Statistical Learning
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
We study the generalization error of randomized learning algorithms--focusing on stochastic gradient descent (SGD)--using a novel combination of P AC-Bayes and algorithmic stability. Importantly, our generalization bounds hold for all posterior distributions on an algorithm's random hyperparameters, including distributions that depend on the training data.
Group Sparse Additive Machine
Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang
However, the previous works mainly focus on the least squares regression problem, not the classification task.
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
Cong Fang, Feng Cheng, Zhouchen Lin
Neural Information Processing Systems http://nips.cc/
Gradient descent GAN optimization is locally stable
Vaishnavh Nagarajan, J. Zico Kolter
Neural Information Processing Systems http://nips.cc/
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Training Deep Networks without Learning Rates Through Coin Betting
Francesco Orabona, Tatiana Tommasi
Neural Information Processing Systems http://nips.cc/
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther
From the earliest stages of childhood, humans learn to represent high-dimensional sensory input to make temporal predictions. From the visual image of a moving tennis ball, we can imagine its trajectory, and prepare ourselves in advance to catch it.
Probabilistic Rule Realization and Selection
Haizi Yu, Tianxi Li, Lav R. Varshney
Neural Information Processing Systems http://nips.cc/
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Kristofer Bouchard, Alejandro Bujan, Fred Roosta, Shashanka Ubaru, Mr. Prabhat, Antoine Snijders, Jian-Hua Mao, Edward Chang, Michael W. Mahoney, Sharmodeep Bhattacharya
We introduce Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced model selection and estimation.
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet
We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution.