335 citations · 1.1k across the 63 of their papers we have counts for
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Linear Combinatorial Semi-Bandit with Causally Related Rewards
Behzad Nourani-Koliji, Saeed Ghoorchian, Setareh Maghsudi
In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alter…
Hypothesis Transfer in Bandits by Weighted Models
Steven Bilaj, Sofien Dhouib, Setareh Maghsudi
We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning. That is, we assume having access to a previously learned model on an unobs…
A Learning-Based Approach to Approximate Coded Computation
Navneet Agrawal, Yuqin Qiu, Matthias Frey +4
Lagrange coded computation (LCC) is essential to solving problems about matrix polynomials in a coded distributed fashion; nevertheless, it can only solve the problems that are rep…
Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization
Sofien Dhouib, Setareh Maghsudi
Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance. At the same time, em…
Distributed Task Management in Fog Computing: A Socially Concave Bandit Game
Xiaotong Cheng, Setareh Maghsudi
Fog computing leverages the task offloading capabilities at the network's edge to improve efficiency and enable swift responses to application demands. However, the design of task…
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
Saeed Ghoorchian, Evgenii Kortukov, Setareh Maghsudi
Taking advantage of contextual information can potentially boost the performance of recommender systems. In the era of big data, such side information often has several dimensions.…