Statistical Learning
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
We apply the derived computational error bounds to study the generalization performance ofmulti-pass stochastic gradient descent (SGD) ina non-parametric setting.
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
Chris Russell, Matteo Toso, Neill Campbell
Neural Information Processing Systems http://nips.cc/
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao
Learning the probability distribution of high-dimensional data is a challenging problem.
Estimating Learnability in the Sublinear Data Regime
Neural Information Processing Systems http://nips.cc/
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Rรณbert Busa-Fekete, Krzysztof Dembczynski
Neural Information Processing Systems http://nips.cc/
Learning to Optimize Tensor Programs
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Neural Information Processing Systems http://nips.cc/
Self-supervised Graph Neural Networks via Low-Rank Decomposition Liang Y ang
Unfortunately, this common choice tends to cause two serious issues.
Learning Confidence Sets using Support Vector Machines
Our problem can be understood as a two-sided NP classification problem.