Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning the maximum grade to all submissions. With a naive grading scheme, such as averaging the assigned grades, all students would receive the maximum grade. In this paper, we develop three grading schemes that provide incentives for truthful peer grading. In the first scheme, the instructor grades a fraction p of the submissions, and penalizes students whose grade deviates from the instructor grade. We provide lower bounds on p to ensure truthfulness, and conclude that these schemes work only for moderate class sizes, up to a few hundred students. To overcome this limitation, we propose a hierarchical extension of this supervised scheme, and we show that it can handle classes of any size with bounded (and little) instructor work, and is therefore applicable to Massive Open Online Courses (MOOCs). Finally, we propose unsupervised incentive schemes, in which the student incentive is based on statistical properties of the grade distribution, without any grading required by the instructor. We show that the proposed unsupervised schemes provide incentives to truthful grading, at the price of being possibly unfair to individual students.
The peer-prediction method @cite_21 uses proper scoring rules to reward experts depending on how good their input for predicting other experts' reports. Similarly, the model described in @cite_12 evaluates experts depending on how good their reports are in predicting the consensus of other workers.
@cite_21 @cite_12
2017669196 2109062107
When eliciting opinions from a group of experts, traditional devices used to promote honest reporting assume that there is an observable future outcome. In practice, however, this assumption is not always reasonable. In this paper, we propose a scoring method built on strictly proper scoring rules to induce honest reporting without assuming observable outcomes. Our method provides scores based on pairwise comparisons between the reports made by each pair of experts in the group. For ease of exposition, we introduce our scoring method by illustrating its application to the peer-review process. In order to do so, we start by modeling the peer-review process using a Bayesian model where the uncertainty regarding the quality of the manuscript is taken into account. Thereafter, we introduce our scoring method to evaluate the reported reviews. Under the assumptions that reviewers are Bayesian decision-makers and that they cannot influence the reviews of other reviewers, we show that risk-neutral reviewers strictly maximize their expected scores by honestly disclosing their reviews. We also show how the group's scores can be used to find a consensual review. Experimental results show that encouraging honest reporting through the proposed scoring method creates more accurate reviews than the traditional peer-review process. When a decision maker (DM) contracts with an expert to provide information, the nature of the contract can, create incentives for the expert, and it is up to the DM to ensure that the contract provides incentives that align the expert’s and DM’s interests. In this paper, scoring rules (and related functions) are viewed as such contracts and are reinterpreted in terms of agency theory and the theory of revelation games from economics. Although scoring rules have typically been discussed in the literature as devices for eliciting and evaluating subjective probabilities, this study relies on the fact that strictly proper scoring rules reward greater expertise as well as honest revelation. We describe conditions under which a DM can use a strictly proper scoring rule as a contract to give an expert an incentive to gather an amount of information that is optimal from the DM’s perspective. The conditions we consider focus on the expert’s cost structure, and we find that the DM must have substantial knowledge of that cost structure in order to design a specific contract that provides the correct incentives. The model and analysis suggest arguments for hiring and maintaining experts in-house rather than using outside consultants. Abstract Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensin... We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss function is applied to measure the discrepancy between the predictions and actual observations. The algorithm keeps a weight for each expert. At each trial the weights are first used to help produce the prediction and then updated according to the observed outcome. Our starting point is Vovk's Aggregating Algorithm, in which the weights have a simple form: the weight of an expert decreases exponentially as a function of the loss incurred by the expert. The prediction of the Aggregating Algorithm is typically a non-linear function of the weights and the experts' predictions. We analyze here a simplified algorithm in which the weights are as in the original Aggregating Algorithm, but the prediction is simply the weighted average of the experts' predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most c ln n, where n is the number of experts and c a constant that depends on the loss function. Thus, the bound is of the same form as the known bounds for the Aggregating Algorithm, although the constants here are not quite as good. We use relative entropy to rewrite the bounds in a stronger form and to motivate the update. A financial expert system for predicting the daily stock movements.Knowledge base captures both traditional and online data sources.The inference engine uses three artificial intelligence techniques.Prediction accuracy of 85 is higher than the reported results in the literature.The system is hosted online and freely available for investors and researchers. There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of crowd-sourced knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the publics collective interest. Google and Wikipedia have become important knowledge bases for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our inference engine. