Search Results for author:
Found 9 papers, 4 papers with code
Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data
With the recent explosive growth of interest and investment in virtual reality (VR) and the so-called "metaverse," public attention has rightly shifted toward the unique security and privacy threats that these platforms may pose.
Learning to Learn to Predict Performance Regressions in Production at Meta
Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder.
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a "bellwether"-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data.
Software Engineering
Is One Hyperparameter Optimizer Enough?
Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner.
Transfer Learning with Bellwethers to find Good Configurations
Using this insight, this paper proposes BEETLE, a novel bellwether based transfer learning scheme, which can identify a suitable source and use it to find near-optimal configurations of a software system.
Software Engineering
Finding Faster Configurations using FLASH
FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area.
Software Engineering
Faster Discovery of Faster System Configurations with Spectral Learning
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations.
Beyond Evolutionary Algorithms for Search-based Software Engineering
Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods. Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach.
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners.
