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no code implementations • 23 Nov 2022 • Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos

We design a Markov chain whose stationary distribution coincides with $\mathcal{D}$ and give an algorithm to obtain exact samples using the technique of Coupling from the Past.

no code implementations • 19 Feb 2022 • Jason Milionis, Alkis Kalavasis, Dimitris Fotakis, Stratis Ioannidis

We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates.

no code implementations • NeurIPS 2021 • Róbert Busa-Fekete, Dimitris Fotakis, Balazs Szorenyi, Emmanouil Zampetakis

In this paper, we devise identity tests for ranking data that is generated from Mallows model both in the \emph{asymptotic} and \emph{non-asymptotic} settings.

no code implementations • NeurIPS 2021 • Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis

We study the problem of uniformity testing for statistical data that consists of rankings over $m$ items where the alternative class is restricted to Mallows models with single parameter.

no code implementations • 4 Nov 2021 • Dimitris Fotakis, Alkis Kalavasis, Eleni Psaroudaki

We introduce a generative model for Label Ranking, in noiseless and noisy nonparametric regression settings, and provide sample complexity bounds for learning algorithms in both cases.

no code implementations • 22 Aug 2021 • Dimitris Fotakis, Alkis Kalavasis, Vasilis Kontonis, Christos Tzamos

Our main algorithmic result is that essentially any problem learnable from fine grained labels can also be learned efficiently when the coarse data are sufficiently informative.

1 code implementation • 17 Jul 2021 • Dimitris Fotakis, Evangelia Gergatsouli, Themis Gouleakis, Nikolas Patris

We prove that the competitive ratio decreases smoothly from sublogarithmic in the number of demands to constant, as the error, i. e., the total distance of the predicted locations to the optimal facility locations, decreases towards zero.

no code implementations • 8 Jun 2021 • Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis

We study dynamic clustering problems from the perspective of online learning.

no code implementations • 9 Dec 2020 • Agapi Rissaki, Orestis Pavlou, Dimitris Fotakis, Vicky Papadopoulou, Andreas Efstathiou

We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs).

1 code implementation • NeurIPS 2020 • Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis

We consider a natural model of online preference aggregation, where sets of preferred items $R_1, R_2, \ldots, R_t$ along with a demand for $k_t$ items in each $R_t$, appear online.

no code implementations • 2 Nov 2020 • Dimitris Fotakis, Alkis Kalavasis, Konstantinos Stavropoulos

We consider the problem of learning the true ordering of a set of alternatives from largely incomplete and noisy rankings.

no code implementations • 5 Jul 2020 • Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos

A stunning consequence is that virtually any statistical task (e. g., learning in total variation distance, parameter estimation, uniformity or identity testing) that can be performed efficiently for Boolean product distributions, can also be performed from truncated samples, with a small increase in sample complexity.

no code implementations • 3 Jun 2019 • Róbert Busa-Fekete, Dimitris Fotakis, Balázs Szörényi, Manolis Zampetakis

The main result of the paper is a tight sample complexity bound for learning Mallows and Generalized Mallows Model.

no code implementations • 18 Jul 2017 • Dimitris Fotakis, Vasilis Kontonis, Piotr Krysta, Paul Spirakis

The $k$'th power of this distribution, for $k$ in a range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.

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