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pro vyhledávání: '"Alomar, Abdullah"'
Autor:
Alomar, Abdullah
The ever-increasing availability of data from dynamical systems offers an opportunity for automated data-driven decision-making in various domains. However, a significant barrier to realizing this potential is the issues inherent to these datasets: h
Externí odkaz:
https://hdl.handle.net/1721.1/153861
The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has been shown that one can lea
Externí odkaz:
http://arxiv.org/abs/2305.16491
Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism
Externí odkaz:
http://arxiv.org/abs/2211.07434
Autor:
Alomar, Abdullah
The analysis of multivariate time series data is of great interest across many domains, including cyber-physical systems, finance, retail, healthcare to name a few. A common goal across all of these domains is accurate imputation and forecasting of m
Externí odkaz:
https://hdl.handle.net/1721.1/140365
Autor:
Alomar, Abdullah, Hamadanian, Pouya, Nasr-Esfahany, Arash, Agarwal, Anish, Alizadeh, Mohammad, Shah, Devavrat
Publikováno v:
20th USENIX Symposium on Networked Systems Design and Implementation (2023) 1115--1147
We present CausalSim, a causal framework for unbiased trace-driven simulation. Current trace-driven simulators assume that the interventions being simulated (e.g., a new algorithm) would not affect the validity of the traces. However, real-world trac
Externí odkaz:
http://arxiv.org/abs/2201.01811
Autor:
Al Maghraby, Mohamed A., Alshami, Ali M., Muaidi, Qassim I., Abualait, Turki S., Alzahrani, Matar A., Alotaibi, Sultan S., Alamir, Hassan M., AlOudah, Mashari A., Aljiry, Abdulmajeed A., Alhijji, Hassan S., Alomar, Abdullah S., Al Ensaif, Mohammed
Publikováno v:
In Journal of Taibah University Medical Sciences June 2024 19(3):637-643
Autor:
Agarwal, Anish, Alomar, Abdullah, Alumootil, Varkey, Shah, Devavrat, Shen, Dennis, Xu, Zhi, Yang, Cindy
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance
Externí odkaz:
http://arxiv.org/abs/2102.06961
We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series. Under a spatio-temporal factor model we introduce, given $N$ time series and $T$ obs
Externí odkaz:
http://arxiv.org/abs/2006.13448
As we reach the apex of the COVID-19 pandemic, the most pressing question facing us is: can we even partially reopen the economy without risking a second wave? We first need to understand if shutting down the economy helped. And if it did, is it poss
Externí odkaz:
http://arxiv.org/abs/2005.00072
A major bottleneck of the current Machine Learning (ML) workflow is the time consuming, error prone engineering required to get data from a datastore or a database (DB) to the point an ML algorithm can be applied to it. Hence, we explore the feasibil
Externí odkaz:
http://arxiv.org/abs/1903.07097