Transfer Learning for Improving Model Predictions in Highly - PowerPoint PPT Presentation

Transfer Learning for Improving Model Predictions   in Highly Configurable Software Pooyan Jamshidi, Miguel Velez , Christian Kästner, Norbert Siegmund, Prasad Kawthekar

Highly Configurable Systems In Dynamic Environments [Credit to CMU CoBot]

Options Influence Performance

Adapt to Different Environments TurtleBot

Adapt to Different Environments TurtleBot 50 + 3*C1 + 15*C2 - 7*C2 *C3

Classic Sensitivity Analysis TurtleBot 50 + 3*C1 + 15*C2 - 7*C2 *C3

Classic Sensitivity Analysis TurtleBot Measure 50 + 3*C1 + 15*C2 - 7*C2 *C3

Classic Sensitivity Analysis TurtleBot Measure Learn 50 + 3*C1 + 15*C2 - 7*C2 *C3

Applications TurtleBot Optimization Adaptation + Reasoning + Debugging + Measure Learn 50 + 3*C1 + 15*C2 - 7*C2 *C3

Measuring Performance is Expensive 25 options × 10 values = 10 25 configurations Measure [Credit to Peng Hou]

Transfer Learning

Reuse Data From Similar System TurtleBot Measure

Reuse Data From Similar System TurtleBot Measure Simulator (Gazebo)

Reuse Data From Similar System TurtleBot Measure Simulator (Gazebo) Measure

Reuse Data From Similar System TurtleBot Measure Reuse Simulator (Gazebo) Measure

Reuse Data From Similar System TurtleBot Measure Learn Reuse 50 + 3*C1 + 15*C2 - 7*C2 *C3 with TL Simulator (Gazebo) Measure

Transfer Between Different Systems

Transfer Between Different Systems (0, 0) → (0, 10) (0, 0) → (7, 12)

Exploiting Similarity

Function Prediction Target function 200 Target samples 150 100 50 0 1 3 5 9 10 11 12 13 14

Prediction without Transfer Learning

Prediction with More Data but without Transfer Learning

Prediction with Transfer Learning

Technical Details

Prediction With Transfer Learning true function GP variance GP mean observation Motivations: 1. mean estimates + variance 2. all computation are linear algebra 3. good estimations with few data selected point true minimum

Prediction With Transfer Learning true function GP variance GP mean observation Motivations: 1. mean estimates + variance 2. all computation are linear algebra 3. good estimations when few data selected point true minimum https://github.com/pooyanjamshidi/transferlearning

Prediction With Transfer Learning true function GP variance GP mean observation Motivations: 1. mean estimates + variance 2. all computation are linear algebra 3. good estimations when few data selected point true minimum https://github.com/pooyanjamshidi/transferlearning pjsamshid@cs.cmu.edu

Evaluation

Case Study and Controlled Experiments RQ1: Improve prediction accuracy? RQ2: Tradeoffs among number of source and target samples? RQ3: Fast enough for self-adaptive systems?

Analyzed Systems Autonomous service robot Environmental change 3 stream processing apps Workload change NoSQL DBMS Workload & hardware changes

Prediction Accuracy

Performance Prediction for CoBot 25 25 20 Source 20 Model 15 15 10 10 5 5 0 5 10 15 20 25

Performance Prediction for CoBot 25 25 25 25 Source Target 20 20 20 20 Model Model 15 15 15 15 10 10 10 10 5 5 5 5 0 0 5 10 15 20 25 5 10 15 20 25

Performance Prediction for CoBot 25 25 25 25 Source Target 20 20 20 20 Model Model 15 15 15 15 10 10 10 10 5 5 5 5 0 0 5 10 15 20 25 5 10 15 20 25 25 CPU usage[%] 25 Prediction 20 20 with 4 samples 15 15 10 10 5 5 0 5 10 15 20 25

