er dcops a framework for dcop with uncertainty in
play

ER-DCOPS: A FRAMEWORK FOR DCOP WITH UNCERTAINTY IN CONSTRAINT - PowerPoint PPT Presentation

Apr 06, 2024 •380 likes •1.01k views

1 ER-DCOPS: A FRAMEWORK FOR DCOP WITH UNCERTAINTY IN CONSTRAINT UTILITIES Tiep Le, Ferdinando Fioretto, William Yeoh, Tran Cao Son, Enrico Pontelli Computer Science Department New Mexico State University 2 OUTLINE BACKGROUND &

  1. 1 ER-DCOPS: A FRAMEWORK FOR DCOP WITH UNCERTAINTY IN CONSTRAINT UTILITIES Tiep Le, Ferdinando Fioretto, William Yeoh, Tran Cao Son, Enrico Pontelli Computer Science Department New Mexico State University

  2. 2 OUTLINE • BACKGROUND & MOTIVATION • ER-DCOP • ER-DPOP ALGORITHM • EXPERIMENTAL RESULTS • CONCLUSION

  3. 3 OUTLINE • BACKGROUND & MOTIVATION • ER-DCOP • ER-DPOP ALGORITHM • EXPERIMENTAL RESULTS • CONCLUSION

  4. 4 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > x 3 x 1 x 2

  5. 5 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > x 3 x 1 x 2 D 1 = D 2 = {0} D 3 = {0, 1}

  6. 6 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > x 3 f 13 f 23 x 1 x 2 D 1 = D 2 = {0} D 3 = {0, 1}

  7. 7 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > f 13 f 23 x 3 x 1 x 3 U 13 x 2 x 3 U 23 f 13 f 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 D 1 = D 2 = {0} D 3 = {0, 1}

  8. 8 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > f 13 f 23 x 3 x 1 x 3 U 13 x 2 x 3 U 23 f 13 f 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 D 1 = D 2 = {0} Worker x 1 owns variable x 1 Worker x 2 owns variable x 2 D 3 = {0, 1} Assistant robot x 3 owns variable x 3

  9. 9 DISTRIBUTED CONSTRAINT OPTIMZATION PROBLEMS • DCOP P = < Χ , D, F, A, α > • Goal: The assignment for all variables maximizes the aggregate utility f 13 f 23 x 3 x 1 x 3 U 13 x 2 x 3 U 23 f 13 f 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 D 1 = D 2 = {0} Worker x 1 owns variable x 1 Worker x 2 owns variable x 2 D 3 = {0, 1} Assistant robot x 3 owns variable x 3

  10. 10 MOTIVATION • In real-world applications, the utilities are stochastic. f 23 x 2 x 3 U 23 0 0 (Fail) 0 (Success) 40 0 1 (Fail) 0 (Success) 50

  11. 11 UR-DCOP • In real-world applications, the utilities are stochastic. f 23 x 2 x 3 U 23 Good Bad 0 0 (Fail) 0 50% 90% (Success) 40 50% 10% 0 1 (Fail) 0 90% 50% (Success) 50 10% 50% • Stochastic utilities can be sampled from a known probability distribution space.

  12. 12 MOTIVATION • In real-world applications, the utilities are stochastic. f 23 x 2 x 3 U 23 Good Bad 0 0 (Fail) 0 50% 90% (Success) 40 50% 10% 0 1 (Fail) 0 50% 20% (Success) 50 50% 80% • Stochastic utilities can be sampled from a known probability distribution space. • Expected-regret

  13. 13 MOTIVATION • In real-world applications, the utilities are stochastic. f 23 x 2 x 3 U 23 Good Bad 0 0 (Fail) 0 50% 90% (Success) 40 50% 10% 0 1 (Fail) 0 50% 20% (Success) 50 50% 80% • Stochastic utilities can be sampled from a known probability distribution space. ER-DCOP framework!

