Paper mines rank data patterns from rankings.
problem Mining rank data patterns from rankings.
method Proposes algorithms for frequent rankings and dependencies.
result Experimental validation of algorithms on synthetic and real data.
Proposes tensor Q-rank for better tensor rank recovery in complex data.
problem Improving tensor rank recovery for complex data with low sampling rate.
method Introduces tensor Q-rank and two selection methods for Q, proposing VMTQN and MOTQN models. result Demonstrates superior performance in tensor completion problems compared to TNN-based methods.
Develops a method to infer partial rankings from sparse comparisons.
problem Challenges in ranking items with limited and noisy comparisons.
method Nonparametric Bayesian approach for learning partial rankings.
result Finds partial rankings that distinguish meaningful differences only when data supports it.
New methods provide stable ranking without assumptions on data distributions.
problem Stability issues in ranking problems with noisy data.
method Developed a stability framework and two ranking operators.
result Guaranteed stability without assumptions on data distributions.
Low-rank approach to metric learning from data.
problem Learning a Mahalanobis metric from data.
method Low-rank geometric mean metric learning (GMML) approach.
result Competes effectively with GMML at lower ranks.
GANs improve missing data imputation for ranking tasks.
problem Missing data in ranking datasets violates standard assumptions.
method Conditional Imputation GAN for Extended Missing At Random and Extended Always Missing At Random mechanisms.
result Optimal GAN imputation for EMAR and EAMAR mechanisms.
New ranking models for time series data using GARCH-type approach.
problem Handling time series of ranking data.
method Developed ranking GARCH models based on Mallows distribution and maximum likelihood estimation.
result The proposed models capture temporal dynamics of rankings effectively.
Paper proposes methods for estimating partially ranked data with graph regularization.
problem Estimating parameters for partially ranked data with missing data.
method Graph regularization in conjunction with Expectation-Maximization algorithm.
result The proposed estimators work well under non-ignorable missing mechanisms.
A new kernel for ranked data tackles computational challenges.
problem Complex geometric structure and partial rankings make existing algorithms infeasible for real-world applications.
method Derives a graph cut kernel that combines submodular optimization and kernel-based methods.
result The graph cut kernel efficiently handles large-scale ranked data.
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.
Paper introduces robust methods for consensus ranking in AI systems.
problem Developing reliable ranking systems in AI despite contaminated data.
method Introduces robustness concepts and statistical methods for consensus ranking.
result Proposes extensions of breakdown point for consensus ranking.
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.
A new ranking algorithm learns data affinity and ranking scores simultaneously.
problem Retrieving similar objects in large databases is challenging.
method Proposes a ranking algorithm that learns data affinity and ranking scores simultaneously, using adaptive neighbors and smoothness constraints.
result The proposed algorithm outperforms existing methods in synthetic and real datasets.
Model learns tensor representations from imperfect multimodal data.
problem Learning from imperfect multimodal data with noise or missing entries.
method Tensor rank minimization to regularize rank of tensor representations.
result Model effectively learns tensor representations from imperfect data.
Flexible ranking models from choice data.
problem Difficulties in modeling, learning from, and predicting rankings.
method Choice-based ranking models using repeated selection.
result Choice-based ranking models outperform existing models in various ranking tasks.
Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper, we consider the scenario where the rank is not given and we aim to approximate the unknown rank based on the location of sampled entries an…
The paper tackles fair ranking in ranked data by addressing causal discrimination.
problem Fairness in predictive models for ranked data.
method Mapping rank positions to continuous scores, building causal graphs, and using path-specific effects.
result Effective algorithms for discovering and removing discrimination from ranked datasets.
CRS model improves ranking data modeling with theoretical guarantees.
problem Lack of rich, multimodal models for ranking data.
method Contextual Repeated Selection (CRS) model for multimodal ranking data.
result CRS model significantly outperforms existing methods in various ranking contexts.
We consider the classic problem of establishing a statistical ranking of a set of n items given a set of inconsistent and incomplete pairwise comparisons between such items. Instantiations of this problem occur in numerous applications in data analysis (e.g., ranking teams in sports data), computer vision, and machine …
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…
GNNRank uses neural networks to learn global rankings from competition match data.
problem Learning global rankings from pairwise comparisons in directed graphs.
method Proposes GNNRank, a trainable GNN-based framework with digraph embedding and new objectives.
result GNNRank achieves competitive and superior performance compared to baselines.
The paper reveals low-rank structure in neural network gradients, influenced by data and model parameters.
problem Investigating low-rank structure in gradients of neural networks under relaxed assumptions.
method Spiked data model, relaxation of isotropy assumptions, analysis of mean-field and neural-tangent-kernel scalings.
result Gradient of input weights is approximately low rank, dominated by two rank-one terms.
