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Incentivize Crowd Labeling under Budget Constraint Qi Zhang, Yutian Wen, Xiaohua Tian, Xiaoying Gan, Xinbing Wang Shanghai Jiao Tong University, China.

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Presentation on theme: "Incentivize Crowd Labeling under Budget Constraint Qi Zhang, Yutian Wen, Xiaohua Tian, Xiaoying Gan, Xinbing Wang Shanghai Jiao Tong University, China."— Presentation transcript:

1 Incentivize Crowd Labeling under Budget Constraint Qi Zhang, Yutian Wen, Xiaohua Tian, Xiaoying Gan, Xinbing Wang Shanghai Jiao Tong University, China

2 2 Outline Introduction to Crowdsourcing Mechanism Problem Formulation and Mechanism Setting Mechanism Analysis Performance Evaluation 2

3 Background  Crowdsourcing systems leverage human wisdom to : perform tasks, such as:   Image classification  Character recognition  Data collection 3

4 Types of Tasks Types of Tasks  Tasks can be divided into two categories:  Structured  Structured response format Binary choice Multiple choice Real Value  Unstructured  Unstructured response format Logo design 4

5 5 Motivating Example  Example: Image classification Workers Allocation CrowdsourcingPlatform Task Dog Dog Cat Cat Dog Inference Algorithm Dog

6 6 Framework: Reverse Auction (1)Tasks (2)Bids (3)Winning bids determination ( 4)Winning bids ( 6)Payments (5)Answers

7 7 Major Challenges(1)  To design a successful crowdsourcing system  Task Allocation (winning bids) Tasks should be allocated evenly  Payment Determination: Must provide proper incentives (monetary rewards)  Inference Algorithm: Should improve overall accuracy Should address the diversity of the crowd

8 8 Major Challenges(2)  We need to model on  Diverse task difficulty Dog or Cat Older than 30 or Not  Diverse worker quality Cat

9 9 Model on Tasks(1)  We focus on binary choice tasks  Each task is a 0 – 1 question  (Assumption) Each worker is uniformly reliable  Task Soft Label Probability that the task is labeled as 1( by a reliable worker) Crowd Label 0 or 1

10 10 Model on Tasks(2)  The soft label is viewed as a random variable drawn from Beta distribution  Update parameters (a,b) by Bayes rule  Inference The task is inferred as 1 Prior Parameters Prior Posterior Likelihood More than half agree

11 11 Framework: Reverse Auction  The platform publicizes a set of binary tasks  Workers reply with a set of bids Each bid is a task-price pair  (Allocation) The platform sequentially decide winning bids  (Payment) Winning workers provide labels and get payment

12 12 Crowdsourcing Platform Utility  After observing all crowd labels, the distribution is updated as  Platform Utility: KL Divergence between the initial and the final distribution

13 13 Problem Formulation We want:  Platform utility maximization under budget constraint  Individual rationality  Truthful about the cost Truthful bid Untruthful bid  Computation Efficiency

14 14 Allocation Scheme (1) The task allocation(winning bid determination) is sequential :  Candidate selection one candidate a round  Proportional rule check  Answer collection & Soft label update The allocation scheme repeats the 3 steps until Remaining bids Candidate Discard Winning bid All bids Discard Winning bids

15 15 Allocation Scheme (2)  The candidate selection is greedy The largest platform utility gain per unit price Platform utility gain: PU Gain Price Candidate Updated distribution Current distribution

16 16 Allocation Scheme (3)  Proportional rule check  Soft label update Collect the answer from the winning bid Update the soft label according to Bayes rule price budget fraction ratio

17 17 Allocation Scheme (5) Candidate selection Proportional rule check Soft label update Computationally efficient !

18 18 Payment Scheme(1) p(C) = max {b1,b2, b3, b4} Winning bids {A, B, C} Discard {D, E, F} Kick out C { A,B,D,E,F } Winning bids {A, B, D, E} Discard {F} C b1 b2 b3 b4 b1 is the minimum price that bid C can replace bid A

19 19 Payment Scheme(2) (Proposition)The winning bid C is paid threshold payment.  p(C) C’s payment, b(C) C’s bid  if b(C) < p(C), C is a winning bid if b(C) > p(C), C is discarded p(C)=max { b1, b2, b3, b4} Winning bids {A, B, D, E} C b1 b2 b3 b4

20 20 Payment Scheme(3) (Proposition)The incentive mechanism is truthful  Each bid has a cost  Workers will truthfully reveal the cost as asked price Why? Proof: Threshold payment + Greedy candidate Selection

21 21 Individual Rationality (Proposition)The incentive mechanism is individual rational  The utility of a winning bid is nonnegative Proof : Let us consider the winning bid C 1.C is the 3 rd winning bid. 2.The first 2 bids are the same 3.b3 is the minimum price that bid C can replace the new 3 rd bid (D) It is true that b3 > b(c) ! p(C) = max {b1, b2, b3, b4}, p(C) > b3 p(C) > b(C) New Winning bids {A, B, D, E} b1 b2 b3 b4 { A, B, C} Original wining bids

22 22 Budget Feasibility (Proposition, Payment Bound) Payment to each winning bid is upper bounded by Proportional rule: Set

23 23 Performance Evaluation(1)  Benchmark 1.Untruthful Allocation: Workers’ cost is public information 2.Random Allocation: Candidate selection is random TruthfulRunning Time Platform Utility Benchmark 1  High Benchmark 2 Low My Mechanism High

24 24 Performance Evaluation(2)  Metric 1 : Platform Utility Platform utility vs. Budget Price of Truthfulness Gain over random allocation

25 25 Performance Evaluation(3)  Metric 2 : Budget Utilization Payment / Budget Budget utilization gain Over random allocation

26 Thank you ! Presented by : Qi Zhang


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