  14. 14 OUTLINE • BACKGROUND • ER-DCOP • ER-DPOP ALGORITHM • EXPERIMENTAL RESULTS • CONCLUSION

  15. 15 EXPECTED REGRET-DCOP (ER-DCOP) x 3 x 1 x 3 U 13 x 2 x 3 U 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2

  16. 16 EXPECTED REGRET-DCOP (ER-DCOP) x 3 x 1 x 3 U 13 x 2 x 3 U 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 x 2 x 3 U 23 0 0 (Fail) 0 (Success) 40 0 1 (Fail) 0 (Success) 50

  17. 17 EXPECTED REGRET-DCOP (ER-DCOP) x 3 x 1 x 3 U 13 x 2 x 3 U 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 x 2 x 3 r 2 U 23 0 0 0 (Fail) 0 1 (Success) 40 0 1 0 (Fail) 0 1 (Success) 50

  18. 18 EXPECTED REGRET-DCOP (ER-DCOP) x 3 x 1 x 3 U 13 x 2 x 3 U 23 0 0 50 0 0 40 0 1 30 0 1 50 x 1 x 2 x 2 x 3 r 2 U 23 Good Bad 0 0 0 (Fail) 0 50% 90% Good: 12% 1 (Success) 40 50% 10% Bad: 88% 0 1 0 (Fail) 0 20% 50% 1 (Success) 50 80% 50%

  19. 19 EXPECTED REGRET-DCOP (ER-DCOP) • ER-DCOP P = < Χ , D, A, α , R, S, F> x 1 x 3 r 1 U 13 Good Bad x 2 x 3 r 2 U 23 Good Bad 0 0 0 (Fail) 0 10% 30% 0 0 0 (Fail) 0 50% 90% 1 (Success) 50 90% 70% 1 (Success) 40 50% 10% 0 1 0 (Fail) 0 30% 50% 0 1 0 (Fail) 0 20% 50% 1 (Success) 30 70% 50% 1 (Success) 50 80% 50%

  20. 20 EXPECTED REGRET-DCOP (ER-DCOP) • ER-DCOP P = < Χ , D, A, α , R, S, F> • belief of r 1 , belief of r 2 • x : joint belief for all random variables x 1 x 3 r 1 U 13 Good x 2 x 3 r 2 U 23 Good 0 0 0 (Fail) 0 10% 0 0 0 (Fail) 0 50% 1 (Success) 50 90% 1 (Success) 40 50% 0 1 0 (Fail) 0 30% 0 1 0 (Fail) 0 20% 1 (Success) 30 70% 1 (Success) 50 80%

  21. 21 EXPECTED REGRET-DCOP (ER-DCOP) • ER-DCOP P = < Χ , D, A, α , R, S, F> • Using Expected Utility (EU) consider only 1 joint belief of good weather x 1 x 3 r 1 U 13 Good EU x 2 x 3 r 2 U 23 Good EU 0 0 0 (Fail) 0 10% 45 0 0 0 (Fail) 0 50% 20 1 (Success) 50 90% 1 (Success) 40 50% 0 1 0 (Fail) 0 30% 21 0 1 0 (Fail) 0 20% 40 1 (Success) 30 70% 1 (Success) 50 80%

  22. 22 EXPECTED REGRET-DCOP (ER-DCOP) bad weather x 1 x 2 x 3 EU 0 0 0 39 0 0 1 40 Optimal assignment if bad weather (EU = 40) good weather x 1 x 2 x 3 EU Optimal assignment if good weather (EU = 65) 0 0 0 65 0 0 1 41 Belief Space Good: 12% Bad: 88%

  23. 23 EXPECTED REGRET-DCOP (ER-DCOP) bad weather x 1 x 2 x 3 EU Regret 0 0 0 39 40-39=1 0 0 1 40 40-40=0 Assignment x 1 = x 2 = x 3 = 0 has regret of 1 if bad weather good weather regret of 0 if good weather x 1 x 2 x 3 EU Regret 0 0 0 65 65-65=0 0 0 1 41 65-61=4 Belief Space Good: 12% Bad: 88%

