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User-Initiated Learning for Assistive Interfaces USER-INITIATED LEARNING  Motivation  All learning tasks are pre-defined before deployment  The learning.

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Presentation on theme: "User-Initiated Learning for Assistive Interfaces USER-INITIATED LEARNING  Motivation  All learning tasks are pre-defined before deployment  The learning."— Presentation transcript:

1 User-Initiated Learning for Assistive Interfaces USER-INITIATED LEARNING  Motivation  All learning tasks are pre-defined before deployment  The learning components are carefully hand-tuned by machine learning experts  Proposed Idea  Empower the end-users to define new learning tasks without a machine learning expert  CALO autonomously formulates and solves the learning problem  An Example Scenario  User asks CALO to learn to predict whether I intend to set sensitivity of an outgoing  CALO collects training examples for this task and learns to predict sensitivity  CALO reminds the user whenever he forgets to set sensitivity EXPERIMENTS  Problem  Attachment Prediction  Data Set  s obtained from a real user SUMMARY AND FUTURE WORK  Summary  A prototype functionality was developed that allows a user to define new learning tasks  Experiments show that self-tuning of parameters is important for successful learning  Systems that allow the users to guide learning is a possibility  Future Work  Natural interface for the user to guide learning: create learning tasks give advice (advice on relational features?) examine performance provide feedback (improve advice)  Newer algorithms that incorporate advice: learn from good advice resist bad advice  CALO should notice when it could help the user by formulating and solving new learning tasks. LEARNING  Learning Algorithm  Logistic Regression chosen as the core learning algorithm  Features  Relational features extracted from ontology  Incorporate User Advice on Features  Apply large prior variance on user selected features  Select prior variance on rest of the features through cross-validation  Automated Model Selection  Parameters: Prior variance on weights, classification threshold  Technique: Maximization of leave- one-out cross-validation estimate of kappa user Instrumented Outlook Instrumented Outlook Integrated Task Learning SAT Based Reasoning System Modify Procedure Compose New User Interface for Feature Guidance Feature Guidance + Related Objects Ontology Machine Learner Knowledge Base Training Examples Events SPARK Procedure Legal Features User Selected Features Instrumented Outlook Instrumented Outlook Trained Classifier Assistant SAT Based Reasoning System user Compose New New New Prediction Forgot? Prediction And Forgot = False Send Forgot = True Remind user ARCHITECTURE Learning Configurations Compared  No User Advice + Fixed Model Parameters  User Advice + Fixed Model Parameters  No User Advice + Model Selection  User Advice + Model Selection UIL ACTIVITY FLOW UIL EXPERIENCE Kshitij Judah, Jim Blythe, Oliver Brdiczka, Thomas Dietterich, Christopher Ellwood, Melinda Gervasio, Jed Irvine, Bill Jarrold, Michael Slater, Prasad Tadepalli, Jim Thornton, Alan Fern IRIS CALO DESKTOP PLUGINS EXTERNAL APPLICATIONS Integrated Task Learning Instrumented Outlook UIL SAT Based Reasoning System User Interface for Feature Guidance Machine Learner in the Box Assistant


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