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85 accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index. In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal-Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal one, we obtain a bound that does not contain the nominal number of experts at all. We use the defensive forecasting method and introduce an application of defensive forecasting to multi-valued supermartingales. In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model, and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate the effectiveness of our method compared to a variety of other regression models. Peer-prediction is a (meta-)mechanism which, given any proper scoring rule, produces a mechanism to elicit privately-held, non-verifiable information from self-interested agents. Formally, truth-telling is a strict Nash equilibrium of the mechanism. Unfortunately, there may be other equilibria as well (including uninformative equilibria where all players simply report the same fixed signal, regardless of their true signal) and, typically, the truth-telling equilibrium does not have the highest expected payoff. The main result of this paper is to show that, in the symmetric binary setting, by tweaking peer-prediction, in part by carefully selecting the proper scoring rule it is based on, we can make the truth-telling equilibrium focal---that is, truth-telling has higher expected payoff than any other equilibrium. Along the way, we prove the following: in the setting where agents receive binary signals we 1) classify all equilibria of the peer-prediction mechanism; 2) introduce a new technical tool for understanding scoring rules, which allows us to make truth-telling pay better than any other informative equilibrium; 3) leverage this tool to provide an optimal version of the previous result; that is, we optimize the gap between the expected payoff of truth-telling and other informative equilibria; and 4) show that with a slight modification to the peer prediction framework, we can, in general, make the truth-telling equilibrium focal---that is, truth-telling pays more than any other equilibrium (including the uninformative equilibria). A large number of very popular team sports involve the act of one team trying to score a goal against the other. During this game play, defending players constantly try to predict the next move of the attackers to prevent them from scoring, whereas attackers constantly try to predict the next move of the defenders in order to defy them and score. Such behavior is a prime example of the general human faculty to make predictions about the future and is an important facet of human intelligence. An algorithmic solution to learning a model of the external world from sensory inputs in order to make forecasts is an important unsolved problem. In this work we develop a generic framework for forecasting future events in team sports videos directly from visual inputs. We introduce water polo and basketball datasets towards this end and compare the predictions of the proposed methods against expert and non-expert humans. 1. Introduction 2. Prediction with expert advice 3. Tight bounds for specific losses 4. Randomized prediction 5. Efficient forecasters for large classes of experts 6. Prediction with limited feedback 7. Prediction and playing games 8. Absolute loss 9. Logarithmic loss 10. Sequential investment 11. Linear pattern recognition 12. Linear classification 13. Appendix. Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that of the best expert in hindsight (in other words, whose average regret approaches zero). Traditionally the regret of online algorithms was bounded in terms of the number of prediction rounds. Cesa-Bianchi, Mansour and Stoltz (Mach. Learn. 66(2---3):21---352, 2007) posed the question whether it is be possible to bound the regret of an online algorithm by the variation of the observed costs. In this paper we resolve this question, and prove such bounds in the fully adversarial setting, in two important online learning scenarios: prediction from expert advice, and online linear optimization. Crowdsourcing is widely proposed as a method to solve a large variety of judgment tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called peer consistency. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh [2013] show that, in some cases, exerting high effort can be encouraged in the highest-paying equilibrium. In this article, we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties. We study two problems of online learning under restricted information access. In the first problem, prediction with limited advice, we consider a game of prediction with expert advice, where on each round of the game we query the advice of a subset of M out of N experts. We present an algorithm that achieves O(√N M T ln N) regret on T rounds of this game. The second problem, the multiarmed bandit with paid observations, is a variant of the adversarial N-armed bandit game, where on round t of the game we can observe the reward of any number of arms, but each observation has a cost c. We present an algorithm that achieves O((cN lnN)1 3 T2 3 + √T lnN) regret on T rounds of this game in the worst case. Furthermore, we present a number of refinements that treat arm- and time-dependent observation costs and achieve lower regret under benign conditions. We present lower bounds that show that, apart from the logarithmic factors, the worst-case regret bounds cannot be improved. We consider online prediction with expert advice. Over the course of many trials, the goal of the learning algorithm is to achieve small additional loss (i.e. regret) compared to the loss of the best from a set of K experts. The two most popular algorithms are Hedge Weighted Majority and Follow the Perturbed Leader (FPL). The latter algorithm first perturbs the loss of each expert by independent additive noise drawn from a fixed distribution, and then predicts with the expert of minimum perturbed loss (“the leader”) where ties are broken uniformly at random. To achieve the optimal worst-case regret as a function of the lossL of the best expert in hindsight, the two types of algorithms need to tune their learning rate or noise magnitude, respectively, as a function ofL . Instead of perturbing the losses of the experts with additive noise, we randomly set them to 0 or 1 before selecting the leader. We show that our perturbations are an instance of dropout — because experts may be interpreted as features — although for non-binary losses the dropout probability needs to be made dependent on the losses to get good regret bounds. We show that this simple, tuning-free version of the FPL algorithm achieves two feats: optimal worst-case O( p L lnK + lnK) regret as a function ofL , and