Statistical Learning
Balanced Policy Evaluation and Learning
Neural Information Processing Systems http://nips.cc/
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Xiaoxuan Zhang, Mingrui Liu, Xun Zhou, Tianbao Yang
In this paper, we consider online F-measure optimization (OFO). Unlike traditional performance metrics (e.g., classification error rate), F-measure is non-decomposable over training examples and is a non-convex function of model parameters, making it much more difficult to be optimized in an online fashion. Most existing results of OFO usually suffer from high memory/computational costs and/or lack statistical consistency guarantee for optimizing F-measure at the population level.
Improving Explorability in Variational Inference with Annealed Variational Objectives Chin-Wei Huang,?, 1 Shawn Tan
In our experiments, we demonstrate our method's
A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice
The computational complexity of a property tester is the number of adjacency list entries it reads, denoted its queries . Many works in graph property testing focus on testing plain graphs that contain only the pure combinatorial information.
Connecting Optimization and Regularization Paths
Consequently, a line of work has focused on characterizing the implicit biases of global optimum reached by various optimization algorithms. For example, Gunasekar et al. [ 2017 ] consider the problem of matrix factorization and show that gradient descent (GD) on un-regularized objective converges to the minimum nuclear norm solution.
On Coresets for Logistic Regression
Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David Woodruff
Coresets are one of the central methods to facilitate the analysis of large data.
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
Xiaodong Cui, Wei Zhang, Zoltรกn Tรผske, Michael Picheny
The complementarity between SGD and EA is worth investigating.
A Smoother Way to Train Structured Prediction Models
Venkata Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaid Harchaoui
Neural Information Processing Systems http://nips.cc/
Stochastic Nonparametric Event-Tensor Decomposition
Neural Information Processing Systems http://nips.cc/
Neural Proximal Gradient Descent for Compressive Imaging
Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John Pauly
Neural Information Processing Systems http://nips.cc/