bayesian counterfactual risk minimization
play

Bayesian Counterfactual Risk Minimization Ben London (blondon@) - PowerPoint PPT Presentation

Apr 20, 2023 •478 likes •593 views

Bayesian Counterfactual Risk Minimization Ben London (blondon@) Amazon Music Ted Sandler (sandler@) Amazon Music International Conference on Machine Learning Long Beach, CA, June 11, 2019 Learning from Logged Data Pull log data e.g., user i

  1. Bayesian Counterfactual Risk Minimization Ben London (blondon@) Amazon Music Ted Sandler (sandler@) Amazon Music International Conference on Machine Learning Long Beach, CA, June 11, 2019

  2. Learning from Logged Data Pull log data e.g., user i listened to item j Launch new Design/train if unsuccessful policy new rec policy if successful A/B test new policy vs old policy

  3. Problem 1: Bandit Feedback • Only observe outcomes from actions taken • e.g., only get feedback on recommendations Alexa, play music Here’s a station you might like …

  4. Problem 2: Bias • Logged data is biased • Policy typically not uniform distribution high support → better estimate • User typically doesn’t see everything • Bias affects inferences • Self-fulfilling prophecies; “rich get richer” low support • Miss key insights due to insufficient support → who knows?

  5. <latexit sha1_base64="PSf7DjZY4iJDoPC0SXiPXT/dCmw=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="vq06mY9CIP/+RPZL4LrwG8KBOrc=">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</latexit> IPS Policy Optimization • Use inverse propensity score (IPS) estimator n 1 π ( a i | x i ) X logged propensity p i = π 0 ( a i | x i ) arg min − r i n p i π i =1 • IPS is an unbiased estimator of expected reward n a ∼ π ( x ) [ ρ ( x, a )] ≈ 1 π ( a i | x i ) X E E r i ( x, ρ ) ∼ D n p i i =1 • Caveat: logging policy must have full support

  6. <latexit sha1_base64="vq06mY9CIP/+RPZL4LrwG8KBOrc=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> <latexit sha1_base64="8NS4mO5kWFTfLpxFhH3ZJ5ymWCA=">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</latexit> IPS Policy Optimization • Use inverse propensity score (IPS) estimator n 1 π ( a i | x i ) X logged propensity p i = π 0 ( a i | x i ) arg min − r i n p i π i =1 • Problem: IPS has high variance ! " ($|&) ! " ($|&) !($|&) !((|&)

  7. <latexit sha1_base64="IUqBlo6BGJCKN8j7pt+Y8e1iNg=">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</latexit> <latexit sha1_base64="cWvFQAa06y/zjgRhGkPYJf5mUuE=">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</latexit> CRM Principle • Counterfactual Risk Minimization (CRM) principle n 1 π ( a i | x i ) q X ˆ arg min + λ Var( π , S ) − r i n p i π i =1 variance regularization • Motivated by PAC risk analysis • Stochastic optimization of variance regularizer is tricky • Policy optimization for exponential models (POEM) algorithm [Swaminathan & Joachims, ICML 2015]

Recommend

Counterfactual Regret Minimization and Domination in Extensive-Form Games Richard Gibson

Counterfactual Regret Minimization and Domination in Extensive-Form Games Richard Gibson

Counterfactual Regret Minimization and Domination in Extensive-Form Games Richard Gibson University of Alberta Edmonton, Alberta, Canada Overview Counterfactual Regret Minimization (CFR) Overview Counterfactual Regret Minimization (CFR)

479 views • 36 slides
Counterfactual Regret Minimization Gabriele Farina 1 Christian Kroer 2 Noam Brown 1 Tuomas Sandholm

Counterfactual Regret Minimization Gabriele Farina 1 Christian Kroer 2 Noam Brown 1 Tuomas Sandholm

Stable-Predictive Optimistic Counterfactual Regret Minimization Gabriele Farina 1 Christian Kroer 2 Noam Brown 1 Tuomas Sandholm 1,3 1 Computer Science Department, Carnegie Mellon University 2 IEOR Department, Columbia University 3 Strategic

283 views • 5 slides
Empirical Risk Minimization October 29, 2015 Outline Empirical risk minimization view

Empirical Risk Minimization October 29, 2015 Outline Empirical risk minimization view

Empirical Risk Minimization October 29, 2015 Outline Empirical risk minimization view Perceptron CRF Notation for Linear Models Training data: {(x 1 , y 1 ), (x 2 , y 2 ), , (x N , y N )} Testing data: {(x N+1 , y N+1 ),

418 views • 21 slides
Introduction to Machine Learning CMU-10701 10. Risk Minimization Barnabs Pczos 10. Risk

Introduction to Machine Learning CMU-10701 10. Risk Minimization Barnabs Pczos 10. Risk

