Learning Management
Online Learning for Multivariate Hawkes Processes
Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
We develop a nonparametric and online learning algorithm that estimates the triggering functions of a multivariate Hawkes process (MHP).
Online Learning with Transductive Regret
Online learning is a general framework for sequential prediction.
Stochastic and Adversarial Online Learning without Hyperparameters
Ashok Cutkosky, Kwabena A. Boahen
The algorithm's objective is to minimize regret, which is the total loss of all rounds relative to
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
M. Sevi Baltaoglu, Lang Tong, Qing Zhao
UP A is widely used in practice.
Online Learning with a Hint
Ofer Dekel, arthur flajolet, Nika Haghtalab, Patrick Jaillet
We study a variant of online linear optimization where the player receives a hint about the loss function at the beginning of each round. The hint is given in the form of a vector that is weakly correlated with the loss vector on that round.
Bandit Learning with Implicit Feedback Yi Qi
Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's
Community Exploration: From Offline Optimization to Online Learning
Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui
We introduce the community exploration problem that has many real-world applications such as online advertising.
Online Learning of Quantum States
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
Neural Information Processing Systems http://nips.cc/
Adaptive Online Learning in Dynamic Environments
Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
In the literature, there are two different forms of dynamic regret.
Private Online Learning via Lazy Algorithms
We study the problem of private online learning, focusing on online prediction from experts (OPE) and online convex optimization (OCO).