Combinations of adaptive filters: performance and convergence properties
J Arenas-Garcia, LA Azpicueta-Ruiz… - IEEE Signal …, 2015 - ieeexplore.ieee.org
Adaptive filters are at the core of many signal processing applications, ranging from acoustic
noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to …
noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to …
Optimally efficient sequential calibration of binary classifiers to minimize classification error
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2108.08780, 2021 - arxiv.org
In this work, we aim to calibrate the score outputs of an estimator for the binary classification
problem by finding an'optimal'mapping to class probabilities, where the'optimal'mapping is …
problem by finding an'optimal'mapping to class probabilities, where the'optimal'mapping is …
Low regret binary sampling method for efficient global optimization of univariate functions
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2201.07164, 2022 - arxiv.org
In this work, we propose a computationally efficient algorithm for the problem of global
optimization in univariate loss functions. For the performance evaluation, we study the …
optimization in univariate loss functions. For the performance evaluation, we study the …
An online minimax optimal algorithm for adversarial multiarmed bandit problem
K Gokcesu, SS Kozat - IEEE Transactions on Neural Networks …, 2018 - ieeexplore.ieee.org
We investigate the adversarial multiarmed bandit problem and introduce an online algorithm
that asymptotically achieves the performance of the best switching bandit arm selection …
that asymptotically achieves the performance of the best switching bandit arm selection …
A generalized online algorithm for translation and scale invariant prediction with expert advice
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2009.04372, 2020 - arxiv.org
In this work, we aim to create a completely online algorithmic framework for prediction with
expert advice that is translation-free and scale-free of the expert losses. Our goal is to create …
expert advice that is translation-free and scale-free of the expert losses. Our goal is to create …
Recursive experts: An efficient optimal mixture of learning systems in dynamic environments
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2009.09249, 2020 - arxiv.org
Sequential learning systems are used in a wide variety of problems from decision making to
optimization, where they provide a'belief'(opinion) to nature, and then update this belief …
optimization, where they provide a'belief'(opinion) to nature, and then update this belief …
Optimal and efficient algorithms for general mixable losses against switching oracles
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2108.06411, 2021 - arxiv.org
We investigate the problem of online learning, which has gained significant attention in
recent years due to its applicability in a wide range of fields from machine learning to game …
recent years due to its applicability in a wide range of fields from machine learning to game …
Asymptotically optimal contextual bandit algorithm using hierarchical structures
MM Neyshabouri, K Gokcesu… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
We propose an online algorithm for sequential learning in the contextual multiarmed bandit
setting. Our approach is to partition the context space and, then, optimally combine all of the …
setting. Our approach is to partition the context space and, then, optimally combine all of the …
Mixtures of conditional random fields for improved structured output prediction
M Kim - IEEE transactions on neural networks and learning …, 2016 - ieeexplore.ieee.org
The conditional random field (CRF) is a successful probabilistic model for structured output
prediction problems. In this brief, we consider to enlarge the representational capacity of …
prediction problems. In this brief, we consider to enlarge the representational capacity of …
Efficient, anytime algorithms for calibration with isotonic regression under strictly convex losses
K Gokcesu, H Gokcesu - arXiv preprint arXiv:2111.00468, 2021 - arxiv.org
We investigate the calibration of estimations to increase performance with an optimal
monotone transform on the estimator outputs. We start by studying the traditional square …
monotone transform on the estimator outputs. We start by studying the traditional square …