This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surpri…
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48 results for “Ranking Aggregation”
The paper addresses privacy in rank aggregation using randomized responses.
problem Preserving privacy while aggregating pairwise rankings.
method Adaptive debiasing method for randomized response rankings.
result Established minimax rates for estimation errors and optimal privacy guarantees.
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground tr…
This paper compares rank aggregation methods for partial label ranking.
problem Handling partial label ranking with ties.
method Scoring-based and non-parametric probabilistic-based rank aggregation methods.
result Scoring-based variants consistently outperform the state-of-the-art method.
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
problem Combining multiple binary labels for optimal bipartite ranking.
method Loss aggregation vs. label aggregation approaches.
result Label aggregation is preferable to loss aggregation due to label dictatorship issues.
We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's ρto extreme ranks. Our main …
We analyze incomplete ranking data, modeling coarsening and studying rank aggregation methods.
problem Statistical inference for incomplete ranking data, especially under rank-dependent coarsening.
method Modeling rank-dependent coarsening, studying Plackett-Luce distribution, and analyzing rank aggregation methods.
result The ability to recover a target ranking from incomplete observations, despite coarsening bias, is theoretically addressed.
A method for learning rankings in non-stationary data streams.
problem Learning preferences in a population that changes over time.
method Generalized Borda algorithm for non-stationary ranking streams.
result Bounds on the minimum number of samples required to output the ground truth.
Proposes HTM for aggregating ranked data considering user accuracy.
problem Aggregating ranked data from heterogeneous users with varying accuracy levels.
method Heterogeneous Thurstone Model (HTM) with alternating gradient descent algorithm.
result Algorithm converges linearly and outperforms existing methods.
We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall τ and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on…
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show ho…
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes, 2012) in several ways. We show that they represent a distortion between a 'score' and an 'ordering', thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show h…
Unified approach to aggregating models and preferences.
problem Consistent aggregation of models and preferences.
method Formal definition and weighted averaging of models and preferences.
result All rational aggregation rules are weighted averages of highest-ranked models/experts.
A new method for forming learning objectives using the sum of ranked range.
problem Forming learning objectives from aggregated values.
method Sum of ranked range (SoRR) minimization with DCA.
result The proposed method effectively forms learning objectives and is applicable to binary and multi-label/multi-class classification.
Improved rank aggregation via spectral method reduces sample complexity.
problem Ranking items from pairwise comparisons with corrupted data.
method Spectral ranking algorithms based on unnormalized and normalized data matrices.
result Sharper -norm perturbation bound and error bound on maximum displacement for each item.
The paper tackles targeted attacks on rank aggregation methods, proving the fixed point of adversarial game.
problem The security issue of rank aggregation methods, especially the vulnerability to targeted attacks.
method Formulated as a game-theoretic framework, the attack behavior is a fixed point of the composition of the adversary and the victim.
result The victims will produce the target ranking list once the adversary has complete information.
AtC combines human judgments and model scores for better human-centered assessments.
problem Lack of verifiable ground truth in human-centered assessments.
method Two-stage framework: aggregate judgments, then calibrate model scores.
result AtC outperforms human-only or model-only assessments across datasets.
Paper develops a method to approximate Markov chains with fewer states.
problem Identifying the state aggregation structure of Markov chains with fewer states.
method Proposes a convex optimization problem with a nonnegative factorization approach.
result The method likely converges to the global solution and outperforms existing methods.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
This paper solves aggregation of Pareto optimal models by using Bayesian priors and weighted averaging.
problem How to rationally aggregate Pareto optimal models while preserving Pareto efficiency.
method Four logical steps: 1) Bayesian models, 2) Prior as preference ranking, 3) Consistent aggregation, 4) Weighted average of priors.
result All rational/consistent aggregation rules follow a generalized hierarchical Bayesian model.
A novel MQCAL method selects high-value samples via weighted rank aggregation.
problem Lack of scalable and general integration criteria for MQCAL methods.
method Proposes a novel MQCAL method using weighted rank aggregation.
result Achieves superior results compared to state-of-the-art MQCALs.
Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random de…
Decentralized ranking consensus via gossip for robust and scalable systems.
problem Achieving reliable and resilient consensus on collective rankings in a decentralized setting.
method Random gossip communication for decentralized computation of global rankings.
result Robust and scalable consensus on collective rankings achieved through decentralized, local interactions.
Introduces SoRR for aggregating losses in supervised learning.
problem Aggregating individual losses into a single output for machine learning models.
method Sum of ranked range (SoRR) minimization using DCA.
result Demonstrates effectiveness of AoRR and TKML in improving robustness of multi-label learning.
Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied t…
PREMA recovers detailed data from aggregated views.
problem Reconstructing detailed data from aggregated views.
method Low-rank tensor factorization.
result Recovery guarantees under certain conditions.
