Search Results for author: Morteza Ibrahimi

Found 12 papers, 3 papers with code

Approximate Thompson Sampling via Epistemic Neural Networks

1 code implementation18 Feb 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Further, we demonstrate that the \textit{epinet} -- a small additive network that estimates uncertainty -- matches the performance of large ensembles at orders of magnitude lower computational cost.

Thompson Sampling

Epistemic Neural Networks

1 code implementation NeurIPS 2023 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

We introduce the epinet: an architecture that can supplement any conventional neural network, including large pretrained models, and can be trained with modest incremental computation to estimate uncertainty.

Reinforcement Learning, Bit by Bit

no code implementations6 Mar 2021 Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

To illustrate concepts, we design simple agents that build on them and present computational results that highlight data efficiency.

reinforcement-learning Reinforcement Learning (RL)

On Efficiency in Hierarchical Reinforcement Learning

no code implementations NeurIPS 2020 Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy, Satinder Singh

Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efficient solutions to sequential decision making problems, both in terms of statistical as well as computational efficiency.

Computational Efficiency Decision Making +4

Support Recovery for the Drift Coefficient of High-Dimensional Diffusions

no code implementations19 Aug 2013 Jose Bento, Morteza Ibrahimi

Consider the problem of learning the drift coefficient of a $p$-dimensional stochastic differential equation from a sample path of length $T$.

Vocal Bursts Intensity Prediction

Accelerated Time-of-Flight Mass Spectrometry

no code implementations18 Dec 2012 Morteza Ibrahimi, Andrea Montanari, George S Moore

We study a simple modification to the conventional time of flight mass spectrometry (TOFMS) where a \emph{variable} and (pseudo)-\emph{random} pulsing rate is used which allows for traces from different pulses to overlap.

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