Paper proposes a new anomaly detection method using Random Forest with Mallows-like criterion.
problem Inherent uncertainty in model selection for anomaly detection.
method Integrates Mallows-like criterion into Random Forest algorithm for anomaly detection.
result Proposed method outperforms traditional methods in accuracy and robustness.
Paper improves feature selection accuracy using transfer learning.
problem Improving feature selection accuracy in information criteria-based methods.
method Proposes TLCp, a transfer learning procedure based on Mallows' Cp.
result TLCp outperforms conventional Cp in accuracy and stability.
The paper addresses model averaging and ensembling, providing theoretical and practical insights.
problem Combining least squares estimators from multiple candidate models for improved predictive accuracy.
method Establishes oracle inequalities for Mallows' Cp criterion, proposes a novel Mallows-type MA procedure. result Demonstrates the effectiveness of the proposed Mallows-type MA estimator through numerical experiments.
The Mallows model, introduced in the seminal paper of Mallows 1957, is one of the most fundamental ranking distribution over the symmetric group Sm. To analyze more complex ranking data, several studies considered the Generalized Mallows model defined by Fligner and Verducci 1986. Despite the significant research in…
Paper extends top-k Mallows model for better user preference analysis.
problem Capturing real-world user preferences focusing on a limited set of items.
method Generalized top-k Mallows model, novel sampling scheme, efficient algorithm, active learning.
result New tools for analysis and prediction in decision-making scenarios.
The paper tackles learning true rankings from noisy, incomplete data.
problem Learning true rankings from incomplete and noisy data.
method Introduces a selective Mallows model for noisy rankings and derives upper and lower bounds on sample complexity.
result Strong asymptotically tight bounds on sample complexity for learning complete rankings and top-k rankings.
New method speeds up model selection for complex scientific tasks.
problem Exhaustive model selection is computationally infeasible for large model spaces.
method Branch-and-bound algorithm with non-monotonic criteria.
result Guaranteed identification of optimal models with significant computational speedups.
The paper proposes methods to identify and sample from mixtures of Mallows models for top-k rankings.
problem Identifying and sampling from mixtures of Mallows models for top-k rankings in a heterogeneous population.
method Efficient sampling algorithms and identifiability proofs for both components of the mixture.
result The identifiability and learnability of the Mallows components' parameters in the mixture.
We propose a novel parameterized family of Mixed Membership Mallows Models (M4) to account for variability in pairwise comparisons generated by a heterogeneous population of noisy and inconsistent users. M4 models individual preferences as a user-specific probabilistic mixture of shared latent Mallows components. Our k…
Mixtures of Mallows models are a popular generative model for ranking data coming from a heterogeneous population. They have a variety of applications including social choice, recommendation systems and natural language processing. Here we give the first polynomial time algorithm for provably learning the parameters of…
Extends Mallows model to handle item indifference in rankings.
problem Real data often contains item indifference, challenging strict preference assumptions.
method Proposes Clustered Mallows Model (CMM) to accommodate item indifference.
result CMM provides a flexible representation of rank collections with ordered clusters.
New algorithm learns permutations mixtures with optimal sample complexity.
problem Learning mixtures of permutations in high-dimensional settings.
method Combining groups of pairwise comparisons and combinatorial method of moments.
result Optimal sample complexity proportional to log(n) for high-dimensional data.
New voting rules protect against strategic voting by robust statistics.
problem Strategic voting can skew election outcomes.
method Revisit Mallows model, develop robust estimator.
result Efficient estimator achieves nearly optimal robustness.
We generalize Mallows model to learn distance metrics from data.
problem Learning optimal distance metrics from noisy ranking data.
method Propose Lα distances and develop FPTAS for sampling and MLE. result Strong consistency of estimators for various α and β. Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. To date, there has been relatively limited study of regression, classification, and testing problems …
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.
This paper protects rankings from differential privacy breaches.
problem Leakage of personal information in rankings.
method Develops ε-ranking differential privacy and a multistage ranking algorithm.
result Establishes the connection between Mallows model and ε-ranking differential privacy.
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new results: (1) We show that search methods can estimate both the central ranking pi0 and the model parameters theta exactly. The search is n!…
A new algorithm improves solving QAP with better performance.
problem Solving the Quadratic Assignment Problem (QAP) efficiently.
method Estimation of Distribution Algorithms (EDAs) with a non-parametric distance-based Mallows model.
result The proposed algorithm outperforms existing methods for QAP.
The study of isospectral surfaces in Euclidean and hyperbolic geometries.
problem Existence of non-isometric surfaces with identical chord length distributions.
method Construction of isospectral pairs of hyperbolic surfaces without common covers.
result Found isospectral pairs of hyperbolic surfaces with no common cover.
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…
Extends Fisher's Discriminant Analysis for interval-valued data.
problem Classifying entities represented by intervals and histograms.
method Adapts Fisher's Discriminant Analysis using Moore's interval arithmetic and Mallows' distance.
result Discriminant directions for interval-valued data are numerically maximized.
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its properties and give procedures to estimate it from data. For estimation of multimodal distributions we introduce the Exponential-Blurring-Mean-Sh…
The paper applies communication theory to improve language model reranking.
problem Ensuring safety and accuracy in language model outputs.
method Drawing parallels between communication theory and language model reranking, the authors propose a protocol to improve reliability.
result The proposed protocol can achieve asymptotically error-free performance in noisy communication scenarios.
