2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력

Journal of the American Statistical Association: William S. Cleveland, Marylyn E. McGill and Robert McGill, “The shape parameter for a two variable graph” 83 (1988) 289–300

여성사 쓰기의 가능성과 현주소, 한국여성연구소 여성사연구실 지음, 청년사 1999

The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.

The Frontiers of Fairness in Machine Learning

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

Fairness in Machine Learning: A Survey.

We study fairness within the stochastic, multi-armed bandit (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" [5] to this seing. Here, an 'individual' corresponds to an arm and two arms are 'similar' if they have a similar quality distribution. First, we adopt a smoothness constraint that if two arms have a similar quality distribution then the probability of selecting each arm should be similar. In addition, we dene the fairness regret, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization. We show that a variation on ompson sampling satises smooth fairness for total variation distance, and give an O((kT) 2/3) bound on fairness regret. is complements prior work [12], which protects an on-average beer arm from being less favored. We also explain how to extend our algorithm to the dueling bandit seing. ACM Reference format:

Calibrated Fairness in Bandits

Peer prediction mechanisms incentivize agents to truthfully report their signals, in the absence of a verification mechanism, by comparing their reports with those of their peers. Prior work in this area is essentially restricted to the case of homogeneous agents, whose signal distributions are identical. This is limiting in many domains, where we would expect agents to differ in taste, judgment, and reliability. Although the Correlated Agreement (CA) mechanism [Shnayder et al. 2016a] can be extended to handle heterogeneous agents, there is a new challenge of efficiently estimating agent signal types. We solve this problem by clustering agents based on their reporting behavior, proposing a mechanism that works with clusters of agents, and designing algorithms that learn such a clustering. In this way, we also connect peer prediction with the Dawid and Skene [1979] literature on latent types. We retain the robustness against coordinated misreports of the CA mechanism, achieving an approximate incentive guarantee of e-informed truthfulness. We show on real data that this incentive approximation is reasonable in practice, even with a small number of clusters.

https://dl.acm.org/doi/pdf/10.1145/3381519

Peer Prediction with Heterogeneous Users

We take an unorthodox view of voting by expanding the design space to include both the elicitation rule, whereby voters map their (cardinal) preferences to votes, and the aggregation rule, which transforms the reported votes into collective decisions. Intuitively, there is a tradeoff between the communication requirements of the elicitation rule (i.e., the number of bits of information that voters need to provide about their preferences) and the efficiency of the outcome of the aggregation rule, which we measure through distortion (i.e., how well the utilitarian social welfare of the outcome approximates the maximum social welfare in the worst case). Our results chart the Pareto frontier of the communication-distortion tradeoff.

/pdf/efficient-and-thrifty-voting-by-any-means-necessary-10t75vevse.pdf

Efficient and Thrifty Voting by Any Means Necessary

Motivated by allocation and pricing problems faced by service requesters on modern crowdsourcing platforms, we study a multi-armed bandit (MAB) problem with several real-world features: (a) the requester wishes to crowdsource a number of tasks but has a fixed budget which leads to a trade-off between cost and quality while allocating tasks to workers; (b) each task has a fixed deadline and a worker who is allocated a task is not available until this deadline; (c) the qualities (probability of completing a task successfully within deadline) of crowd workers are not known; and (d) the crowd workers are strategic about their costs. We propose a mechanism that maximizes the expected number of successfully completed tasks, assuring budget feasibility, incentive compatibility, and individual rationality. We establish an upper bound of O(B2/3(K ln(KB))1/3) on the expected regret of the proposed mechanism with respect to an appropriate benchmark algorithm, where B is the total budget and K is the number of workers. Next, we provide a characterization of any deterministic truthful mechanism that solves the above class of problems and use this characterization to establish a lower bound of Ω(B2=3K1=3) on the expected regret for any budgeted MAB mechanism satisfying the above properties.

A Truthful Budget Feasible Multi-Armed Bandit Mechanism for Crowdsourcing Time Critical Tasks

In recent work, Mandal et al. [2019] study a novel framework for the winner selection problem in voting, in which a voting rule is seen as a combination of an elicitation rule and an aggregation rule. The elicitation rule asks voters to respond to a query based on their preferences over a set of alternatives, and the aggregation rule aggregates voter responses to return a winning alternative. They study the tradeoff between the communication complexity of a voting rule, which measures the number of bits of information each voter must send in response to its query, and its distortion, which measures the quality of the winning alternative in terms of utilitarian social welfare. They prove upper and lower bounds on the communication complexity required to achieve a desired level of distortion, but their bounds are not tight. Importantly, they also leave open the question whether the best randomized rule can significantly outperform the best deterministic rule. We settle this question in the affirmative. For a winner selection rule to achieve distortion d with m alternatives, we show that the communication complexity required is ~Θ (m/d) when using deterministic elicitation, and ~Θ (m/d3) when using randomized elicitation; both bounds are tight up to logarithmic factors. Our upper bound leverages recent advances in streaming algorithms. To establish our lower bound, we derive a new lower bound on a multi-party communication complexity problem. We then study the k-selection problem in voting, where the goal is to select a set of k alternatives. For a k-selection rule that achieves distortion d with m alternatives, we show that the best communication complexity is ~Θ (m/(kd)) when the rule uses deterministic elicitation and ~Θ (m/(kd3)) when the rule uses randomized elicitation. Our optimal bounds yield the non-trivial implication that the k-selection problem becomes strictly easier as k increases.

Debmalya Mandal

Papers

Calibrated Fairness in Bandits

Peer Prediction with Heterogeneous Users

Efficient and Thrifty Voting by Any Means Necessary

A Truthful Budget Feasible Multi-Armed Bandit Mechanism for Crowdsourcing Time Critical Tasks

Optimal Communication-Distortion Tradeoff in Voting