Unified model combines scores and rankings for grant panel review.
problem Combining scores and rankings for quality assessment in panel review.
method Mallows-Binomial model with tree-search algorithm for exact MLE.
result Model combines scores and rankings to quantify object quality and measure consensus.
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…
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been conside…
Proposes a new method for high-dimensional data analysis.
problem Sparse PCA limitations in high-dimensional data analysis.
method Low-rank principal eigenmatrix analysis, matricized rank-truncated power method.
result Competitive empirical performance in synthetic data sets.
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational da…
Bayesian model improves image completion accuracy by automatically learning low rank structure.
problem Improving image completion accuracy with limited data and avoiding overfitting.
method Developed a Bayesian low rank tensor ring model with multiplicative interaction and Student-T distribution for sparse core factors.
result The proposed method outperforms state-of-the-art image completion techniques, especially in recovery accuracy.
CNNs trained by gradient descent can learn intrinsic image rank robustly to background noises.
problem Understanding the intrinsic dimension of data in over-parameterized CNNs.
method Theoretical analysis and experiments on synthetic and real datasets.
result CNNs trained by gradient descent can learn the intrinsic dimension of clean images robustly to background noises.
New method interprets ranked data on permutahedron graph.
problem Interpreting and exploiting structure in ranked data sets.
method Combining combinatorial representation theory and signal processing on graphs.
result Developed scalable transform method using Parseval frames.
Ranking recommendation algorithms across datasets using Bradley-Terry model
problem Comparing recommendation algorithms across different datasets
method Introduce a novel data-driven ranking methodology based on Bradley-Terry model
result The obtained ranking depends on key dataset statistics
This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.
problem Estimating the rank of a low-rank tensor model of joint PMF from observed data.
method Bayesian framework for estimating low-rank components and rank simultaneously, using variational inference.
result Automatic rank detection and improved estimation accuracy compared to cross-validation methods.
Study ranks of elliptic curves via prime averages.
problem Classifying elliptic curves by rank.
method Average Frobenius trace over primes, data science experiments.
result Oscillating pattern in average trace values, correlates with rank.
RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.
problem Determining the optimal rank in NMF.
method RSIC analyzes sensitivity of relative residuals to different initializations.
result RSIC identifies meaningful ranks consistent with data structure.
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.
New methods extend kernel estimators for partial rankings, improving performance in machine learning tasks.
problem Incomplete rankings data in real-world applications.
method Antithetic and Monte Carlo kernel estimators for partial rankings, variance reduction scheme.
result Improved antithetic kernel estimator with lower variance and better performance.
Algorithm recovers multiple low-rank matrices from unlabeled data.
problem Learning mixtures of low-rank models from unlabelled data.
method Three-stage meta-algorithm that copes with non-convexity and noise.
result Near-optimal sample and computational complexities under Gaussian designs.
Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why the low rank structure ap…
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
Framework for optimizing search engine rankings using observational data.
problem Optimizing ranking policies for search engines using limited observational data.
method Formulated expected reward optimization problem, estimated context value distribution, trained ranking policy via Bayesian inference.
result Demonstrated trade-offs in ranking policies trained on empirical reward estimates.
Paper stabilizes persistent homology rank functions for statistical inference.
problem Stability issues in persistent homology rank functions.
method Derive stability results for rank functions under FDA metrics.
result Rank functions stabilize, improving statistical inference.
Develops methods to estimate high rank tensors from noisy data.
problem Estimating high rank tensors from noisy observations.
method Generative latent variable tensor model, polynomial-time spectral algorithm.
result Achieves computationally optimal rate for signal tensor estimation.
Bayesian model identifies outliers and determines tensor rank in streaming data.
problem Outliers and over-fitting in streaming tensor factorization.
method Variational Bayesian Inference for robust tensor rank determination and outlier identification.
result Model accurately identifies sparse outliers and determines tensor rank.
New model for high rank matrix completion with online and batch methods.
problem Matrix completion for high rank matrices with latent structure.
method Kernel trick to map data into a high dimensional feature space, explicit parametrization of low dimensional subspace, online fitting procedure.
result Online method can handle streaming data and adapt to non-stationary latent structure.
New methods recover best rank-r approximations from few entries.
problem Recovering best rank-r approximations from limited data entries.
method Two agnostic approaches: spectral truncation and projected gradient descent.
result Projected gradient descent yields superior performance.
Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object …
The paper recovers missing data entries of high-rank matrices using polynomial polynomials.
problem Recovering missing entries of high-rank matrices with low intrinsic dimension.
method Developed a new polynomial matrix completion method using the kernel trick and relaxation of rank objective.
result Identified complete matrix of minimum intrinsic dimension by minimizing rank in high-dimensional feature space.
A new model for data with zeros or missing values.
problem Data with excess zeros or missing values.
method Composite loss framework for low-rank modeling, combining generalized low-rank and hurdle methods.
result Demonstrated on a manufacturing data set and applied to missing value imputation.