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20172021
most citedModel-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

63 citations · 100 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.LG20216 cited

Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices

Yuhong Song, Weiwen Jiang, Bingbing Li +6

A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…

cs.LG20209 cited

Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start

Weiwen Jiang, Lei Yang, Sakyasingha Dasgupta +2

Hardware and neural architecture co-search that automatically generates Artificial Intelligence (AI) solutions from a given dataset is promising to promote AI democratization; howe…

cs.LG2019

Continual Learning via Online Leverage Score Sampling

Dan Teng, Sakyasingha Dasgupta

In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for continual learning, effectiv…

cs.LG201963 cited

Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

Pengqian Yu, Joon Sern Lee, Ilya Kulyatin +2

Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profi…

cs.LG2018

Internal Model from Observations for Reward Shaping

Daiki Kimura, Subhajit Chaudhury, Ryuki Tachibana +1

Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functi…

cs.LG20175 cited

Conditional generation of multi-modal data using constrained embedding space mapping

Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar +2

We present a conditional generative model that maps low-dimensional embeddings of multiple modalities of data to a common latent space hence extracting semantic relationships betwe…