Active Learning for Decision-Making from Imbalanced Observational - PowerPoint PPT Presentation

Feb 10, 2024 •457 likes •527 views

Active Learning for Decision-Making from Imbalanced Observational Data 11.06.2019 Iiris Sundin 1 , Peter Schulam 2 , Eero Siivola 1 , Aki Vehtari 1 , Suchi Saria 2 , Samuel Kaski 1 1. Department of Computer Science, Aalto University, Helsinki,

Active Learning for Decision-Making from Imbalanced Observational Data 11.06.2019 Iiris Sundin 1 , Peter Schulam 2 , Eero Siivola 1 , Aki Vehtari 1 , Suchi Saria 2 , Samuel Kaski 1 1. Department of Computer Science, Aalto University, Helsinki, Finland 2. Department of Computer Science, Johns Hopkins University, Baltimore, USA Poster #239
Problem and setup • Decision-making task: Choose treatment to a new, previously unseen unit ෤ 𝑦 • Learn individual treatment effect 𝜐 = 𝔽[𝑧 1 − 𝑧 0 ∣ 𝑦] Treated • Imbalance Control (=not treated) • Different covariate distributions in treated and control groups 𝑞(𝜐 ∣ 𝑦, 𝐸) • Causes uncertainty to Ƹ 𝑦 11.6.2019 Poster #239 2
Decision-making performance • Type S error rate • Probability that the model infers the sign of the treatment effect wrong True treatment effects Effect Estimated treatment effects correct decision wrong decision 0 Type S error 11.6.2019 Poster #239 3
Contributions • Conditions when imbalance increases Type S error rate • Estimate for Type S error rate • Active learning to minimize estimated Type S error rate Semi-synthetic medical data * Decision-making aware (this work) Expected targeted information gain Expected information gain Uncertainty sampling * Infant Health and Development Program (IHDP) data (Hill 2011). 11.6.2019 Poster #239 4
Summary • Imbalance impairs decision-making performance • Type S error rate • A natural measure for decision-making performance • Bayesian estimate of Type S error rate • Active learning that targets the Type S error rate the most effective in improving decisions • Code available at https://github.com/IirisSundin/active-learning-for-decision-making Poster #239 Twitter: @iirisSun

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability

Active Learning Passive Learning Active Learning 1. Think 1. Acquisition of knowledge Ability 2. Decision making 2. Application of knowledge 3. Explain/communicate 1. Deep learning Approach to Uni-directional 2. Interactive Learning

282 views • 5 slides

Course outline Online decision making 1. Statistical active learning 2. Combinatorial

Active Learning and Optimized Information Gathering Lecture 3 Reinforcement Learning CS 101.2 Andreas Krause Announcements Homework 1: out tomorrow Due Thu Jan 22 Project Proposal due Tue Jan 27 (start soon!) Office hours Come to

573 views • 23 slides

Background & Prerequisites Basic probability and statistics Algorithms Helpful but not

Active Learning and Optimized Information Gathering Lecture 1 Introduction CS 101.2 Andreas Krause Overview Research-oriented special topics course 3 main topics Sequential decision making / bandit problems Statistical active learning

413 views • 26 slides

Steve Kroon PLEASED: Planning, Learning, and Search for Decision-making.

Steve Kroon PLEASED: Planning, Learning, and Search for Decision-making. http://www.cs.sun.ac.za/~kroon/decision.html Maties Machine Learning: 25 October 2019 This group considers almost any aspect of the general decision-making problem,

716 views • 12 slides

DECISION MAKING readysetpresent.com Decision Making Program Objectives ( 1 of 2 ) To examine

DECISION MAKING readysetpresent.com Decision Making Program Objectives ( 1 of 2 ) To examine different decision making models. To maximize creativity and logic in decision making. To acquire decision making tips and techniques that

2.35k views • 175 slides

MI MI and Shared MI MI and Shared and Shared Decision Making and Shared Decision Making

The project PRECIOUS has received funding from the European Union s Seventh Framework Programme under Grant Agreement n 611366" MI MI and Shared MI MI and Shared and Shared Decision Making and Shared Decision Making Decision Making

412 views • 20 slides

Decision Making 1 Decision Making Skills Establishing a positive decision-making environment.

Decision Making 1 Decision Making Skills Establishing a positive decision-making environment. Generating potential solutions. Evaluating the solutions. Deciding. How did you score? Checking the decision. Communicating and

938 views • 50 slides

Module 2 Probability Theory CS 886 Sequential Decision Making and Reinforcement Learning

Module 2 Probability Theory CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo 1 CS886 (c) 2013 Pascal Poupart A Decision Making Scenario You are considering to buy a used car Is it in good

784 views • 41 slides

Module 4 Markov Processes CS 886 Sequential Decision Making and Reinforcement Learning

Module 4 Markov Processes CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo Sequential Decision Making In general: exponentially large decision tree s1 a b . 9 . 1 . 2 . 8 s2 s3 s12 s13 a b a b

364 views • 12 slides

Overview of Robot Decision Making Prof. Yuke Zhu Fall 2020 CS391R: Robot Learning (Fall 2020) 1

