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no code implementations • 11 Oct 2019 • Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian, Vahab Mirrokni

We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems.

1 code implementation • 11 Oct 2019 • Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton

We propose $\tt RandUCB$, a bandit strategy that builds on theoretically derived confidence intervals similar to upper confidence bound (UCB) algorithms, but akin to Thompson sampling (TS), it uses randomization to trade off exploration and exploitation.

no code implementations • 4 Feb 2019 • Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian, Vianney Perchet

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 players receive zero reward.

no code implementations • NeurIPS 2018 • Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan

We prove that ϴ(k d^2 / ε^2) samples are necessary and sufficient for learning a mixture of k Gaussians in R^d, up to error ε in total variation distance.

no code implementations • 25 Aug 2018 • Gabor Lugosi, Abbas Mehrabian

We give the first theoretical guarantees for the second model: an algorithm with a logarithmic regret, and an algorithm with a square-root regret type that does not depend on the gaps between the means.

no code implementations • 18 Jun 2018 • Luc Devroye, Abbas Mehrabian, Tommy Reddad

Let $G$ be an undirected graph with $m$ edges and $d$ vertices.

no code implementations • 11 Jan 2018 • Hassan Ashtiani, Abbas Mehrabian

Density estimation is an interdisciplinary topic at the intersection of statistics, theoretical computer science and machine learning.

no code implementations • 14 Oct 2017 • Hassan Ashtiani, Shai Ben-David, Nick Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan

We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance.

no code implementations • 6 Jun 2017 • Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian

Let $\mathcal F$ be an arbitrary class of probability distributions, and let $\mathcal{F}^k$ denote the class of $k$-mixtures of elements of $\mathcal F$.

no code implementations • 8 Mar 2017 • Peter L. Bartlett, Nick Harvey, Chris Liaw, Abbas Mehrabian

We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function.

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