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arXiv research

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.

168,742 papers · 148 categories

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8 results for “rank-preserving”

Learning Counterfactual Outcomes Under Rank Preservationcs.LG

Paper proposes a method to estimate counterfactual outcomes without a known SCM.

problem Estimating counterfactual outcomes without a known structural causal model.

method Introduces rank preservation assumption and a novel ideal loss for unbiased learning of counterfactual outcomes.

result The proposed method is effective and unbiased, as shown by theoretical analysis and experiments.

counterfactual inference rank preservation structural causal model ideal loss kernel-based estimator

2025-02-10abs ↗pdf ↗

Causal Fair Machine Learning via Rank-Preserving Interventional Distributionscs.LG

New method uses causal thinking to make AI fairer decisions.

problem Designing fair machine learning models that treat equal individuals equally and unequals unequally.

method Rank-preserving interventional distributions and warping method.

result Warping method effectively identifies discriminated individuals and mitigates unfairness.

fairness machine learning causality rank-preserving interventional

2023-07-24abs ↗pdf ↗

Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representationcs.LG

Proposes MvLPE for better multi-view representation learning.

problem Learning representations from multi-view data with varying correlations.

method Integrates multi-view data into a centroid view while maintaining low-rank reconstruction relations.

result MvLPE outperforms existing methods on benchmark datasets.

multi-view learning representation low-rank preserving centroid view multi-view information fusion

2020-06-14abs ↗pdf ↗

Counterfactual Identifiability via Dynamic Optimal Transportcs.LG

We establish a foundation for multivariate counterfactual identification using dynamic optimal transport.

problem Addressing the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data.

method Establish a foundation for multivariate counterfactual identification using continuous-time flows, including non-Markovian settings, with tools from dynamic optimal transport.

result Characterise the conditions under which flow matching yields a unique, monotone, and rank-preserving counterfactual transport map, ensuring consistent inference.

counterfactual identification dynamic optimal transport observational data multivariate outcomes causal inference

2025-10-09abs ↗pdf ↗

Obtaining the One-Holed Torus from Pants: Duality in an SL(3,C)-Character Varietymath.AG

The SL(3,C)-representation variety R of a free group F arises naturally by considering surface group representations for a surface with boundary. There is a SL(3,C)-action on the coordinate ring of R. The geometric points of the subring of invariants of this action is an affine variety X. The points of X parametrize is…

2008-03-25abs ↗pdf ↗

Nonparametric Identification and Inference for Counterfactual Distributions with Confoundingstat.ME

Proposes methods to identify and estimate counterfactual distributions with confounding.

problem Estimating counterfactual distributions in the presence of confounding.

method Nonparametric identification and semiparametric estimation using conditional copulas and machine learning.

result Valid inference for individual-level effects and nonparametric identifiability of latent confounding subspace.

counterfactual inference confounding nonparametric identification machine learning copulas

2026-02-17abs ↗pdf ↗

Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankingscs.LG

The paper introduces a framework to select efficient datasets for preserving model rankings.

problem Efficient evaluation of machine learning models on small, representative datasets.

method Bootstrap aggregation, clustering, design criteria, random baselines, and greedy farthest-first (FAFI).

result Several selection strategies improve rank preservation compared to random subsets, especially in time series classification.

dataset selection model ranking machine learning time series classification natural language processing

2026-06-26abs ↗pdf ↗

Is Causality Necessary for Efficient Portfolios? A Computational Perspective on Predictive Validity and Model Misspecificationq-fin.PM

The paper shows that causal identification is not essential for efficient portfolios, focusing on geometric sufficiency conditions.

problem The necessity of causal identification for efficient portfolios.

method Re-examination of predictive signals and their impact on portfolio efficiency under structural misspecification.

result Efficiency is governed by geometric sufficiency conditions (directional alignment, ranking preservation, and calibration) rather than causal identification.

portfolio optimization predictive validity model misspecification geometric sufficiency signal alignment

2025-07-30abs ↗pdf ↗