Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Tamás, Ambrus"'
Stochastic multi-armed bandits (MABs) provide a fundamental reinforcement learning model to study sequential decision making in uncertain environments. The upper confidence bounds (UCB) algorithm gave birth to the renaissance of bandit algorithms, as
Externí odkaz:
http://arxiv.org/abs/2406.05710
In this paper we revisit the classical method of partitioning classification and study its convergence rate under relaxed conditions, both for observable (non-privatised) and for privatised data. Let the feature vector $X$ take values in $\mathbb{R}^
Externí odkaz:
http://arxiv.org/abs/2312.14889
Publikováno v:
IEEE Control Systems Letters, Volume 7, 2023, pp. 2701-2706
The paper introduces robust independence tests with non-asymptotically guaranteed significance levels for stochastic linear time-invariant systems, assuming that the observed outputs are synchronous, which means that the systems are driven by jointly
Externí odkaz:
http://arxiv.org/abs/2308.02054
Autor:
Tamás, Ambrus, Csáji, Balázs Csanád
One of the key objects of binary classification is the regression function, i.e., the conditional expectation of the class labels given the inputs. With the regression function not only a Bayes optimal classifier can be defined, but it also encodes t
Externí odkaz:
http://arxiv.org/abs/2308.01835
Autor:
Tamás, Ambrus, Csáji, Balázs Csanád
Publikováno v:
Journal of Machine Learning Research, Volume 25, 2024
Kernel mean embeddings, a widely used technique in machine learning, map probability distributions to elements of a reproducing kernel Hilbert space (RKHS). For supervised learning problems, where input-output pairs are observed, the conditional dist
Externí odkaz:
http://arxiv.org/abs/2302.05955
Autor:
Tamás, Ambrus, Csáji, Balázs Csanád
Publikováno v:
IEEE Control Systems Letters, Volume 6, 2022, pp. 860-865
In this paper we suggest two statistical hypothesis tests for the regression function of binary classification based on conditional kernel mean embeddings. The regression function is a fundamental object in classification as it determines both the Ba
Externí odkaz:
http://arxiv.org/abs/2103.05126
Autor:
Csáji, Balázs Csanád, Tamás, Ambrus
The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier,
Externí odkaz:
http://arxiv.org/abs/1903.09790
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