157 citations · 173 across the 6 of their papers we have counts for
6 papers · 1 filter
RLFlow: Optimising Neural Network Subgraph Transformation with World Models
Sean Parker, Sami Alabed, Eiko Yoneki
Training deep learning models takes an extremely long execution time and consumes large amounts of computing resources. At the same time, recent research proposed systems and compi…
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB
Sami Alabed, Eiko Yoneki
RocksDB is a general-purpose embedded key-value store used in multiple different settings. Its versatility comes at the cost of complex tuning configurations. This paper investigat…
Learning Index Selection with Structured Action Spaces
Jeremy Welborn, Michael Schaarschmidt, Eiko Yoneki
Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies. Injecting task-specific knowledge into the tuner for a task may allow…
Wield: Systematic Reinforcement Learning With Progressive Randomization
Michael Schaarschmidt, Kai Fricke, Eiko Yoneki
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale. They assume standardized simulator interfaces but are not concerned wit…
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Michael Schaarschmidt, Sven Mika, Kai Fricke +1
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communic…
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
Michael Schaarschmidt, Alexander Kuhnle, Ben Ellis +3
Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent…