42 citations · 197 across the 45 of their papers we have counts for
8 papers · 1 filter
Order Optimal One-Shot Distributed Learning
Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani
We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine th…
Interventional Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
It is known that from purely observational data, a causal DAG is identifiable only up to its Markov equivalence class, and for many ground truth DAGs, the direction of a large port…
Distributed Voting in Beep Model
Benyamin Ghojogh, Saber Salehkaleybar
We consider the problem of distributed multi-choice voting in a setting that each node can communicate with its neighbors merely by sending beep signals. Given its simplicity, the…
Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference
M. Reza Heydari, Saber Salehkaleybar, Kun Zhang
We propose two nonlinear regression methods, named Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) f…
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash +1
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effe…
Broadcast Distributed Voting Algorithm in Population Protocols
Hamidreza Bandealinaeini, Saber Salehkaleybar
We consider the problem of multi-choice majority voting in a network of agents where each agent initially selects a choice from a set of possible choices. The agents try to…
Seedless Graph Matching via Tail of Degree Distribution for Correlated Erdos-Renyi Graphs
Mahdi Bozorg, Saber Salehkaleybar, Matin Hashemi
The network alignment (or graph matching) problem refers to recovering the node-to-node correspondence between two correlated networks. In this paper, we propose a network alignmen…
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
Saber Salehkaleybar, Arsalan Sharifnassab, S. Jamaloddin Golestani
We consider distributed statistical optimization in one-shot setting, where there are machines each observing i.i.d. samples. Based on its observed samples, each machine se…