8 citations · 8 across the 1 of their papers we have counts for
8 papers
Regret Bounds for Batched Bandits
Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian +1
We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems. We prove bounds for their expected regrets t…
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
Sharan Vaswani, Abbas Mehrabian, Audrey Durand +1
We propose , a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampl…
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian +1
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved…
Multiplayer bandits without observing collision information
Gabor Lugosi, Abbas Mehrabian
We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involve…
The Minimax Learning Rates of Normal and Ising Undirected Graphical Models
Luc Devroye, Abbas Mehrabian, Tommy Reddad
Let be an undirected graph with edges and vertices. We show that -dimensional Ising models on can be learned from i.i.d. samples within expected total variat…
Some techniques in density estimation
Hassan Ashtiani, Abbas Mehrabian
Density estimation is an interdisciplinary topic at the intersection of statistics, theoretical computer science and machine learning. We review some old and new techniques for bou…