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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6 results for “group-aware”

Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priorsstat.ML

Improves model robustness to shifts in subpopulations.

problem Poor performance of ML models under data distribution shifts.

method Develops group-aware priors (GAP) over neural network parameters.

result Training with GAP yields state-of-the-art performance.

subpopulation shifts robustness group-aware priors Bayesian inference neural networks

2024-03-14abs ↗pdf ↗

Group-Aware Matrix Estimation and Latent Subspace Recoverystat.ML

GAME improves matrix completion by considering subgroup-specific latent structures.

problem Heterogeneous data with overlapping categories, smoothing away subgroup-specific variation.

method Group-Aware Matrix Estimation (GAME) with overlapping nuclear-norm penalties.

result GAME outperforms global low-rank estimators in structured missingness regimes.

matrix completion latent subspace recovery group-aware overlapping categories nuclear-norm penalties

2026-05-19abs ↗pdf ↗

Adaptive Conformal Prediction via Bayesian Uncertainty Weighting for Hierarchical Healthcare Datacs.LG

Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.

problem Jointly satisfying distribution-free coverage guarantees and risk-adaptive precision in clinical decision-making.

method Integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.

result Achieves target coverage (94.3% vs 95% target) with adaptive precision, 21% narrower intervals for low-uncertainty cases.

Bayesian uncertainty conformal prediction healthcare data clinical decision-making risk stratification

2026-01-03abs ↗pdf ↗

Leveraging Structure for Improved Classification of Grouped Biased Datastat.ML

Algorithm improves binary classification of biased grouped data.

problem Improving binary classification for biased, grouped data.

method Assumes partition-projected class-conditional invariance across groups and derives a semi-supervised algorithm to learn a group-aware classifier.

result Demonstrates improved area under the ROC curve compared to baselines.

semi-supervised learning grouped data biased data binary classification invariance

2022-12-07abs ↗pdf ↗

DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecastingcs.LG

DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.

problem Forecasting long-term time series data across diverse scales.

method Dynamic tokenizer, multi-scale transformer, dynamic sparse learning, rotary position encoding.

result DRFormer outperforms existing methods in forecasting accuracy.

time series forecasting transformer model dynamic tokenization multi-scale sparse learning

2024-08-05abs ↗pdf ↗

Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindnessstat.ME

Unified framework for fair classification with group-blindness/awareness guarantees.

problem Challenges in enforcing fairness and group-blindness in binary classification.

method Unified framework based on post-processing procedure, applicable to various group fairness notions.

result Minimax rate-optimality of the proposed algorithm with controlled excess risk.

fairness binary classification group-blindness post-processing minimax lower bound

2024-10-21abs ↗pdf ↗