Statistical Learning
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, René Vidal, Manolis Tsakiris
Fitting a linear subspace to a dataset corrupted by outliers is a fundamental problem in machine learning and statistics, primarily known as(Robust) Principal Component Analysis (PCA)[10,2].
Masking: A New Perspective of Noisy Supervision
Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, Masashi Sugiyama
Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels.
Hamiltonian descent for composite objectives
Brendan O'Donoghue, Chris J. Maddison
Neural Information Processing Systems http://nips.cc/
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel
Neural Information Processing Systems http://nips.cc/
Making AI Forget You: Data Deletion in Machine Learning
Antonio Ginart, Melody Guan, Gregory Valiant, James Y. Zou
We investigate algorithmic principles that enable efficient data deletion in ML.
Quantum Embedding of Knowledge for Reasoning
Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam
Neural Information Processing Systems http://nips.cc/
Should We Learn Most Likely Functions or Parameters? Shikai Qiu Tim G. J. Rudner Sanyam Kapoor Andrew Gordon Wilson New York University
Machine learning has matured to the point where we often take key design decisions for granted.
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová
Neural Information Processing Systems http://nips.cc/