no code implementations • 30 Dec 2023 • Ali Eshragh, Luke Yerbury, Asef Nazari, Fred Roosta, Michael W. Mahoney
We demonstrate that, with high probability, the accuracy of SALSA's approximations is within $(1 + O({\varepsilon}))$ of the true leverage scores.
no code implementations • 14 Oct 2023 • Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Designed as the foundational architecture of the trading system, the CausalReinforceNet framework enhances the capabilities of the reinforcement learning agent through causal analysis.
no code implementations • 13 Jun 2023 • Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee
The efficacy of the proposed model in predicting cryptocurrency price directions is evaluated from two perspectives.
no code implementations • 26 Mar 2023 • Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee
The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins.
no code implementations • 27 May 2022 • Antonio Robles-Kelly, Asef Nazari
In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks.
no code implementations • 20 Dec 2020 • Jian Liu, Lei Gao, Sujie Guo, Rui Ding, Xin Huang, Long Ye, Qinghua Meng, Asef Nazari, Dhananjay Thiruvady
In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels.
no code implementations • 27 Nov 2019 • Ali Eshragh, Fred Roosta, Asef Nazari, Michael W. Mahoney
We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes.