The study proposes algorithms to minimize rating discordance in missing data.
problem Missing ratings in combined rating lists.
method Optimization models and algorithms that minimize total rating discordance.
result The proposed methods outperform state-of-the-art imputation methods in accuracy.
Tensor methods tackle high-dimensional additive index models with discordance and heterogeneity.
problem High-dimensional datasets with sampling problems and heterogeneity.
method Method of moments based procedures for estimating indices of discordant additive index models.
result Rates of convergence of estimators in both high and low-dimensional settings.
Advances neural tri-factorization for clustering and discordance analysis of multi-typed data.
problem Challenges in analyzing heterogeneous, multimodal relational data.
method Deep collective matrix tri-factorization for spectral clustering and cluster association learning.
result Demonstrates efficacy over previous non-neural approaches in clustering and discordance analysis.
New method finds local anomalies in time series by considering context information.
problem Finding anomalous subsequence in long time series is difficult.
method Introduces semantic discord, an approach that incorporates context information.
result Significantly outperforms state-of-the-art methods in locating anomalies.
Ideas from deformation quantization applied to algebras with one generator lead to methods to treat a nonlinear flat connection. It provides us elements of algebras to be parallel sections. The moduli space of the parallel sections is studied as an example of bundle-like objects with discordant (sogo) transition functi…
Study uses sentiment analysis to predict cryptocurrency token returns in virtual reality.
problem Predicting cryptocurrency token returns in virtual reality economies.
method Used BERT for sentiment analysis and developed LSTM models integrating multi-modal features.
result Multi-modal model significantly outperforms price-only baseline in prediction accuracy.
SDSR reconstructs species trees from genetic markers efficiently.
problem Challenges in reconstructing species trees from genetic data.
method Spectral divide-and-conquer approach based on graph theory.
result SDSR achieves up to 10-fold faster runtime with comparable accuracy.
A new tool, matrix profile, finds all pair similarities in time series data.
problem Finding all pair similarities in time series data.
method Near universal time series data mining tool called matrix profile.
result Matrix profile solves the all-pairs-similarity-search problem for time series subsequences.
New algorithm detects unique events in time series data.
problem Detecting anomalous events in unknown properties.
method Model-free, unsupervised detection using Temporal Outlier Factor (TOF).
result TOF outperforms traditional outlier detection methods.
Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and s…
Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.
problem Quantifying trust dynamics and redistribution between centralized and decentralized exchanges.
method Interdisciplinary approach combining causal inference and computational text analysis.
result Significant price declines and capital reallocation from centralized to decentralized exchanges following the FTX collapse.
The abstract shows how conditional Kendall's tau can be seen as a classification problem.
problem Estimating conditional Kendall's tau from random variables.
method Rewriting the problem as a classification task, proving consistency and asymptotic normality of estimators, adapting machine learning techniques.
result The consistency and asymptotic normality of penalized approximate maximum likelihood estimators.
Optimizes choice sets to influence group decisions.
problem Maximizing agreement or disagreement in group decisions.
method Discrete choice modeling to develop optimization framework.
result Promoting a choice can be easier than encouraging consensus or discord.
Proposes an optimization framework for sparse robust subspace estimation.
problem Sparse robust one-dimensional subspace estimation.
method l1-norm regularization, linear relaxation, simple ratios, sorting techniques.
result Achieves global optimality for sparse robust subspace with polynomial time efficiency.