  24. 24 EXPECTED REGRET-DCOP (ER-DCOP) bad weather x 1 x 2 x 3 EU Regret 0 0 0 39 40-39=1 88% 0 0 1 40 40-40=0 Expected-Regret (ER) x 1 x 2 x 3 ER 0 0 0 12%*0 + 88%*1 = 0.88 good weather 0 0 1 12%*4 + 88%*0 = 0.48 x 1 x 2 x 3 EU Regret 12% 0 0 0 65 65-65=0 0 0 1 41 65-61=4 Belief Space Good: 12% Bad: 88%

  25. 25 EXPECTED REGRET-DCOP (ER-DCOP) bad weather x 1 x 3 EU x 2 x 3 EU Regret 0 0 35 0 0 4 40-39=1 88% 0 1 15 0 1 25 40-40=0 Expected-Regret (ER) x 1 x 2 x 3 ER 0 0 0 12%*0 + 88%*1 = 0.88 good weather 0 0 1 12%*4 + 88%*0 = 0.48 x 1 x 3 EU x 2 x 3 EU Regret 12% 0 0 45 0 0 20 65-65=0 0 1 21 0 1 40 65-61=4 The solution minimizes the expected-regret

  26. 26 OUTLINE • BACKGROUND • ER-DCOP • ER-DPOP ALGORITHM • EXPERIMENTAL RESULTS • CONCLUSION

  27. 27 ER-DPOP • Phase 1: Generation of the pseudo-tree x 3 x 1 x 2

  28. 28 ER-DPOP • Phase 2: Resolution of subproblems x 1 x 3 r 1 U 13 Good Bad 0 0 0 (Fail) 0 10% 30% x 3 1 (Success) 50 90% 70% 0 1 0 (Fail) 0 30% 50% x 1 x 2 1 (Success) 30 70% 50% x 3 EU(Good) EU(Bad) 0 45 35 1 21 15 EU = Expected Utility

  29. 29 ER-DPOP • Phase 2: Resolution of subproblems x 2 x 3 r 2 U 23 Good Bad 0 0 0 (Fail) 0 50% 90% x 3 1 (Success) 40 50% 10% 0 1 0 (Fail) 0 20% 50% x 1 x 2 1 (Success) 50 80% 50% x 3 EU(Good) EU(Bad) 0 20 4 1 40 25 EU = Expected Utility

  30. 30 ER-DPOP • Phase 2: Resolution of subproblems x 3 EU(Good) EU(Bad) x 3 0 45+20=65 35+4=39 1 21+40=61 15+25=40 x 1 x 2 EU = Expected Utility

  31. 31 ER-DPOP • Phase 3: Resolution of the main problems • Generate DCOP with expected-regret as utilities • Use DPOP [Petcu et al. AAAI2007] to solve that DCOP x 1 x 3 EU(Good) EU(Bad) x 2 x 3 EU(Good) EU(Bad) 0 0 45 35 0 0 20 4 0 1 21 15 0 1 40 25 x 1 x 3 Expected-Regret x 2 x 3 Expected-Regret 0 0 12%*(45-45) + 0 0 12%*(20-20) + 88%*(15-35) = -17.6 88%*(25-4) = 18.48 0 1 12%*(45-21) + 0 1 12%*(20-40) + 88%*(15-15) = 2.88 88%*(25-25) = 2.4

  32. 32 ER-DPOP IMPLEMENTATIONS • GPU-ER-DPOP (GPU-based ER-DPOP) • Utilizes the parallelism offered by Graphical Processing Unit (GPU) to speed up computations in ER-DPOP • ASP-ER-DPOP (ASP-based ER-DPOP) • Prunes the search space offered by logic-programming based inference rules in Answer Set Programming (ASP)

  33. 33 RELATED WORK • UR-DCOP (F. Wu et al. AAAI 2014) • Beliefs of random variables are independent with values of decision variables; • Belief space does not exhibit probabilistic model; • Minimizing the worst-case loss (regret) over belief space.

