Crowdsourcing with Sparsely Interacting Workers

We consider estimation of worker skills from worker-task interaction data (with unknown labels) for the single-coin crowd-sourcing binary classification model in symmetric noise. We define the (worker) interaction graph whose nodes are workers and an edge between two nodes indicates whether or not the two workers participated in a common task. We show that skills are asymptotically identifiable if and only if an appropriate limiting version of the interaction graph is irreducible and has odd-cycles. We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph. We propose a gradient descent scheme and show that for such interaction graphs estimates converge asymptotically to the global minimum. We characterize noise robustness of the gradient scheme in terms of spectral properties of signless Laplacians of the interaction graph. We then demonstrate that a plug-in estimator based on the estimated skills achieves state-of-art performance on a number of real-world datasets. Our results have implications for rank-one matrix completion problem in that gradient descent can provably recover $W \times W$ rank-one matrices based on $W+1$ off-diagonal observations of a connected graph with a single odd-cycle

Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers

We consider worker skill estimation for the singlecoin Dawid-Skene crowdsourcing model. In practice skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary, and uncontrolled availability of workers. We formulate skill estimation as a rank-one correlation-matrix completion problem, where the observed components correspond to observed label correlation between workers. We show that the correlation matrix can be successfully recovered and skills identifiable if and only if the sampling matrix (observed components) is irreducible and aperiodic. We then propose an efficient gradient descent scheme and show that skill estimates converges to the desired global optima for such sampling matrices. Our proof is original and the results are surprising in light of the fact that even the weighted rank-one matrix factorization problem is NP hard in general. Next we derive sample complexity bounds for the noisy case in terms of spectral properties of the signless Laplacian of the sampling matrix. Our proposed scheme achieves state-of-art performance on a number of real-world datasets.Published versio

Mouse Behavior Recognition with The Wisdom of Crowd

In this thesis, we designed and implemented a crowdsourcing system to annotatemouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system

A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks

Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing applications where each worker can exhibit two levels of accuracy depending on a task's type. Applying algorithms designed for the traditional Dawid-Skene model to such a scenario results in performance which is limited by the hard tasks. Therefore, we first extend the model to allow worker accuracy to vary depending on a task's unknown type. Then we propose a spectral method to partition tasks by type. After separating tasks by type, any Dawid-Skene algorithm (i.e., any algorithm designed for the Dawid-Skene model) can be applied independently to each type to infer the truth values. We theoretically prove that when crowdsourced data contain tasks with varying levels of difficulty, our algorithm infers the true labels with higher accuracy than any Dawid-Skene algorithm. Experiments show that our method is effective in practical applications