Hierarchical Partial-Order Models for Ranking
problem Rank aggregation combining ordered lists
method Hierarchical partial-order models
result Bayesian inference for latent poset hierarchy
Novel algorithm optimizes decision trees for nonlinear metrics.
problem Optimizing decision trees for nonlinear metrics like F1-score.
method Bi-objective optimisation approach to find optimal trees on Pareto frontier.
result The optimal tree for nonlinear metrics lies on the Pareto frontier.
Cross-validation estimates model performance on unseen data, not training data.
problem Understanding how cross-validation estimates prediction error and its limitations.
method Analyzing linear models and popular prediction error estimates, introducing nested cross-validation.
result Cross-validation estimates the average prediction error of models fit on other unseen training sets, not the model at hand.
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.
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters …
We study the problem of learning an unknown mixture of k rankings over n elements, given access to noisy samples drawn from the unknown mixture. We consider a range of different noise models, including natural variants of the "heat kernel" noise framework and the Mallows model. For each of these noise models we giv…
We study the residual bootstrap (RB) method in the context of high-dimensional linear regression. Specifically, we analyze the distributional approximation of linear contrasts c⊤(β^ρ−β), where β^ρ is a ridge-regression estimator. When regression coefficients are estimated via least squares, classical…
This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression, spline smoothing or locally weighted regression, and the choice of a kernel in multiple kern…
In sparse regression modeling via regularization such as the lasso, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows' Cp type criteria may be used as a tuning param…
Study disproves a generalized numerical criterion for certain pairs.
problem Generalized numerical criterion for pairs
method Provided counterexamples
result Negative answer to the generalized numerical criterion problem
New polynomial criterion for periodic knots identified.
problem Identifying periodic knots efficiently.
method Examined HOMFLY-PT and Kauffman polynomials of periodic links.
result Criterion is stronger than existing methods.
New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.
problem Computing robust policies for high-stakes decisions with limited data.
method Soft-robust criterion using risk measures, two algorithms for optimization.
result Our algorithms produce less conservative solutions than existing methods.
We introduce a new criterion to determine the order of an autoregressive model fitted to time series data. It has the benefits of the two well-known model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bay…
Modified Bakry-Émery criterion inequality for Tsallis entropy monotonicity.
problem Establishing improved logarithmic Sobolev inequalities and monotonicity of Tsallis entropy.
method Proving a one-parameter family of weighted Bakry-Émery Γ2 criterion inequalities and a modified inequality. result Yields a family of sharp Sobolev inequalities and monotonicity of Tsallis entropy.
Birg{é} and Massart proposed in 2001 the slope heuristics as a way to choose optimally from data an unknown multiplicative constant in front of a penalty. It is built upon the notion of minimal penalty, and it has been generalized since to some "minimal-penalty algorithms". This paper reviews the theoretical results ob…
A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian information criterion. Adjustment of…
New criterion improves predictive evaluation in weighted inference scenarios.
problem Improving predictive evaluation in scenarios with different likelihoods for estimation and evaluation.
method Developed the posterior covariance information criterion (PCIC) to handle weighted likelihood inference.
result PCIC is asymptotically unbiased for quasi-Bayesian generalization error in weighted inference.
Criterion for stopping conjugacy class enumeration in triangle groups.
problem Enumerating all conjugacy classes in cocompact triangle groups.
method Encoding by P. Dehornoy and T. Pinsky; stopping criterion based on geometric length.
result Stopping criterion for the generation of conjugacy classes in cocompact triangle groups.
In [D.A. Fedoseev, V.O. Manturov, A sliceness criterion for odd free knots,arXiv:1707.04923], the authors proved a sliceness criterion for odd free knots: free knots with odd chords. In the present paper we give a similar criterion for stably odd free knots. Some additional results on knot sliceness and cobordism are g…
New criterion for solving inverse Hessian equations, including J-equation.
problem Existence of solutions to inverse Hessian equations, including J-equation.
method Stability of pairs in the sense of Paul, formulated in terms of GIT criterion.
result New numerical criterion for existence of solutions to inverse Hessian equations.
Criterion for nilpotent Lie groups to have nilsolitons.
problem Existence of nilsolitons in nilpotent Lie groups.
method Algebraic criterion for nilpotent Lie algebras, proving necessary and sufficient condition for nilsolitons.
result Criterion provides a necessary and sufficient condition for nilpotent Lie groups to admit nilsolitons.
Study proposes a stopping criterion for active learning based on error stability.
problem Improving predictive performance in active learning by adaptively annotating samples.
method Proposes a stopping criterion based on error stability for Bayesian active learning.
result Demonstrates the proposed criterion stops active learning at the appropriate timing for various models and datasets.
Clarifies boundary criterion for non-one-ended subgroups in cubulation theory.
problem Boundary criterion for relative cubulation in non-one-ended subgroups.
method Showed that if boundary criterion is satisfied for a relatively hyperbolic group, the group admits a relatively geometric action on a CAT(0) cube complex.
result The refinement of the boundary criterion is useful for constructing new relative cubulations.
Extends Kelly Criterion to more complex betting scenarios.
problem Maximizing long-term growth in complex betting models.
method Generalizes Kelly Criterion to Lévy processes and high-frequency limits.
result Improved strategies for high-frequency betting.