Optimization
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer, Mona Meister, Duy Nguyen-Tuong
For time-series modeling we employ a Gaussian process with a nonlinear exogenous input structure.
Online Adaptive Methods, Universality and Acceleration
Kfir Y. Levy, Alp Yurtsever, Volkan Cevher
In this work we propose such a method.
Fast Similarity Search via Optimal Sparse Lifting
Wenye Li, Jingwei Mao, Yin Zhang, Shuguang Cui
Similarity search is a fundamental problem in computing science with various applications and has attracted significant research attention, especially in large-scale search with high dimensions.
Deep Structured Prediction with Nonlinear Output Transformations Colin Graber Ofer Meshi Alexander Schwing
While deep nets are a very convenient tool these days, enabling rapid progress in both industry and academia, their training is known to require significant amounts of data.
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Reinforcement Learning for Solving the Vehicle Routing Problem
MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder, Martin Takac
We present an end-to-end framework for solving the V ehicle Routing Problem (VRP) using reinforcement learning.
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
Neural Information Processing Systems http://nips.cc/
An Efficient Pruning Algorithm for Robust Isotonic Regression
Neural Information Processing Systems http://nips.cc/
Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar
Equal Contribution 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. is often attributed to the rapid decay in the learning rate when gradients are dense, which is often the case in many machine learning applications.
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
Kevin Scaman, Francis Bach, Sebastien Bubeck, Laurent Massoulié, Yin Tat Lee
Neural Information Processing Systems http://nips.cc/