arXiv research

Paper proposes an unbiased classifier from triplet comparison data.

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…

SC improves robustness in model comparison for misspecified models.

New model reduces bias in crowdsourced pairwise comparisons.

The paper examines how optimizer comparisons in deep learning are influenced by hyperparameter tuning.

Paper tackles clustering with ordinal comparisons, achieving near-optimal results.

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…

We performed an empirical comparison of ICA and PCA algorithms by applying them on two simulated noisy time series with varying distribution parameters and level of noise. In general, ICA shows better results than PCA because it takes into account higher moments of data distribution. On the other hand, PCA remains quit…

Proposes a revenue function to evaluate dendrograms from comparisons.

DECS tool assesses swap rates of DEXes and Fusion outperforms competitors.

Study metric learning from limited preference comparisons, showing how low-dimensional structure can still reveal metric information.

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal solution is to use an efficient sorting algorithm, such as Quicksor…

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects $i$ and $j$ are more …

Study on deep neural networks for reward modeling with pairwise comparison data.

Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES…

We propose the point process model as the Poissonian-like stochastic sequence with slowly diffusing mean rate and adjust the parameters of the model to the empirical data of trading activity for 26 stocks traded on NYSE. The proposed scaled stochastic differential equation provides the universal description of the trad…

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 $x_i$ is closer to object $x_j$ than to object $x_k$." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet …

Paper tackles noisy comparison oracle for robust clustering algorithms.

Develops a statistical framework to measure uncertainty in model rankings based on human preferences.

SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.

This work analyzes Fréchet regression using comparison geometry, providing theoretical and practical insights.

The paper introduces uncertainty estimates for embedding objects based on noisy triplet comparisons.

Herd behavior is an important economic phenomenon, especially in the context of the recent financial crises. In this paper, herd behavior in global stock markets is investigated with a focus on intercontinental comparison. Since most existing herd behavior indices do not provide a comparative method, we propose a new h…

A new method learns from pairwise comparisons to predict sensitive data without making strong assumptions.

Modeling preference rankings with salient features to explain irrational choices.

ERM with square loss achieves sublinear error for learnable function classes with smoothed data.

The main object of Bayesian statistical inference is the determination of posterior distributions. Sometimes these laws are given for quantities devoid of empirical value. This serious drawback vanishes when one confines oneself to considering a finite horizon framework. However, assuming infinite exchangeability gives…

Generative Adversarial Networks create realistic financial correlation matrices.

This paper compares FAISS and FENSHSES for nearest neighbor search in Hamming space.

We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. We introduce a tight asymptotic regret lower bound that is based on the information divergence. An algorithm that is inspired by the Deterministic…

We study a new ensemble of random correlation matrices related to multivariate Student (or more generally elliptic) random variables. We establish the exact density of states of empirical correlation matrices that generalizes the Marcenko-Pastur result. The comparison between the theoretical density of states in the St…

The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages …

New oracle uses uncertainty for active classification with noisy feedback.

New algorithm learns human preferences from few comparisons efficiently.

The paper designs tests for comparing ranked preference data and finds significant differences.

Develops a method to infer partial rankings from sparse comparisons.

Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.

We study the dependence structure of market states by estimating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized ones, where the non-stationarity has been removed. The empirical pairwise copula for ea…

Bispectral OT improves dataset comparison by preserving intrinsic coherence.

Bayesian method infers contextual bandit policies robustly.

Extends covariance estimation with multiple targets for better performance.

This paper extends Median-of-Means to new learning problems involving pairwise comparisons.

We review statistical properties of models generated by the application of a (positive and negative order) fractional derivative operator to a standard random walk and show that the resulting stochastic walks display slowly-decaying autocorrelation functions. The relation between these correlated walks and the well-kno…

Paper investigates monotonicity issues in AI preference learning.

Box Thirding identifies the best arm efficiently under limited samples.

Improves Active Learning fairness and comparability across domains.

COPT optimizes graph distances via simultaneous optimal transport.

Two methods for quantile regression are compared and found to produce tighter intervals.