Search Results for author: Sahand N. Negahban

Found 3 papers, 1 papers with code

Distributed Machine Learning with Sparse Heterogeneous Data

no code implementations NeurIPS 2021 Dominic Richards, Sahand N. Negahban, Patrick Rebeschini

Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a graph topology.

BIG-bench Machine Learning Denoising +2

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

1 code implementation2 Jan 2019 Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand N. Negahban

We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data.

Multi-Armed Bandits

Individualized Rank Aggregation using Nuclear Norm Regularization

no code implementations3 Oct 2014 Yu Lu, Sahand N. Negahban

In recent years rank aggregation has received significant attention from the machine learning community.

Collaborative Ranking Matrix Completion

Cannot find the paper you are looking for? You can Submit a new open access paper.