Browsing by Keywords "Online Learning"
Now showing items 1-9 of 9
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Algorithms and regret bounds for multi-objective contextual bandits with similarity information
(Bilkent University, 2019-01)Contextual bandit algorithms have been shown to be e ective in solving sequential decision making problems under uncertain environments, ranging from cognitive radio networks to recommender systems to medical diagnosis. ... -
An asymptotically optimal solution for contextual bandit problem in adversarial setting
(Bilkent University, 2018-05)We propose online algorithms for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and then optimally combine all of the possible mappings between the partition ... -
Low complexity efficient online learning algorithms using LSTM networks
(Bilkent University, 2018-12)In this thesis, we implement efficient online learning algorithms using the Long Short Term Memory (LSTM) networks with low time and computational complexity. In Chapter 2, we investigate efficient covariance information-based ... -
Mixture of Learners for Cancer Stem Cell Detection Using CD13 and H and e Stained Images
(SPIE, 2016)In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis ... -
Online learning in structured Markov decision processes
(Bilkent University, 2017-07)This thesis proposes three new multi-armed bandit problems, in which the learner proceeds in a sequence of rounds where each round is a Markov Decision Process (MDP). The learner's goal is to maximize its cumulative ... -
Online learning under adverse settings
(Bilkent University, 2015-05)We present novel solutions for contemporary real life applications that generate data at unforeseen rates in unpredictable forms including non-stationarity, corruptions, missing/mixed attributes and high dimensionality. ... -
Online learning with recurrent neural networks
(Bilkent University, 2018-07)In this thesis, we study online learning with Recurrent Neural Networks (RNNs). Particularly, in Chapter 2, we investigate online nonlinear regression and introduce novel regression structures based on the Long Short ... -
Online minimax optimal density estimation and anomaly detection in nonstationary environments
(Bilkent University, 2017-08)Online anomaly detection has attracted signi cant attention in recent years due to its applications in network monitoring, cybersecurity, surveillance and sensor failure. To this end, we introduce an algorithm that ... -
Prediction with expert advice: on the role of contexts, bandit feedback and risk-awareness
(Bilkent University, 2018-12)Along with the rapid growth in the size of data generated and collected over time, the need for developing online algorithms that can provide answers without any offline training has considerably increased. In this thesis, ...