Introduction to Machine Learning CMU-10701 10. Risk Minimization Barnabs Pczos 10. Risk Minimization 2 What have we seen so far? Several algorithms that seem to work fine on training datasets: Linear regression Nave Bayes

1.13k views • 51 slides
CSC2412: Private Gradient Descent &amp; Empirical Risk Minimization Sasho Nikolov 1 Empirical

CSC2412: Private Gradient Descent &amp; Empirical Risk Minimization Sasho Nikolov 1 Empirical

CSC2412: Private Gradient Descent &amp; Empirical Risk Minimization Sasho Nikolov 1 Empirical Risk Minimization Learning: Reminder Known data universe X and an unknown probability distribution D on X Known concept class C and an unknown

341 views • 17 slides
Introduction to Machine Learning ML-Basics: Losses &amp; Risk Minimization Learning goals Know

Introduction to Machine Learning ML-Basics: Losses &amp; Risk Minimization Learning goals Know

Introduction to Machine Learning ML-Basics: Losses &amp; Risk Minimization Learning goals Know the concept of loss Understand the relationship between loss and risk Understand the relationship between risk minimization and finding the best

645 views • 10 slides
Introduction to Machine Learning Risk Minimization Barnabs Pczos What have we seen so far?

Introduction to Machine Learning Risk Minimization Barnabs Pczos What have we seen so far?

Introduction to Machine Learning Risk Minimization Barnabs Pczos What have we seen so far? Several classification &amp; regression algorithms seem to work fine on training datasets: Linear regression Gaussian Processes Nave

802 views • 53 slides
Suboptimality of Penalized Empirical Risk Minimization in Classification. Guillaume Lecu e

Suboptimality of Penalized Empirical Risk Minimization in Classification. Guillaume Lecu e

Suboptimality of Penalized Empirical Risk Minimization in Classification. Guillaume Lecu e Universit e Paris 6 COLT 2007, June 13 General Framework. Aggregations Procedures. Optimality in classification. Motivation. M prior estimators

708 views • 54 slides
WEIGHTED SUMS OF RANDOM KITCHEN SINKS Replacing minimization with randomization in learning

WEIGHTED SUMS OF RANDOM KITCHEN SINKS Replacing minimization with randomization in learning

WEIGHTED SUMS OF RANDOM KITCHEN SINKS Replacing minimization with randomization in learning The model Given a set of training data in a domain Fit a function to minimize risk Empirical Risk Risk Loss Function Hinge loss

621 views • 13 slides
High Order Methods for Empirical Risk Minimization Alejandro Ribeiro Department of Electrical and

High Order Methods for Empirical Risk Minimization Alejandro Ribeiro Department of Electrical and

High Order Methods for Empirical Risk Minimization Alejandro Ribeiro Department of Electrical and Systems Engineering University of Pennsylvania aribeiro@seas.upenn.edu Thanks to: Aryan Mokhtari, Mark Eisen, ONR, NSF DIMACS Workshop on

775 views • 40 slides
Di ff erentially Private Empirical Risk Minimization with Non-convex Loss Functions Di Wang ,

Di ff erentially Private Empirical Risk Minimization with Non-convex Loss Functions Di Wang ,

Di ff erentially Private Empirical Risk Minimization with Non-convex Loss Functions Di Wang , Changyou Chen and Jinhui Xu State University of New York at Bu ff alo International Conference on Machine Learning 2019 Di Wang Non-convex DP-ERM ICML

544 views • 27 slides
Minimization Using Descent Information we will consider the minimization of unconstrained

Minimization Using Descent Information we will consider the minimization of unconstrained

Minimization Using Descent Information we will consider the minimization of unconstrained functions of several variables where we now assume we have some derivative information such as the gradient vector or the Hessian matrix. Recall

131 views • 11 slides
Counterfactual Donkeys and the Modal Horizon Andreas Walker and Maribel Romero Counterfactual

Counterfactual Donkeys and the Modal Horizon Andreas Walker and Maribel Romero Counterfactual

Counterfactual Donkeys and the Modal Horizon Andreas Walker and Maribel Romero Counterfactual Donkeys and the Modal Horizon Andreas Walker and Maribel Romero 1 Introduction Counterfactual Donkey Sentences 2 Readings High and low readings 3

593 views • 30 slides
Detection and Estimation Theory Lecture 13 Mojtaba Soltanalian- UIC msol@uic.edu

Detection and Estimation Theory Lecture 13 Mojtaba Soltanalian- UIC msol@uic.edu

Detection and Estimation Theory Lecture 13 Mojtaba Soltanalian- UIC msol@uic.edu http://msol.people.uic.edu Based on ECE 531 Slides- 2011 (Prof. Natasha Devroye) Bayesian Estimation -- Summary Bayesian Risk From Bayesian Risk to Estimation