  34. 34 OUTLINE • BACKGROUND • ER-DCOP • ER-DPOP ALGORITHM • EXPERIMENTAL RESULTS • CONCLUSION

  35. 35 EXPERIMENTAL RESULTS • Algorithms: • GPU-ER-DPOP • ASP-ER-DPOP • FRODO-ER (solve subproblems in Phase 2 sequentially) • Domains: • Random Graph (varying |X|, |D|, constraint density p 1 , constraint tightness p 2 , or belief space’s size) • Power Network Problems (varying Topology or |D|)

  36. 36 EXPERIMENTAL RESULTS |X| ASP-ER-DPOP GPU-ER-DPOP FRODO-ER 8 3.1 0.1 0.3 13 9.4 0.2 61.1 18 44.1 N/A N/A 23 120.8 N/A N/A runtime in second |D| ASP-ER-DPOP GPU-ER-DPOP FRODO-ER N/A: not available 4 4.5 0.1 1.8 6 8.9 0.1 33.6 8 22.2 1.2 143.2 10 80.4 4.8 N/A 12 121.2 15.4 N/A Random Graphs

  37. 37 EXPERIMENTAL RESULTS |A| = 13, |X| = 74, |F| = 51 |A| = 37, |X| = 218, |F| = 147 10 6 10 6 Simulated Runtime (ms) Simulated Runtime (ms) 10 5 10 5 10 4 10 4 10 3 10 3 ASP-ER ASP-ER Frodo-ER Frodo-ER 10 2 10 2 3 5 7 9 11 13 15 3 5 7 9 11 13 15 Domain Size Domain Size |A| = 124, |X| = 748, |F| = 497 10 6 Simulated Runtime (ms) 10 5 10 4 Power Network Problems 10 3 ASP-ER Frodo-ER 10 2 3 5 7 9 11 13 15 Domain Size

Recommend

Outline Special Topics in AI: Intelligent Agents and Multi-Agent Systems Introduction

Outline Special Topics in AI: Intelligent Agents and Multi-Agent Systems Introduction

23/10/2012 Outline Special Topics in AI: Intelligent Agents and Multi-Agent Systems Introduction DCOP for MAS how to model problems in the DCOP framework Distributed Constraint Optimization Solution Techniques for DCOPs (Exact

441 views • 12 slides
A Dynamic Programming-based MCMC Framework for Solving DCOPs with GPUs Ferdinando Fioretto 1(2)

A Dynamic Programming-based MCMC Framework for Solving DCOPs with GPUs Ferdinando Fioretto 1(2)

A Dynamic Programming-based MCMC Framework for Solving DCOPs with GPUs Ferdinando Fioretto 1(2) (joint work with) William Yeoh 2 and Enrico Pontelli 2 1 University of Michigan 2 New Mexico State University CP 2016, Toulouse Introduction GPUs

796 views • 27 slides
Intro to Artificial Intelligence CS 171 Reasoning Under Uncertainty Chapter 13 and 14.1-14.2

Intro to Artificial Intelligence CS 171 Reasoning Under Uncertainty Chapter 13 and 14.1-14.2

Intro to Artificial Intelligence CS 171 Reasoning Under Uncertainty Chapter 13 and 14.1-14.2 Andrew Gelfand 3/1/2011 Today Representing uncertainty is useful in knowledge bases o Probability provides a coherent framework for uncertainty

802 views • 59 slides
Uncertainty AIMA Chapter 13 Outline Uncertainty Uncertainty Probability Syntax and

Uncertainty AIMA Chapter 13 Outline Uncertainty Uncertainty Probability Syntax and

Uncertainty Uncertainty AIMA Chapter 13 Outline Uncertainty Uncertainty Probability Syntax and Semantics Inference Independence and Bayes Rule Uncertainty Uncertainty Let action A t = leave for airport t minutes before

999 views • 46 slides
A Preliminary Study on Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings

A Preliminary Study on Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings

A Preliminary Study on Preference Elicitation in DCOPs for Scheduling Devices in Smart Buildings Atena M. Tabakhi Ferdinando Fioretto William Yeoh New Mexico State University July 9, 2016 Home Automation Fig.1 Fig.2 2 The culture of

1.02k views • 29 slides
A DCOP Approach to the Economic Dispatch with Demand Response Ferdinando Fioretto 1 , William Yeoh

