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pro vyhledávání: '"Theocharous, P"'
Autor:
de Berg, Mark, Theocharous, Leonidas
Let $\mathcal{P}$ be a simple polygon with $m$ vertices and let $P$ be a set of $n$ points inside $\mathcal{P}$. We prove that there exists, for any $\varepsilon>0$, a set $\mathcal{C} \subset P$ of size $O(1/\varepsilon^2)$ such that the following h
Externí odkaz:
http://arxiv.org/abs/2403.04513
Let $d$ be a (well-behaved) shortest-path metric defined on a path-connected subset of $\mathbb{R}^2$ and let $\mathcal{D}=\{D_1,\ldots,D_n\}$ be a set of geodesic disks with respect to the metric $d$. We prove that $\mathcal{G}^{\times}(\mathcal{D})
Externí odkaz:
http://arxiv.org/abs/2403.04905
Given a set of $n$ points in the Euclidean plane, the $k$-MinSumRadius problem asks to cover this point set using $k$ disks with the objective of minimizing the sum of the radii of the disks. After a long line of research on related problems, it was
Externí odkaz:
http://arxiv.org/abs/2312.08803
Recommendation strategies are typically evaluated by using previously logged data, employing off-policy evaluation methods to estimate their expected performance. However, for strategies that present users with slates of multiple items, the resulting
Externí odkaz:
http://arxiv.org/abs/2308.14165
We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_n)$ on $(y_n)$, causally conditio
Externí odkaz:
http://arxiv.org/abs/2305.14131
Autor:
Kostas, James E., Jordan, Scott M., Chandak, Yash, Theocharous, Georgios, Gupta, Dhawal, White, Martha, da Silva, Bruno Castro, Thomas, Philip S.
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpr
Externí odkaz:
http://arxiv.org/abs/2305.09838
Autor:
Deshmukh, Shripad Vilasrao, Dasgupta, Arpan, Krishnamurthy, Balaji, Jiang, Nan, Agarwal, Chirag, Theocharous, Georgios, Subramanian, Jayakumar
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this
Externí odkaz:
http://arxiv.org/abs/2305.04073
Autor:
Andriani Angelopoulou, Giorgos Theocharous, Dimitrios Valakos, Aikaterini Polyzou, Sophia Magkouta, Vassilios Myrianthopoulos, Sophia Havaki, Marco Fiorillo, Ioanna Tremi, Konstantinos Vachlas, Theodoros Nisotakis, Dimitris-Foivos Thanos, Anastasia Pantazaki, Dimitris Kletsas, Jiri Bartek, Russell Petty, Dimitris Thanos, Rory J McCrimmon, Angelos Papaspyropoulos, Vassilis G Gorgoulis
Publikováno v:
Molecular Cancer, Vol 23, Iss 1, Pp 1-13 (2024)
Abstract Non-small cell lung cancer (NSCLC) constitutes one of the deadliest and most common malignancies. The LKB1/STK11 tumour suppressor is mutated in ∼ 30% of NSCLCs, typically lung adenocarcinomas (LUAD). We implemented zebrafish and human lun
Externí odkaz:
https://doaj.org/article/e39879f4050a4ba8adc5bd15a456cebe
Autor:
Sinha, Atanu R, Goyal, Navita, Dhamnani, Sunny, Asija, Tanay, Dubey, Raja K, Raja, M V Kaarthik, Theocharous, Georgios
Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to themselves. Biases take many forms, with cognitive biases occupying a cen
Externí odkaz:
http://arxiv.org/abs/2206.15129
Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switching decisions in consecutive rounds. We study smoothed online combina
Externí odkaz:
http://arxiv.org/abs/2204.10979