Performance Prediction for CoBot 25 25 25 25 Source Target 20 20 20 20 Model Model 15 15 15 15 10 10 10 10 5 5 5 5 0 0 5 10 15 20 25 5 10 15 20 25 25 25 CPU usage[%] CPU usage[%] 25 25 Prediction Prediction 20 20 20 20 with 4 samples with TL 15 15 15 15 10 10 10 10 5 5 5 5 0 0 5 10 15 20 25 5 10 15 20 25

Number of Source and Target Samples

Prediction Error with Different Source and Target Samples % Source %Target

Accuracy and Costs % Source B u d g e t %Target

Prediction error of other systems

Applicable in Self-Adaptive Systems

Transfer Learning in Self-Adaptive Systems Low model training overhead Produces accurate models Models can improve at each adaptation cycle

Future Work, Insights, and Ideas

Selecting from Multiple Sources C1 (1) Source Robot (4) Target Robot C2 (5) (2) C3 (3) Target Simulator Source Simulator

Active Learning with Transfer Learning TurtleBot Iteratively find best sample points that maximize knowledge Measure Learn Reuse 50 + 3*C1 + 15*C2 - 7*C2 *C3 with TL Simulator (Gazebo) Measure

Integrating Transfer Learning in MAPE-K Contribute to Knowledge Assist in self-optimization Support online learning

Summary

Transfer Learning for Improving Model Prediction in Highly Configurable Software Reuse data from similar system Improves model accuracy Applicable in self-adaptive systems

Transfer Learning Improves Sampling

Transfer Learning for Improving Model Predictions in Highly - PowerPoint PPT Presentation

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Transfer Learning for Improving Model Predictions in Highly - PowerPoint PPT Presentation

Transfer Learning for Improving Model Predictions in Highly Configurable Software Pooyan Jamshidi, Miguel Velez , Christian Kstner, Norbert Siegmund, Prasad Kawthekar Highly Configurable Systems In Dynamic Environments [Credit to CMU

Condition Monitoring &amp; Transfer Learning Good predictions in situations with (initially)

Warm Up Cold-Start Advertisements Improving CTR predictions via Learning to Learn ID embeddings

Improving Domain-specific Transfer Learning Applications for Image Recognition and Differential

Industrial Transfer Learning Introduction to Industrial Transfer Learning Industrial Transfer

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study

Parameter-Efficient Transfer Learning for NLP N. Houlsby, A. Giurgiu*, S. Jastrzbski*, B.

Transfer Learning Eu Wern Teh What are we covering? Why transfer learning? Fine

Exploring the Limits of Transfer Learning with a unified Text-to-Text Transformer Presented by -

Transfer learning and domain adaptation Semi-supervised and transfer learning Myth : you cant

Improving Inter-Hospital Transfer for Critically Ill Patients Emily B. Finn, MPH, John Sather,

Machine-Learning Methods in Property Predictions: Quo Vadis ? Igor I. Baskin Lomonosov Moscow

Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model Paul

Learning and Meta-learning computation making predictions choosing actions

Meta-transfer Learning for Few-shot Learning Yaoyao Liu Tianjin University and NUS School of

Improving Transit Connections via Transfer Optimization and On-Demand Services iCity CATTS

Model predictions for deep inelastic reactions: towards high Z and A. Giovanni POLLAROLO

1 Predictions for 2020 Predictions for 2020 We will live in flying houses. 1966

Projection based transfer learning Christian Poelitz Dortmund Technical University Christian

Improving Student Learning Recommitting to Professional Learning Communities and an analysis of

Multiple Instance Learning for Fast, Stable and Early RNN Predictions Don Dennis , Microsoft

Improving Homebirth to Hospital Transfer: Interfacing With EMS Murph has been a paramedic

Midterm presentation of A Coupled Transport and Chemical Model for Durability Predictions of

Improving Integration of Learning in Learning Communities Through the Development of a Shared

Hedging Predictions in Machine Learning Alex Gammerman and Zhiyuan Luo zhiyuan@cs.rhul.ac.uk

Condition Monitoring & Transfer Learning Good predictions in situations with (initially)

Parameter-Efficient Transfer Learning for NLP N. Houlsby, A. Giurgiu, S. Jastrzbski, B.