A DCOP Approach to the Economic Dispatch with Demand Response Ferdinando Fioretto 1 , William Yeoh

A DCOP Approach to the Economic Dispatch with Demand Response Ferdinando Fioretto 1 , William Yeoh 2 , Enrico Pontelli 2 , Ye Ma 3 , Satishkumar J. Ranade 2 1 University of Michigan 2 New Mexico State University 3 Siemens Industry Inc. May, 2017

641 views • 28 slides
Knowledge-Uncertainty Axiomatized Framework with Support Vector Machines for Hyperparameter

Knowledge-Uncertainty Axiomatized Framework with Support Vector Machines for Hyperparameter

Knowledge-Uncertainty Axiomatized Framework with Support Vector Machines for Hyperparameter Optimization Marcin Orchel AGH University of Science and Technology in Poland 1 / 54 1 Introduction 2 Problem Definition 3 Solution 4 SVM 5 Measures of

927 views • 55 slides
Uncertainty Quantification Framework for Modeling Prediction Michael Frenklach Collaborators:

Uncertainty Quantification Framework for Modeling Prediction Michael Frenklach Collaborators:

Columbia University October 10, 2014 Uncertainty Quantification Framework for Modeling Prediction Michael Frenklach Collaborators: Andy Packard W. A. Lester, Jr. (DFT) P. Westmoreland (ALS) N. Slavinskaya (SynGas) R.

808 views • 67 slides
Decision Models Provide a framework for decision making under uncertainty Help structure

Decision Models Provide a framework for decision making under uncertainty Help structure

Conducting and Implementing CEAs Karen Kuntz, ScD Recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine Decision Models Provide a framework for decision making under uncertainty Help structure the

848 views • 39 slides
UNCERTAINTY IN KNOWLEDGE Ch. 9 Uncertainty in Knowledge 1 Sources of Uncertainty

UNCERTAINTY IN KNOWLEDGE Ch. 9 Uncertainty in Knowledge 1 Sources of Uncertainty

UNCERTAINTY IN KNOWLEDGE Ch. 9 Uncertainty in Knowledge 1 Sources of Uncertainty Uncertainty in problem solving can be attributed to Imperfect domain knowledge Imperfect case data Experts employ inexact methods for two

605 views • 31 slides
An uncertainty framework for marine indicators based on monitoring data - Variations in time,

An uncertainty framework for marine indicators based on monitoring data - Variations in time,

HELCOM workshop, Uppsala 18 May 2015 An uncertainty framework for marine indicators based on monitoring data - Variations in time, space and methodology Jacob Carstensen Department of Bioscience Aarhus University www.waters.gu.se

543 views • 32 slides
Reducing the Search Space of Resource Constrained DCOPs T. Matsui 1) , M. Silaghi 2) , K.

Reducing the Search Space of Resource Constrained DCOPs T. Matsui 1) , M. Silaghi 2) , K.

Reducing the Search Space of Resource Constrained DCOPs T. Matsui 1) , M. Silaghi 2) , K. Hirayama 3) , M. Yokoo 4) , B. Faltings 5) and H. Matsuo 1) 1) Nagoya Institute of Technology, 2) Florida Institute of Technology, 3) Kobe University, 4)

567 views • 22 slides
Uncertainty and its Representa/on @kordinglab Uncertainty ma7ers

Uncertainty and its Representa/on @kordinglab Uncertainty ma7ers

Uncertainty and its Representa/on @kordinglab Uncertainty ma7ers The brain is good with uncertainty Measure world + uncertainty Derive op/mal behavior

240 views • 11 slides
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs William Kluegel 1 ,

A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs William Kluegel 1 ,

A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs William Kluegel 1 , Muhammad Iqbal 1 , Ferdinando Fioretto 2 , William Yeoh 1 , Enrico Pontelli 1 1 New Mexico State University 2 University of Michigan May, 2017

287 views • 28 slides
Modeling the Catchment Via Mixtures: an Uncertainty Framework for Dynamic Hydrologic Systems

