On-device research index

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.

168,695 papers · 148 categories

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4 results for “DR-learning”

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrastsstat.ML

EP-learning framework improves causal contrast estimation efficiency.

problem Estimating heterogeneous causal contrasts efficiently and stably.

method EP-learning framework combining T-learning and DR-learning.

result EP-learners are oracle-efficient and outperform competitors.

EP-learning causal contrasts T-learning DR-learning oracle efficiency

2024-02-03abs ↗pdf ↗

Distributionally Robust Safe Screeningstat.ML

DRSS method identifies unnecessary samples and features in DR covariate shift.

problem Identifying unnecessary samples and features in DR covariate shift.

method Combines DR learning and safe screening techniques.

result DRSS method provides reliable identification of unnecessary samples and features under specified distribution uncertainty.

Distributionally Robust Safe Screening DR covariate shift Empirical Risk Minimization Weighted Optimization

2024-04-25abs ↗pdf ↗

Multiple Robust Learning for Recommendationcs.IR

Proposes MR estimator for unbiased recommendation models.

problem Data biases in recommender systems lead to inaccurate predictions.

method Introduces multiple robust (MR) learning approach combining multiple imputation and propensity models.

result MR estimator achieves unbiasedness when any of the models is accurate.

recommender systems doubly robust multiple robust bias mitigation generalization error

2022-07-09abs ↗pdf ↗

Safe Distributionally Robust Feature Selection under Covariate Shiftstat.ML

Safe-DRFS selects features robust to covariate shifts for reliable performance.

problem Feature selection fails in diverse deployment environments.

method Safe-DRFS extends safe screening to distributionally robust settings under covariate shift.

result Safe-DRFS identifies a feature subset encompassing optimal subsets across distribution shifts.

feature selection distributionally robust covariate shift sparse sensing false feature elimination

2026-03-17abs ↗pdf ↗