Efficient Online Bayesian Inference for Neural Bandits

Last update: Dec 27, 2022

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Overview

Efficient Online Bayesian Inference for Neural Bandits

By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy
AISTATS 2022. https://arxiv.org/abs/2112.00195

Installation

pip install fire
pip install ml-collections

Reproduce the results

There are two ways to reproduce the results from the paper

Run the scripts

To reproduce the results, cd into the project folder and run

python bandits test

python bandits run_and_plot

Step by step

If you only want to reproduce the results, run

python bandits run_experiments

If you have previously reproduced the results and want to reproduce the plots, run

python bandits plot_experiments

The results will be stored inside bandits/figures/.

Execute the notebooks

An alternative way to reproduce the results is to simply open and run subspace_bandits.ipynb

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Efficient Online Bayesian Inference for Neural Bandits

Related tags

Overview

Efficient Online Bayesian Inference for Neural Bandits

Installation

Reproduce the results

Run the scripts

Step by step

Execute the notebooks

Owner

Probabilistic machine learning

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Repository for Multimodal AutoML Benchmark

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This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

PyTorch implementation for our AAAI 2022 Paper "Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning"

Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.

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Self-Adaptable Point Processes with Nonparametric Time Decays

CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

A CROSS-MODAL FUSION NETWORK BASED ON SELF-ATTENTION AND RESIDUAL STRUCTURE FOR MULTIMODAL EMOTION RECOGNITION

Code for "Primitive Representation Learning for Scene Text Recognition" (CVPR 2021)

This repo contains the code required to train the multivariate time-series Transformer.

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

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