Modeling the Catchment Via Mixtures: an Uncertainty Framework for Dynamic Hydrologic Systems

Modeling the Catchment Via Mixtures: an Uncertainty Framework for Dynamic Hydrologic Systems Dynamic Hydrologic Systems Lucy Marshall Assistant Professor of Watershed Analysis A i P f f W h d A l i Department of Land Resources and

580 views • 30 slides
A Bayesian framework for optimal motion planning with uncertainty Andrea Censi, Daniele Calisi,

A Bayesian framework for optimal motion planning with uncertainty Andrea Censi, Daniele Calisi,

A Bayesian framework for optimal motion planning with uncertainty Andrea Censi, Daniele Calisi, Giuseppe Oriolo, Alessandro De Luca direct path is unsafe start safe motion robot makes detour covariance shrinks to better localize start

531 views • 23 slides
Uncertainty 10 AI Slides (5e) c Lin Zuoquan@PKU 2003-2019 10 1 10 Uncertainty 10.1

Uncertainty 10 AI Slides (5e) c Lin Zuoquan@PKU 2003-2019 10 1 10 Uncertainty 10.1

Uncertainty 10 AI Slides (5e) c Lin Zuoquan@PKU 2003-2019 10 1 10 Uncertainty 10.1 Uncertainty 10.2 Probability Syntax and semantics Inference Independence Bayes rule 10.3 Bayesian networks 10.4 Probabilistic reasoning

1.38k views • 102 slides
Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou Makoto Yokoo William Yeoh

Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou Makoto Yokoo William Yeoh

Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou Makoto Yokoo William Yeoh Roie Zivan Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou Makoto Yokoo William Yeoh Roie Zivan Content Electric Vehicle

592 views • 30 slides
Economic and technology uncertainty and implications for policy advise Fr ed eric Babonneau

Economic and technology uncertainty and implications for policy advise Fr ed eric Babonneau

Methods and models Uncertainty and role of CCS in abatement strategies Uncertainty on other drivers New methods to tackle uncertainty analysis for energy/climate policy Publications Economic and technology uncertainty and implications for

847 views • 27 slides
Infinite-Horizon Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou William Yeoh

Infinite-Horizon Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou William Yeoh

Infinite-Horizon Proactive Dynamic DCOPs Khoi Hoang Ferdinando Fioretto Ping Hou William Yeoh Roie Zivan Makoto Yokoo New Mexico State University All About Discovery! nmsu.edu New Mexico State University Outline Distributed Constraint

1k views • 89 slides
Decision Making Privacy-Motivated . . . under Uncertainty: Uncertainty Leads to . . .

Decision Making Privacy-Motivated . . . under Uncertainty: Uncertainty Leads to . . .

Quantitative . . . The Notion of Utility Traditional Approach: . . . Need for Distributed . . . Decision Making Privacy-Motivated . . . under Uncertainty: Uncertainty Leads to . . . Uncertainty in . . . Algorithmic Approach Uncertainty in

668 views • 62 slides
Reasoning with Uncertainty C h a p t e r 13 1 Outline Uncertainty Probability

Reasoning with Uncertainty C h a p t e r 13 1 Outline Uncertainty Probability

Reasoning with Uncertainty C h a p t e r 13 1 Outline Uncertainty Probability Syntax and Semantics Inference Independence and Bayes Rule 2 The real world is an uncertain place... Example: I need a plan that will get

744 views • 38 slides
Can you trust your models uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Can you trust your models uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Can you trust your models uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia*, Emily Feruig*, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, Jasper Snoek Uncertainty?

677 views • 19 slides
Uncertainty Chapter 13 Chapter 13 1 Outline Uncertainty Probability Syntax and

Uncertainty Chapter 13 Chapter 13 1 Outline Uncertainty Probability Syntax and

Uncertainty Chapter 13 Chapter 13 1 Outline Uncertainty Probability Syntax and Semantics Inference Independence and Bayes Rule Chapter 13 2 Uncertainty Let action A t = leave for airport t minutes before flight Will A t

689 views • 34 slides

More recommend