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Poster
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Tue 18:30
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Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods
Veeranjaneyulu Sadhanala · Yu-Xiang Wang · James Sharpnack · Ryan Tibshirani
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Poster
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Tue 18:30
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Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Hyeonwoo Noh · Tackgeun You · Jonghwan Mun · Bohyung Han
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Poster
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Tue 18:30
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Group Additive Structure Identification for Kernel Nonparametric Regression
Chao Pan · Michael Zhu
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Poster
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Mon 18:30
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FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi · Luigi Carratino · Lorenzo Rosasco
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Poster
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Tue 18:30
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On Optimal Generalizability in Parametric Learning
Ahmad Beirami · Meisam Razaviyayn · Shahin Shahrampour · Vahid Tarokh
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Poster
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Wed 18:30
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A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Ashkan Panahi · Babak Hassibi
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Poster
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Tue 18:30
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Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Alireza Aghasi · Afshin Abdi · Nam Nguyen · Justin Romberg
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Poster
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Tue 18:30
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Generalization Properties of Learning with Random Features
Alessandro Rudi · Lorenzo Rosasco
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Poster
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Wed 18:30
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Variance-based Regularization with Convex Objectives
Hongseok Namkoong · John Duchi
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Poster
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Tue 18:30
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How regularization affects the critical points in linear networks
Amirhossein Taghvaei · Jin W Kim · Prashant Mehta
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Poster
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Mon 18:30
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On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
Xingguo Li · Lin Yang · Jason Ge · Jarvis Haupt · Tong Zhang · Tuo Zhao
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Poster
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Mon 18:30
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Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Matthias Hein · Maksym Andriushchenko
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