optimalO(lnK) regret when the loss vectors are drawn i.i.d. from a fixed distribution and there is a gap between the expected loss of the best expert and all others. A number of recent algorithms from the Hedge family (AdaHedge and FlipFlop) also achieve this, but they employ sophisticated tuning regimes. The dropout perturbation of the losses of the experts result in different noise distributions for each expert (because they depend on the expert’s total loss) and curiously enough no additional tuning is needed: the choice of dropout probability only affects the constants. In this paper, I introduce a class of mechanisms for eliciting private correlated signals from a group of expected score maximizers without external verification or knowledge about the agents’ belief structure. Built on proper scoring rules, these minimum truth serums ask agents to report a signal and a prediction of the signals of others. If two agents with the same signal have the same expectations about the signals of others, the Bayesian incentive compatibility of these mechanisms follows with no further assumptions on the agents’ belief structure. With a slight modification, the mechanism is still feasible and incentive compatible when the prediction portion of the report is optional. Formally published papers that have been through a traditional prepublication peer review process remain the most important means of communicating science today. Researchers depend on them to learn about the latest advances in their fields and to report their own findings. The intentions of traditional peer review are certainly noble: to ensure methodological integrity and to comment on potential significance of experimental studies through examination by a panel of objective, expert colleagues. In principle, this system enables science to move forward on the collective confidence of previously published work. Unfortunately, the traditional system has inspired methods of measuring impact that are suboptimal for their intended uses. Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and diversity of modern hardware and software. Machine learning is aproven technique for learning such heuristics, but its success is bound by thequality of the features used. These features must be hand crafted by developersthrough a combination of expert domain knowledge and trial and error. This makesthe quality of the final model directly dependent on the skill and availabletime of the system architect.Our work introduces a better way for building heuristics. We develop a deepneural network that learns heuristics over raw code, entirely without using codefeatures. The neural network simultaneously constructs appropriaterepresentations of the code and learns how best to optimize, removing the needfor manual feature creation. Further, we show that our neural nets can transferlearning from one optimization problem to another, improving the accuracy of newmodels, without the help of human experts.We compare the effectiveness of our automatically generated heuristics againstones with features hand-picked by experts. We examine two challenging tasks:predicting optimal mapping for heterogeneous parallelism and GPU threadcoarsening factors. In 89 of the cases, the quality of our fully automaticheuristics matches or surpasses that of state-of-the-art predictive models usinghand-crafted features, providing on average 14 and 12 more performance withno human effort expended on designing features. In an expert search task, the users’ need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users’ query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques. We generalize the recent relative loss bounds for on-line algorithms where the additional loss of the algorithm on the whole sequence of examples over the loss of the best expert is bounded. The generalization allows the sequence to be partitioned into segments, and the goal is to bound the additional loss of the algorithm over the sum of the losses of the best experts for each segment. This is to model situations in which the examples change and different experts are best for certain segments of the sequence of examples. In the single segment case, the additional loss is proportional to log n, where n is the number of experts and the constant of proportionality depends on the loss function. Our algorithms do not produce the best partition; however the loss bound shows that our predictions are close to those of the best partition. When the number of segments is k+1 and the sequence is of length e, we can bound the additional loss of our algorithm over the best partition by O(k n+k (e k)). For the case when the loss per trial is bounded by one, we obtain an algorithm whose additional loss over the loss of the best partition is independent of the length of the sequence. The additional loss becomes O(k n+ k (L k)), where L is the loss of the best partitionwith k+1 segments. Our algorithms for tracking the predictions of the best expert aresimple adaptations of Vovk's original algorithm for the single best expert case. As in the original algorithms, we keep one weight per expert, and spend O(1) time per weight in each trial. Evaluating information accuracy in social media is an increasingly important and well-studied area, but limited research has compared journalist-sourced accuracy assessments to their crowdsourced counterparts. This paper demonstrates the differences between these two populations by comparing the features used to predict accuracy assessments in two Twitter data sets: CREDBANK and PHEME. While our findings are consistent with existing results on feature importance, we develop models that outperform past research. We also show limited overlap exists between the features used by journalists and crowdsourced assessors, and the resulting models poorly predict each other but produce statistically correlated results. This correlation suggests crowdsourced workers are assessing a different aspect of these stories than their journalist counterparts, but these two aspects are linked in a significant way. These differences may be explained by contrasting factual with perceived accuracy as assessed by expert journalists and non-experts respectively. Following this outcome, we also show preliminary results that models trained from crowdsourced workers outperform journalist-trained models in identifying highly shared "fake news" stories.