886 views • 21 slides
Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max

Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max

Counterfactual Policy Evaluation in Reproducing Kernel Hilbert Spaces Krikamol Muandet Max Planck Institute for Intelligent Systems Tbingen, Germany Jeju, Korea February 22, 2019 Krikamol Muandet Counterfactual Learning in RKHS Jeju,

1.14k views • 61 slides
Minimization Satoru Iwata (University of Tokyo) Submodular Function Minimization ( )

Minimization Satoru Iwata (University of Tokyo) Submodular Function Minimization ( )

Submodular Function Minimization Satoru Iwata (University of Tokyo) Submodular Function Minimization ( ) 0 f Assumption: X Minimization Evaluation Algorithm Oracle ( X ) f Minimizer min{ ( ) | } ? f Y Y V

177 views • 16 slides
Being Bayesian About Being Bayesian About Net work St ruct ure Net work St ruct ure A Bayesian

Being Bayesian About Being Bayesian About Net work St ruct ure Net work St ruct ure A Bayesian

Being Bayesian About Being Bayesian About Net work St ruct ure Net work St ruct ure A Bayesian Approach t o St ruct ure Discovery in Bayesian Net works Nir Friedman and Daphne Koller 04/ 21/ 2005 CS673 1 Roadmap Roadmap Bayesian lear

657 views • 21 slides
One-Dimensional Minimization Lectures for PHD course on Numerical optimization Enrico Bertolazzi

One-Dimensional Minimization Lectures for PHD course on Numerical optimization Enrico Bertolazzi

One-Dimensional Minimization Lectures for PHD course on Numerical optimization Enrico Bertolazzi DIMS Universit a di Trento November 21 December 14, 2011 One-Dimensional Minimization 1 / 33 Outline Golden Section minimization 1

654 views • 33 slides
Counterfactual-based mediation analysis Workshop 1 Rhian Daniel London School of Hygiene and

Counterfactual-based mediation analysis Workshop 1 Rhian Daniel London School of Hygiene and

Counterfactual-based mediation analysis Workshop 1 Rhian Daniel London School of Hygiene and Tropical Medicine CIMPOD 27th February, 2017 Rhian Daniel/Counterfactual-based mediation analysisWorkshop 1 1/51 Setting the scene Case study

1.78k views • 131 slides
Counterfactual-based mediation analysis Workshop 2 Rhian Daniel London School of Hygiene and

Counterfactual-based mediation analysis Workshop 2 Rhian Daniel London School of Hygiene and

Counterfactual-based mediation analysis Workshop 2 Rhian Daniel London School of Hygiene and Tropical Medicine CIMPOD 28th February, 2017 Rhian Daniel/Counterfactual-based mediation analysisWorkshop 2 1/55 Setting the scene Case study

1.85k views • 171 slides
Outline Intro to RL and Bayesian Learning History of Bayesian RL Model-based Bayesian

Outline Intro to RL and Bayesian Learning History of Bayesian RL Model-based Bayesian

Outline Intro to RL and Bayesian Learning History of Bayesian RL Model-based Bayesian RL Prior knowledge, policy optimization, discussion, Bayesian approaches for other RL variants Model-free Bayesian RL Gaussian

1.23k views • 63 slides
A RISK ANALYSIS OF THE MOLYBDENUM-99 SUPPLY CHAIN USING BAYESIAN NETWORKS 2017 Mo-99 Topical

A RISK ANALYSIS OF THE MOLYBDENUM-99 SUPPLY CHAIN USING BAYESIAN NETWORKS 2017 Mo-99 Topical

A RISK ANALYSIS OF THE MOLYBDENUM-99 SUPPLY CHAIN USING BAYESIAN NETWORKS 2017 Mo-99 Topical Meeting Jeffrey Liang, D.Eng. OVERVIEW Motivation Background and Problem Description Research Questions and Limitations Methodology

182 views • 16 slides
Bayesian Learning 1 Outline MLE, MAP vs. Bayesian Learning Bayesian Linear Regression

Bayesian Learning 1 Outline MLE, MAP vs. Bayesian Learning Bayesian Linear Regression

Bayesian Learning 1 Outline MLE, MAP vs. Bayesian Learning Bayesian Linear Regression Bayesian Gaussian Mixture Models Non-parametric Bayes 2 Take Away ... 1. Maximum Likelihood Estimate (MLE) = arg max p ( D| )

609 views • 27 slides
Learning as Loss Minimization Machine Learning 1 Learning as loss minimization The setup

Learning as Loss Minimization Machine Learning 1 Learning as loss minimization The setup

Learning as Loss Minimization Machine Learning 1 Learning as loss minimization The setup Examples x drawn from a fixed, unknown distribution D Hidden oracle classifier f labels examples We wish to find a hypothesis h that mimics f

294 views • 25 slides

More recommend