A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimal privacy-preserving ranking from noisy comparisons.
New method improves ABC for Bayesian model comparison.
New proposed models are often compared to state-of-the-art using statistical significance testing. Literature is scarce for classifier comparison using metrics other than accuracy. We present a survey of statistical methods that can be used for classifier comparison using precision, accounting for inter-precision corre…
Comparison data arises in many important contexts, e.g. shopping, web clicks, or sports competitions. Typically we are given a dataset of comparisons and wish to train a model to make predictions about the outcome of unseen comparisons. In many cases available datasets have relatively few comparisons (e.g. there are on…
Paper tackles clustering with ordinal comparisons, achieving near-optimal results.
Recent work by Locatello et al. (2018) has shown that an inductive bias is required to disentangle factors of interest in Variational Autoencoder (VAE). Motivated by a real-world problem, we propose a setting where such bias is introduced by providing pairwise ordinal comparisons between instances, based on the desired…
Study compares nonsmooth spaces with integrable Ricci bounds.
Efron et al. (2001) proposed empirical Bayes formulation of the frequentist Benjamini and Hochbergs False Discovery Rate method (Benjamini and Hochberg,1995). This article attempts to unify the `two cultures' using concepts of comparison density and distribution function. We have also shown how almost all of the existi…
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required. Instead, pairwise comparison of function values is used. Our algorithm consists of two steps; one is the direction estimate step and the o…
Develops comparison methods for semilinear elliptic problems on Riemannian manifolds with Ricci lower bound.
SC improves robustness in model comparison for misspecified models.
In a context where most published articles are devoted to the development of "new methods", comparison studies are generally appreciated by readers but surprisingly given poor consideration by many scientific journals. In connection with recent articles on over-optimism and epistemology published in Bioinformatics, thi…
Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal embedding, where we want to learn an embedding in an Euclidean space of the given instances that preserves the comparison order as well as pos…
Proposes a revenue function to evaluate dendrograms from comparisons.
This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due t…
This paper compares and evaluates methods for evaluating statistical models using benchmarking data and simulations.
A novel method compares 3D point clouds using information geometry.
Active sampling algorithm improves accuracy of inferred scores from pairwise comparisons.
Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a…
This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of objects can be identified by standard sorting methods using pairwise comparisons. We are interested in natural situations in which relationships among the o…
New methods rank players using covariates and comparisons, outperforming existing algorithms.
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object is closer to object than to object ." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet …
We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving A…
Develops a method to infer partial rankings from sparse comparisons.
Self-consistency improves the accuracy of model comparison methods.
Paper tackles ranking items with a semi-random comparison graph and a monotone adversary.
New method ranks competitors from multiple types of comparisons.
New methods resolve conflicting treatment effect estimates in health tech assessments.
The paper develops a method to estimate consumer preferences from observed rankings.
New model for pairwise comparisons without stochastic transitivity.
Enhances time series comparison by simplifying warping paths.
Novel method for Bayesian model comparison using deep learning.
We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder t…
Proves curvature comparison theorem for manifolds with conical singularities.
New oracle uses uncertainty for active classification with noisy feedback.
Graph comparison ties to Alexandrov's theorems.
This paper proposes a new method for solving the well-known rank aggregation problem from pairwise comparisons using the method of low-rank matrix completion. The partial and noisy data of pairwise comparisons is transformed into a matrix form. We then use tools from matrix completion, which has served as a major compo…
Comparison of UQ methods in deep learning for a simple physical system.
The paper improves spectral ranking methods for diverse comparison graphs.
Paper proposes a sequential statistical test for comparing imitation learning policies with near-optimal stopping.
Improved ranking method for scarce data with feature info.
Gene annotation has traditionally required direct comparison of DNA sequences between an unknown gene and a database of known ones using string comparison methods. However, these methods do not provide useful information when a gene does not have a close match in the database. In addition, each comparison can be costly…
Paper develops methods for estimating gradients of Finslerian Schrödinger equations.
Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression …
In this work, we will verify some comparison results on Kahler manifolds. They are complex Hessian comparison for the distance function from a closed complex submanifold of a Kahler manifold with holomorphic bisectional curvature bounded below by a constant, eigenvalue comparison and volume comparison in terms of scala…
Evidence Networks simplify Bayesian model comparison for complex models.
The paper extends volume comparison results to total σ_l-curvature.
Paper extends curvature estimates to new tensor types.