-unsupervised-learning
Robust and High-Dimensional Statistics



Other Videos By Simons Institute for the Theory of Computing


2018-11-27Universal Sketches
2018-11-27Adaptive Sparse Recovery with Limited Adaptivity
2018-11-26The Limits of Proof
2018-11-07Sketching Big Data
2018-11-02Realizing Robustness
2018-11-02Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation
2018-11-02k-means and k-medians under Dimension Reduction
2018-11-02Scale Calibration for High-Dimensional Robust Regression
2018-11-01How Many Directions Determine a Shape: A VC Theory for Modulii Spaces of Shapes
2018-11-01Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
2018-11-01Semi-Random Models and Robustness in Unsupervised Learning
2018-11-01Can Non-Convex Optimization be Robust?
2018-11-01Robust Estimation and Generative Adversarial Nets
2018-11-01Sub-Gaussian Mean Estimation in Polynomial Time
2018-11-01Robust Sparse Covariance Estimation by Thresholding Tyler's M-estimator
2018-11-01Simons Institute Polylogues: Algorithmic Fairness and Quantum Computation
2018-10-31A Flexible Framework for Hypothesis Testing in High-Dimensions
2018-10-31The Robustness of Controlling the False Discovery Rate
2018-10-31Controlling FDR While Highlighting Selected Discoveries
2018-10-30Robustness in High-Dimensional Inference Tasks
2018-10-30A Modern Maximum-Likelihood Theory for High-Dimensional Logistic Regression


Tags:
Simons Institute
Theory of Computing
Theory of Computation
Theoretical Computer Science
Computer Science
UC Berkeley
Robust and High-Dimensional Statistics
Aravindan Vijayaraghavan