7 citations · 23 across the 7 of their papers we have counts for
7 papers
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
Abdullah Alomar, Munther Dahleh, Sean Mann +1
The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochasti…
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany +3
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)…
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
Anish Agarwal, Abdullah Alomar, Varkey Alumootil +4
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…
On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants
Anish Agarwal, Abdullah Alomar, Devavrat Shah
We introduce and analyze two extensions of Singular Spectrum Analysis (SSA) to the multivariate setting: a new variant of the well-known matrix-based method (mSSA), and a novel ten…
Synthetic Interventions
Anish Agarwal, Devavrat Shah, Dennis Shen
The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications. Researchers commonly justify the SC framework with a low-rank matrix f…
Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?
Anish Agarwal, Abdullah Alomar, Arnab Sarker +3
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 und…
tspDB: Time Series Predict DB
Anish Agarwal, Abdullah Alomar, Devavrat Shah
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 poin…