34 research outputs found
Fair Grading Algorithms for Randomized Exams
This paper studies grading algorithms for randomized exams. In a randomized exam, each student is asked a small number of random questions from a large question bank. The predominant grading rule is simple averaging, i.e., calculating grades by averaging scores on the questions each student is asked, which is fair ex-ante, over the randomized questions, but not fair ex-post, on the realized questions. The fair grading problem is to estimate the average grade of each student on the full question bank. The maximum-likelihood estimator for the Bradley-Terry-Luce model on the bipartite student-question graph is shown to be consistent with high probability when the number of questions asked to each student is at least the cubed-logarithm of the number of students. In an empirical study on exam data and in simulations, our algorithm based on the maximum-likelihood estimator significantly outperforms simple averaging in prediction accuracy and ex-post fairness even with a small class and exam size
Fair Grading Algorithms for Randomized Exams
This paper studies grading algorithms for randomized exams. In a randomized
exam, each student is asked a small number of random questions from a large
question bank. The predominant grading rule is simple averaging, i.e.,
calculating grades by averaging scores on the questions each student is asked,
which is fair ex-ante, over the randomized questions, but not fair ex-post, on
the realized questions. The fair grading problem is to estimate the average
grade of each student on the full question bank. The maximum-likelihood
estimator for the Bradley-Terry-Luce model on the bipartite student-question
graph is shown to be consistent with high probability when the number of
questions asked to each student is at least the cubed-logarithm of the number
of students. In an empirical study on exam data and in simulations, our
algorithm based on the maximum-likelihood estimator significantly outperforms
simple averaging in prediction accuracy and ex-post fairness even with a small
class and exam size
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an optimal subset of workers so that the outcome obtained from the
selected workers guarantees a target accuracy level. The problem is a
challenging one even in a non strategic setting since the accuracy of
aggregated label depends on unknown qualities. We develop a novel multi-armed
bandit (MAB) mechanism for solving this problem. First, we propose a framework,
Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained
Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound
on the number of time steps the algorithm chooses a sub-optimal set that
depends on the target accuracy level and true qualities. A more challenging
situation arises when the requester not only has to learn the qualities of the
workers but also elicit their true costs. We modify the CCB-NS algorithm to
obtain an adaptive exploration separated algorithm which we call { \em
Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm
produces an ex-post monotone allocation rule and thus can be transformed into
an ex-post incentive compatible and ex-post individually rational mechanism
that learns the qualities of the workers and guarantees a given target accuracy
level in a cost optimal way. We provide a lower bound on the number of times
any algorithm should select a sub-optimal set and we see that the lower bound
matches our upper bound upto a constant factor. We provide insights on the
practical implementation of this framework through an illustrative example and
we show the efficacy of our algorithms through simulations
Approximate inference techniques with expectation constraints
Contains fulltext :
100937.pdf (preprint version ) (Open Access
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
When estimating a regression model, we might have data where some labels are
missing, or our data might be biased by a selection mechanism. When the
response or selection mechanism is ignorable (i.e., independent of the response
variable given the features) one can use off-the-shelf regression methods; in
the nonignorable case one typically has to adjust for bias. We observe that
privileged data (i.e. data that is only available during training) might render
a nonignorable selection mechanism ignorable, and we refer to this scenario as
Privilegedly Missing at Random (PMAR). We propose a novel imputation-based
regression method, named repeated regression, that is suitable for PMAR. We
also consider an importance weighted regression method, and a doubly robust
combination of the two. The proposed methods are easy to implement with most
popular out-of-the-box regression algorithms. We empirically assess the
performance of the proposed methods with extensive simulated experiments and on
a synthetically augmented real-world dataset. We conclude that repeated
regression can appropriately correct for bias, and can have considerable
advantage over weighted regression, especially when extrapolating to regions of
the feature space where response is never observed
A Strict Ex-post Incentive Compatible Mechanism for Interdependent Valuations
Abstract The impossibility result by Jehiel and Moldovanu says that in a setting with interdependent valuations, any efficient and ex-post incentive compatible mechanism must be a constant mechanism. Mezzetti circumvents this problem by designing a two stage mechanism where the decision of allocation and payment are split over the two stages. This mechanism is elegant, however keeps a major weakness. In the second stage, agents are weakly indifferent about reporting their valuations truthfully: an agent's payment is independent of her reported valuation and truthtelling for this stage is by assumption. We propose a strict improvement to this mechanism which makes the second stage strictly truthful retaining the other good properties
Split Variational Inference
We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of the bins and the approximation of the local integrals. We introduce suitable choices for the binning functions such that a standard mean field approximation can be extended to a split mean field approximation without the need for extra derivations. The method can be seen as a revival of the ideas underlying the mixture mean field approach. The latter can be obtained as a special case by taking soft-max functions for the binning. 1