Overview of Robot Decision Making Prof. Yuke Zhu Fall 2020 CS391R: Robot Learning (Fall 2020) 1 Todays Agenda What is Robot Decision Making? Mathematical Framework of Sequential Decision Making Learning for Decision Making

382 views • 35 slides

Module 6 Value Iteration CS 886 Sequential Decision Making and Reinforcement Learning

Module 6 Value Iteration CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo Markov Decision Process Definition Set of states: Set of actions (i.e., decisions): Transition model: Pr

423 views • 21 slides

Module 3 Utility Theory CS 886 Sequential Decision Making and Reinforcement Learning University

Module 3 Utility Theory CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo 1 CS886 (c) 2013 Pascal Poupart Decision Making under Uncertainty I give planning problem to robot: I want coffee but coffee

477 views • 30 slides

Module 11 Introduction to Reinforcement Learning CS 886 Sequential Decision Making and

Module 11 Introduction to Reinforcement Learning CS 886 Sequential Decision Making and Reinforcement Learning University of Waterloo Machine Learning Supervised Learning Teacher tells learner what to remember Reinforcement Learning

401 views • 29 slides

$ Lesson One Making Decisions 04/09 the decision-making process The decision-making process

Presentation Slides $ Lesson One Making Decisions 04/09 the decision-making process The decision-making process Identify the problem Gather information and list possible alternatives Consider consequences of each

314 views • 7 slides

6 Decision- -Making Making MVC (revisited) 6 Decision MVC (revisited) decision

6 Decision- -Making Making MVC (revisited) 6 Decision MVC (revisited) decision decision - - m m a a king and games king and games model state instance core structures levels of decision levels of decision- -making

148 views • 4 slides

Medical Decision Making Learning: Decision Trees Artificial Intelligence CSPP 56553 February

Medical Decision Making Learning: Decision Trees Artificial Intelligence CSPP 56553 February 11, 2004 Agenda Decision Trees: Motivation: Medical Experts: Mycin Basic characteristics Sunburn example From trees to rules

578 views • 28 slides

CS 680: GAME AI WEEK 4: DECISION MAKING IN RTS GAMES 2/6/2012 Santiago Ontan

CS 680: GAME AI WEEK 4: DECISION MAKING IN RTS GAMES 2/6/2012 Santiago Ontan santi@cs.drexel.edu https://www.cs.drexel.edu/~santi/teaching/2012/CS680/intro.html Reminders Projects: Project 1 is simpler than it seems: 1)

1.12k views • 89 slides

Heuristic Theory of Decision-Making: Evidence and Implications for Career Guidance AIOSP/IAEVG

A PPLIED V OCATIONAL P SYCHOLOGY AND P OLICY R ESEARCH U NIT Heuristic Theory of Decision-Making: Evidence and Implications for Career Guidance AIOSP/IAEVG International Conference Buenos Aires, Argentina, September 18-20, 2008 Jean-Jacques

593 views • 30 slides

The Command Shell, Libraries and Clients, Formatted Printing Fundamentals of Computer Science

The Command Shell, Libraries and Clients, Formatted Printing Fundamentals of Computer Science Outline Graphical User Interface vs. Command Shell Starting the Command Shell Commands Compiling and Running a Java Program File

474 views • 36 slides

Round-Tripping Balls Building A Bi-Directional Printer/Parser 1 / 46 Introducing: Enterprise

Round-Tripping Balls Building A Bi-Directional Printer/Parser 1 / 46 Introducing: Enterprise JSON 2 / 46 Introducing: Enterprise JSON datetime "27/6/2013 10:29 pm" 2 / 46 Introducing: Enterprise JSON datetime

973 views • 82 slides

Supplier selection in Electrical &Electronic industry from a sustainable point of view By:

Supplier selection in Electrical &Electronic industry from a sustainable point of view By: Atefeh Amindoust Shamsuddin Ahmed Ali Saghafinia OUTLINE INTRODUCTION OBJECTIVES RESEARCH METHODOLOGY CASE STUDY RESULTS AND

707 views • 13 slides

Decision making A decision is an intention to use/not to use a factor at a given level:

Decision making A decision is an intention to use/not to use a factor at a given level: Use 4 kg of concentrates per cow Cull cow no. 678 Call for the vet! Build a new barn. 1 Necessary information When a decision is made

474 views • 13 slides

Resul sults ts Base sed d Accounta countability bility Are we really making a difference?

Resul sults ts Base sed d Accounta countability bility Are we really making a difference? www.resultsaccountability.com The RBA Framework Aims to Define the results we are seeking in our community, for the clients served, for our

1.21k views • 32 slides

APPROACHES TO MANAGEMENT Dr.M. Thenmozhi Professor Department of Management Studies Indian

APPROACHES TO MANAGEMENT Dr.M. Thenmozhi Professor Department of Management Studies Indian Institute of Technology Madras Chennai 600 036 E-mail: mtm@iitm.ac.in APPROACHES TO MANAGEMENT 1) Empirical Approach 2) Human Behaviour Approach

458 views • 17 slides