Hierarchical framework for model evaluation on leaderboards
problem Uncertainty and variability in model performance across tasks
method Hierarchical framework with task-level and leaderboard-level rank prediction intervals
result Statistically valid and informative model rank intervals
New model reduces bias in crowdsourced pairwise comparisons.
problem Crowdsourced pairwise comparisons are biased due to perceptual factors.
method factorBT model accounts for irrelevant factors affecting worker answers.
result factorBT produces more accurate rankings than previous models.
CoarsenRank improves robustness in rank aggregation despite model misspecification.
problem Rank aggregation under model misspecification in real-world scenarios.
method CoarsenRank designs a neighborhood of ideal preferences to handle agnostic noise-corrupted data.
result CoarsenRank achieves robustness against model misspecification within a defined neighborhood.
Upstreamness and downstreamness in input-output analysis from local and aggregate informationphysics.soc-ph
New method ranks sectors and countries using local and aggregate I-O data.
problem Ranking sectors and countries in global value chains using incomplete I-O tables.
method Rank- approximation to I-O tables using local and aggregate information.
result Consistently good performance in reconstructing rankings of upstreamness and downstreamness.
We solve the multi-criteria benchmarking problem by formalizing it as a social choice problem and identifying conditions for meaningful rankings.
problem Aggregating multiple metrics into a single ranking for models in benchmarking problems.
method Formalizing multi-criteria benchmarking as a social choice problem and identifying sufficient conditions for meaningful rankings.
result We prove that meaningful multi-criteria benchmarking becomes possible under certain preference conditions (single-peaked, group-separable, distance-restricted).
Improved ranking method for scarce data with feature info.
problem Ranking items with limited comparisons and feature data.
method Modified RankCentrality using diffusion methods for feature info.
result Meaningful rankings even with scarce comparisons.
In recent years rank aggregation has received significant attention from the machine learning community. The goal of such a problem is to combine the (partially revealed) preferences over objects of a large population into a single, relatively consistent ordering of those objects. However, in many cases, we might not w…
Spectral methods reduce the complexity of Markov processes.
problem Modeling and simplifying state-transition systems.
method Spectral decomposition and state aggregation.
result Developed methods to estimate low-rank Markov models.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
problem Over-engineering in GNN architectures for common semi-supervised node classification datasets.
method Replacing feature aggregation with a non-parametric learner to streamline GNN design.
result Non-parametric regression is effective for semi-supervised learning on sparse, directed networks.
Paper explores statistical and computational limits of estimating low-rank Gaussian mixtures.
problem Estimating low-rank matrix-variate observations with optimal statistical and computational limits.
method Low-rank Gaussian mixture model (LrMM) and minimax lower bounds.
result Minimax optimality of maximum likelihood estimator and spectral aggregation method.
This paper tackles ranking preferences through local consensus, improving prediction accuracy.
problem Predicting individual preferences over a set of items based on observed characteristics.
method Proposes ranking median regression, introducing local consensus/median for efficient learning.
result Developed efficient methods for ranking median regression, achieving fast learning rates.
Proposes a new ranking method based on analogical reasoning.
problem Object ranking using feature vectors.
method Analogical reasoning applied to instance-based learning and rank aggregation.
result Competitive results across various domains.
Paper proposes a method to recover rankings from limited comparisons using low-rank matrix completion.
problem Rank aggregation from pairwise comparisons with limited and noisy data.
method Low-rank matrix completion, alternating minimization algorithm, maximum likelihood estimation.
result Improved algorithm performance over state-of-the-art methods.
Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking pur…
The paper extends multiple instance learning to multiclass and regression problems.
problem Learning from aggregate observations where supervision is given to sets of instances.
method Probabilistic framework for various aggregate observations, including classification and regression.
result The proposed estimator has nice convergence properties under mild assumptions.
SUMMA aggregates predictions without labeled data.
problem Lack of labeled data limits ensemble methods.
method Developed SUMMA framework for unlabeled data.
result Estimates base classifier performances and optimal ensemble strategy.
The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In mo…
Framework benchmarks optimizers on multiple criteria.
problem Benchmarking optimizers across diverse test functions.
method Union-free generic depth function for partial orders/rankings.
result Identifies central and outlying rankings of optimizers.
Proposes DATELINE for aggregating k-ary preferences with uncertainty.
problem Aggregating k-ary preferences with feature information and uncertainty.
method Employing deep neural networks and a weighted Plackett-Luce model with uncertainty vectors.
result Provides theoretical guarantees for robustness.
A new method aggregates generative classifiers to resist adversarial attacks.
problem Adversarial attacks on deep neural networks.
method Rank-aggregating ensemble of generative classifiers trained on intermediate layer responses.
result The ensemble of generative classifiers shows robustness to adversarial attacks.
Paper uses HodgeRank and information maximization for efficient crowdsourced ranking.
problem Crowdsourced ranking quality improvement with limited budget.
method Information maximization applied to HodgeRank for active sampling.
result Boosts sampling efficiency compared to traditional methods.
Hierarchical Partial-Order Models for Ranking
problem Rank aggregation combining ordered lists
method Hierarchical partial-order models
result Bayesian inference for latent poset hierarchy