David Stern Ralf Herbrich Thore Graepel Microsoft Research Cambridge, UK Horst Samulowitz National ICT Australia University of Melbourne Melbourne, Australia.

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Presentation transcript:

David Stern Ralf Herbrich Thore Graepel Microsoft Research Cambridge, UK Horst Samulowitz National ICT Australia University of Melbourne Melbourne, Australia Luca Pulina Armando Tacchella Universita di Genova Genova, Italy Collaborative Expert Portfolio Management

Expert Portfolios Stream of Problems Solve Problem using recommended Expert Update Model Expert Portfolio Experts: Expert 1 Expert 2... Expert n Submit Problem Characterization (e.g., Feature Vector) Recommend Expert Query Model Report Utility Expert changes Applications: e.g., SATZilla [Xu et al., 07] e.g., AQME [Pulina et al., 08], CPHydra [O’Mahony et al., 08]

Adaptive Expert Portfolios Requirements: - Model must be trained online so it can immediately take account of each outcome to improve future decisions. - Computation cost should not depend on the number of previously seen problems [Pulina, 2008]. -The system should select a specific scheduling strategy for each task (based on task features) [Streeter and Smith, 2008]. - Model should adapt continuously over time, tracking domain and changing expert characteristics. -Support different forms of feedback (to support different problem domains) Cannot be addressed by previously presented approach Model based on Collaborative Filtering fulfills all requirements.

Map Features To ‘Trait’ Space User ID Male Female Gender Country UK USA Item ID Horror Movie Genre Drama Documentary Comedy 4

Learning Feature Contributions User ID Male Female Gender Country UK USA Item ID Horror Movie Genre Drama Documentary Comedy 5

User/Item Trait Space ‘Preference Cone’ for user

Task Features Algorithm Features Feedback Model P(t) Time to complete task (or other objective) u(t) E(u) Utility Function u t Adaptive Algorithm Expert Portfolios P(r) Trait Space Inner Product Algorithm Performance U U V V

Test Data QBF Solvers Competition Data –11 State-of-the-art solvers. –Run times (600 sec time-out). –5000 tasks. Microsoft Solver Foundation Performance Data –Linear Programming Daily test runs. –6 Simplex Solvers. –7 Interior Point Method (IPM) Solvers. –Run times. 8

Task Features Allow Generalisation QBF Features –103 Basic Features: #Clauses, #Variables, etc. 69 –Combined Features: Ratio Universal/Existential,... LP Model Features –Number Variables. –Number Rows. –Number Zeros. Goal: to predict solver performance on unseen tasks 9

Threshold Feedback Model aabb >> << rr qq Time-OutSlowFast

QBF Time Trait Space Properties

User-Defined Algorithm Utility Example:

QBF Portfolio Performance Features

Comparison to other Approaches for QBF 14 ApproachProblems Solved Average Time used per problem (in seconds) AQME [Pulina, Tacchella, 2009] (Adaptive Portfolio that retrains offline + other limitations) Collaborative Expert Portfolio Manager Oracle

Interior Point Method Simplex Method Dual Primal

16 Conclusions –Presented adaptive portfolio manager based on ‘Collaborative Filtering’ –Approach supports: Online adaption of portfolio at a negligible cost Tracking of domain as well as expert changes User-Defined feedback model –Can be applied in other domains as well: e.g., Yahoo Question-Answer