Search Results for author: Morteza Ibrahimi

Found 9 papers, 2 papers with code

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?

1 code implementation9 Oct 2021 Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy

This paper introduces \textit{The Neural Testbed}, which provides tools for the systematic evaluation of agents that generate such predictions.

Epistemic Neural Networks

1 code implementation19 Jul 2021 Ian Osband, Zheng Wen, Mohammad Asghari, Morteza Ibrahimi, Xiyuan Lu, Benjamin Van Roy

All existing approaches to uncertainty modeling can be expressed as ENNs, and any ENN can be identified with a Bayesian neural network.

Reinforcement Learning, Bit by Bit

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

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments.

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.

Decision Making Hierarchical Reinforcement Learning

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 $T$.

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems

no code implementations NeurIPS 2012 Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy

In particular, our algorithm has an average cost of $(1+\eps)$ times the optimum cost after $T = \polylog(p) O(1/\eps^2)$.

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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