Zobrazeno 1 - 10
of 324
pro vyhledávání: '"Tytarenko, P. A."'
Autor:
Tytarenko, Andrii
In this paper, the application of imitation learning in caregiving robotics is explored, aiming at addressing the increasing demand for automated assistance in caring for the elderly and disabled. Leveraging advancements in deep learning and control
Externí odkaz:
http://arxiv.org/abs/2407.19819
Autor:
Tytarenko, Andrii
Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. This creates a pressing need for e
Externí odkaz:
http://arxiv.org/abs/2405.07603
Fine-tuning large pre-trained language models (LLMs) on particular datasets is a commonly employed strategy in Natural Language Processing (NLP) classification tasks. However, this approach usually results in a loss of models generalizability. In thi
Externí odkaz:
http://arxiv.org/abs/2401.16638
In this paper, we present analytical proof demonstrating that the Sandwiched Volterra Volatility (SVV) model is able to reproduce the power-law behavior of the at-the-money implied volatility skew, provided the correct choice of the Volterra kernel.
Externí odkaz:
http://arxiv.org/abs/2311.01228
In this paper, we present a comprehensive survey of continuous stochastic volatility models, discussing their historical development and the key stylized facts that have driven the field. Special attention is dedicated to fractional and rough methods
Externí odkaz:
http://arxiv.org/abs/2309.01033
In this paper, we construct consistent statistical estimators of the Hurst index, volatility coefficient, and drift parameter for Bessel processes driven by fractional Brownian motion with $H<1/2$. As an auxiliary result, we also prove the continuity
Externí odkaz:
http://arxiv.org/abs/2305.15205
The present paper investigates Cox-Ingersoll-Ross (CIR) processes of dimension less than 1, with a focus on obtaining an equation of a new type including local times for the square root of the CIR process. We utilize the fact that non-negative diffus
Externí odkaz:
http://arxiv.org/abs/2303.12911
We consider stochastic volatility dynamics driven by a general H\"older continuous Volterra-type noise and with unbounded drift. For these so-called SVV-models, we consider the explicit computation of quadratic hedging strategies. While the theoretic
Externí odkaz:
http://arxiv.org/abs/2209.13054
We introduce a new model of financial market with stochastic volatility driven by an arbitrary H\"older continuous Gaussian Volterra process. The distinguishing feature of the model is the form of the volatility equation which ensures the solution to
Externí odkaz:
http://arxiv.org/abs/2209.10688
Autor:
Tytarenko, A.
In this paper, we derive a novel method as a generalization over LCEs such as E2C. The method develops the idea of learning a locally linear state space, by adding a multi-step prediction, thus allowing for more explicit control over the curvature. W
Externí odkaz:
http://arxiv.org/abs/2209.01127