파라미터 학습 통한 데이터 잡음 및 간섭극복 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 정교민.인공신경망 모델에 다량의 데이터를 학습시키는 방식은 컴퓨터 비전 및 자연어 처리 분야의 문제들을 해결하는데 새로운 패러다임으로 자리매김하였다. 기존 사람의 직관으로 모델을 설정하는 방식과 비교하여 높은 성능을 달성할 수 있었으나, 학습데이터의 양과 품질에 따라서 그 성능이 크게 좌우된다. 이렇게 인공 신경망을 효과적으로 훈련하려면 많은 양의 데이터를 모으는 것과 데이터의 품질을 저하시키는 요인을 파악하는 것이 중요하다. 본 연구에서는 라벨링된 데이터의 품질을 결정하는 주요 요인으로 알려져 있는 잡음(Noise)과 간섭(Interference)을 극복할 수 있는 기법을 제시한다. 연구자들은 일반적으로 웹기반의 크라우드 소싱시스템을 사용하여 다양한 사람들로부터 답변을 수집하여 데이터그룹을 구성한다\cite{simonyan2014very}. 그러나 사람들의 답변으로 얻는 데이터는 작업 지침에 대한 오해, 책임 부족 및 고유한 오류로 인해서 데이터 입력(Input)과 출력(Target)사이에 잡음이 포함된다. 본 연구에서는 이렇게 크라우드 소싱을 통해 라벨링된 데이터에 존재하는 잡음을 극복하기 위한 추론 알고리즘을 제안한다. 두번째로, 모델의 학습성능을 저하시키는 요인인 데이터간의 간섭을 다룬다. 잡음이 제거되어 정제된 입력과 출력을 라벨링된 데이터 샘플이라고 하면, 학습시에 샘플들 사이의 관계를 생각할 수 있다. 사람 수준의 인공지능에 도달하기 위해서는 하나의 모델이 하나의 문제만을 해결하는 것이 아니라 시간상 순차적으로 직면하는 여러 문제를 동시에 해결할 수 있어야 한다. 이러한 상황에서, 샘플들 사이에 간섭이 발생할 수 있고, 학계에서는 연속학습(Continual Learning)에서의 "Catastrophic Forgetting"또는 "Semantic Drift"으로 정의하고 있다. 본 연구에서는 이러한 간섭을 효과적으로 극복하기 위한 방법에 대한 연구를 다룬다. 앞서 언급한 데이터 잡음을 극복하기 위해서 첫 번째 장에서는 크라우드 소싱 시스템의 이산 객관식 및 실수 벡터 회귀 작업에 대한 새로운 추론 알고리즘을 각각 제안한다. 제안 된 알고리즘은 크라우드 소싱 모델을 그래프 모델(Graphical Model)로서 상정하고, 테스크와 답변을 주는 사람들간의 두 가지 유형의 메시지를 반복적으로 주고 받음으로써 각 작업의 정답과 각 작업자의 신뢰성을 추정 할 수 있다. 또한 이들의 평균 성능은 확률적 군중 모델을 이용하여 분석하고 입증한다. 이러한 성능에러 한계는 작업당 할당되는 사람들의 수와 작업자의 평균 신뢰성의해 결정된다. 사람들의 평균 신뢰도가 일정 수준을 넘어서면, 제안된 알고리즘의 평균 성능은 모든 작업자의 신뢰성을 알고있는 오라클 추정기 (이론적인 한계)에 수렴한다. 실제 데이터 세트와 합성 데이터 세트 모두에 대한 광범위한 실험을 통해, 제안된 알고리즘의 실제 성능이 이전의 state-of-the-art 알고리즘들 보다 우수하다는 것을 입증한다. 논문의 두 번째 장에서는 연속학습상황에서 데이터샘플사이에 발생하는 간섭을 해결하기 위해, 항상성기반의 메타 학습 구조 (Homeostatic Meta Model)를 제안한다. 구체적으로, 이전 테스크 중요한 학습 변수를 찾고 정규화에 선별적으로 적용하는 방법을 사용하는데, 제안된 모델은 이러한 정규화의 강도를 자동으로 제어한다. 이러한 기법은 새로운 학습을 진행할 때 이전에 획득한 지식을 최소한으로 잃어버리도록 인공신경망의 학습을 유도한다. 다양한 유형의 연속 학습 과제에서 제안된 방법을 검증하는데, 실험적으로 제안된 방법이 학습의 간섭완화 측면에서 기존 방법보다 우수하다는 점을 보인다.또한 기존 시냅스 가소성 기반 방법들에 비해 상대적으로 변화에 강인하다.제안된 모델에 의해 생성된 정규화의 강도 값은 시냅스에서 항상성 의 음의 피드백 메커니즘과 유사하게, 특정 범위 내에서 능동적으로 제어된다.Data-driven approaches based on neural networks have emerged as new paradigm to solve problems in computer vision and natural language processing fields. These approaches achieve better performance compared to existing human-design approaches (heuristic), however, these performance gains solely relies on a large amount of high quality labeled data. Accordingly, it is important to collect a large amount of data and improve the quality of data by analyzing degrading factors in order to well-train a model. In this dissertation, I propose iterative algorithms to relieve noise of labeled data in crowdsourcing system and meta architecture to alleviate interference among them in continual learning scenarios respectively. Researchers generally collect data using crowdsourcing system which utilizes human evaluations. However, human annotators' decisions may vary significantly due to misconceptions of task instructions, the lack of responsibility, and inherent noise. To relieve the noise in responses from crowd annotators, I propose novel inference algorithms for discrete multiple choice and real-valued vector regression tasks. Web-based crowdsourcing platforms are widely used for collecting large amount of labeled data. Due to low-paid workers and inherent noise, the quality of acquired data could be easily degraded. The proposed algorithms can overcome the noise by estimating the true answer of each task and a reliability of each worker updating two types of messages iteratively. For performance guarantee, the performances of the algorithms are theoretically proved under probabilistic crowd model. Interestingly, their performance bounds depend on the number of queries per task and the average quality of workers. Under a certain condition, each average performance becomes close to an oracle estimator which knows the reliability of every worker (theoretical upper bound). Through extensive experiments with both real-world and synthetic datasets, the practical performance of algorithms are verified. In fact, they are superior to other state-of-the-art algorithms. Second, when a model learns a sequence of tasks one by one (continual learning), previously learned knowledge may conflict with new knowledge. It is well-known phenomenon called "Catastrophic Forgetting" or "Semantic Drift". In this dissertation, we call the phenomena "Interference" since it occurs between two knowledge from labeled data separated in time. It is essential to control the amount of noise and interference for neural network to be well-trained. In the second part of dissertation, to solve the Interference among labeled data from consecutive tasks in continual learning scenario, a homeostasis-inspired meta learning architecture (HM) is proposed. The HM automatically controls the intensity of regularization (IoR) by capturing important parameters from the previous tasks and the current learning direction. By adjusting IoR, a learner can balance the amount of interference and degrees of freedom for its current learning. Experimental results are provided on various types of continual learning tasks. Those results show that the proposed method notably outperforms the conventional methods in terms of average accuracy and amount of the interference. In experiments, I verify that HM is relatively stable and robust compared to the existing Synaptic Plasticity based methods. Interestingly, the IoR generated by HM appears to be proactively controlled within a certain range, which resembles a negative feedback mechanism of homeostasis in synapses.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1 2 Reliable multiple-choice iterative algorithm for crowdsourcing systems 6 2.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Task Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Multiple Iterative Algorithm . . . . . . . . . . . . . . . . . . 8 2.2.3 Task Allocation for General Setting . . . . . . . . . . . . . . 10 2.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 Analysis of algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.1 Quality of workers . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.2 Bound on the Average Error Probability . . . . . . . . . . . . 18 2.4.3 Proof of the Theorem 1 . . . . . . . . . . . . . . . . . . . . . 20 2.4.4 Proof of Sub-Gaussianity . . . . . . . . . . . . . . . . . . . . 22 2.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 iii2.6 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3 Reliable Aggregation Method for Vector Regression in Crowdsourcing 38 3.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Inference Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.1 Task Message . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2.2 Worker Message . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3.1 Real crowdsourcing data . . . . . . . . . . . . . . . . . . . . 43 3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Dirichlet crowd model . . . . . . . . . . . . . . . . . . . . . 48 3.4.2 Error Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.4.3 Optimality of Oracle Estimator . . . . . . . . . . . . . . . . . 51 3.4.4 Performance Proofs . . . . . . . . . . . . . . . . . . . . . . . 52 3.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Homeostasis-Inspired Meta Continual Learning 60 4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.1 Continual Learning . . . . . . . . . . . . . . . . . . . . . . . 60 4.1.2 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2 Homeostatic Meta-Model . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Preliminary Experiments and Findings . . . . . . . . . . . . . . . . . 66 4.3.1 Block-wise Permutation . . . . . . . . . . . . . . . . . . . . 67 4.3.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . 68 4.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.3 Overall Performance . . . . . . . . . . . . . . . . . . . . . . 70 4.5 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 iv4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5 Conclusion 78 Abstract (In Korean) 89Docto