Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Gokcesu, Kaan"'
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
This paper focuses on optimal unimodal transformation of the score outputs of a univariate learning model under linear loss functions. We demonstrate that the optimal mapping between score values and the target region is a rectangular function. To pr
Externí odkaz:
http://arxiv.org/abs/2304.02141
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
We investigate the nonlinear regression problem under L2 loss (square loss) functions. Traditional nonlinear regression models often result in non-convex optimization problems with respect to the parameter set. We show that a convex nonlinear regress
Externí odkaz:
http://arxiv.org/abs/2303.17745
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
This study presents an effective global optimization technique designed for multivariate functions that are H\"older continuous. Unlike traditional methods that construct lower bounding proxy functions, this algorithm employs a predetermined query cr
Externí odkaz:
http://arxiv.org/abs/2303.14293
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
Our study focuses on determining the best weight windows for a weighted moving average smoother under squared loss. We show that there exists an optimal weight window that is symmetrical around its center. We study the class of tapered weight windows
Externí odkaz:
http://arxiv.org/abs/2303.11958
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
Our research deals with the optimization version of the set partition problem, where the objective is to minimize the absolute difference between the sums of the two disjoint partitions. Although this problem is known to be NP-hard and requires expon
Externí odkaz:
http://arxiv.org/abs/2303.08219
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
We study the adversarial online learning problem and create a completely online algorithmic framework that has data dependent regret guarantees in both full expert feedback and bandit feedback settings. We study the expected performance of our algori
Externí odkaz:
http://arxiv.org/abs/2303.06526
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
In this work, we propose a meta algorithm that can solve a multivariate global optimization problem using univariate global optimizers. Although the univariate global optimization does not receive much attention compared to the multivariate case, whi
Externí odkaz:
http://arxiv.org/abs/2209.03246
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
We investigate an auto-regressive formulation for the problem of smoothing time-series by manipulating the inherent objective function of the traditional moving mean smoothers. Not only the auto-regressive smoothers enforce a higher degree of smoothi
Externí odkaz:
http://arxiv.org/abs/2206.14749
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
In this work, we propose an efficient minimax optimal global optimization algorithm for multivariate Lipschitz continuous functions. To evaluate the performance of our approach, we utilize the average regret instead of the traditional simple regret,
Externí odkaz:
http://arxiv.org/abs/2206.02383
Autor:
Gokcesu, Kaan, Gokcesu, Hakan
We study the sequential calibration of estimations in a quantized isotonic L2 regression setting. We start by showing that the optimal calibrated quantized estimations can be acquired from the traditional isotonic L2 regression solution. We modify th
Externí odkaz:
http://arxiv.org/abs/2206.00744