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Hassam Sheikh

dblp:276/0533 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Reinforcement learning · 60% Probabilistic and Bayesian machine learning · 20% Efficient and distributed learning · 20%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
ensemble reinforcement learning		 1.122022
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning · ICML 2022
Maximizing Ensemble Diversity in Deep Reinforcement Learning · ICLR 2022
Machine learning › Probabilistic and Bayesian machine learning › sampling
neural sampling		 0.612022
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning · ICML 2022

Methods — techniques the papers use, named apart from their topics

k-DPP · 0.6ensemble diversity maximization · 0.6determinantal point process · 0.6REDQ · 0.6
YearPublicationVenuePosition
2022 Maximizing Ensemble Diversity in Deep Reinforcement Learning
Hassam Sheikh, Mariano Phielipp, Ladislau Bölöni
 ICLR1
2022 DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
abstract
The application of an ensemble of neural networks is becoming an imminent tool for advancing state-of-the-art deep reinforcement learning algorithms. However, training these large numbers of neural networks in the ensemble has an exceedingly high computation cost which may become a hindrance in training large-scale systems. In this paper, we propose DNS: a Determinantal Point Process based Neural Network Sampler that specifically uses k-DPP to sample a subset of neural networks for backpropagation at every training step thus significantly reducing the training time and computation cost. We integrated DNS in REDQ for continuous control tasks and evaluated on MuJoCo environments. Our experiments show that DNS augmented REDQ matches the baseline REDQ in terms of average cumulative reward and achieves this using less than 50% computation when measured in FLOPS. The code is available at https://github.com/IntelLabs/DNS
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp
 ICML1