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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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11 results for “Group-Robust”

Towards Group Robustness in the presence of Partial Group Labelscs.LG

Paper tackles group robustness with partially labeled data.

problem Learning invariant representations from datasets with spurious correlations.

method Constructs a constraint set and derives a high probability bound for group assignment. Proposes an optimization algorithm for worst-off group assignments.

result Improvements in minority group's performance while preserving overall accuracy.

group robustness spurious correlations partial labels machine learning invariant representations

2022-01-10abs ↗pdf ↗

On Feature Learning in the Presence of Spurious Correlationscs.LG

Deep models learn spurious features correlated with target, but can still perform well.

problem Spurious correlations in feature learning.

method Empirical risk minimization and specialized group robustness training.

result Feature representations learned by ERM are competitive with specialized methods.

spurious correlations deep learning feature learning empirical risk minimization group robustness

2022-10-20abs ↗pdf ↗

Just Train Twice: Improving Group Robustness without Training Group Informationcs.LG

JTT improves model worst-group accuracy without group annotations.

problem Low worst-group accuracy in standard ERM models with spurious correlations.

method Two-stage approach: first ERM, then upweight misclassified examples.

result JTT closes 75% of the gap in worst-group accuracy compared to group DRO.

group robustness worst-group accuracy spurious correlations empirical risk minimization upweighting

2021-07-19abs ↗pdf ↗

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processesstat.ME

Paper tackles robust decision-making from multiple sites with shared structure.

problem Learning robust sequential decisions from heterogeneous multi-site datasets.

method Group-Robust MDPs with d-rectangular uncertainty sets, feature-wise worst-case aggregation, and cluster-level pooling.

result Proves suboptimality bound for robust planning policy under robust partial coverage assumption.

Sequential Decisions Multi-Site Learning Robust MDPs Group-Robust Heterogeneous Data

2026-02-02abs ↗pdf ↗

Ranking & Reweighting Improves Group Distributional Robustnesscs.LG

This paper improves model robustness to underrepresented groups using ranking metrics and reweighting.

problem Underrepresented groups suffer from low accuracy in models trained via ERM.

method Proposes Discounted Cumulative Gain (DCG) and Discounted Rank Upweighting (DRU) methods.

result Models trained with DRU show superior generalization to unseen groups.

group robustness ranking metrics reweighting DCG DRU

2023-05-09abs ↗pdf ↗

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-playcs.LG

ISP improves DNN uncertainty for better subgroup accuracy.

problem Improving accuracy-group robustness in deep neural networks.

method Introspective Self-play (ISP) adds an introspection task to improve bias-awareness and uncertainty.

result ISP improves the accuracy-group robustness trade-off of AL methods.

deep learning uncertainty estimation active learning bias-awareness deep neural networks

2023-02-11abs ↗pdf ↗

Importance Tempering: Group Robustness for Overparameterized Modelscs.LG

Improves overparameterized models' robustness to distribution shifts.

problem Accuracy drop on testing distributions different from training.

method Importance tempering to improve decision boundaries.

result State-of-the-art results on worst group classification tasks.

overparameterized models distribution shifts robustness temperature selection label shift

2022-09-19abs ↗pdf ↗

Robust Optimization for Fairness with Noisy Protected Groupscs.LG

New methods ensure fairness in noisy protected groups.

problem Noisy or biased protected group information complicates fairness audits.

method Robust optimization techniques to enforce fairness on true groups.

result Robust approaches achieve better true group fairness guarantees.

fairness protected groups robust optimization noisy data machine learning

2020-02-21abs ↗pdf ↗

Simple and Fast Group Robustness by Automatic Feature Reweightingcs.LG

AFR simplifies reducing reliance on spurious features, improving model performance.

problem Reducing reliance on spurious features for out-of-distribution generalization.

method Automatic Feature Reweighting (AFR) updates the model with a weighted loss.

result AFR improves model performance on benchmarks with minimal compute.

out-of-distribution spurious features feature reweighting generalization classification benchmarks

2023-06-19abs ↗pdf ↗

Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learningstat.ML

New method improves transfer and robustness of supervised contrastive learning.

problem Class collapse in supervised contrastive learning leads to poor representation quality.

method Adding a weighted class-conditional InfoNCE loss and a class-conditional autoencoder.

result Improves transfer and robustness on 5 standard datasets and 3 worst-group robustness datasets.

supervised contrastive learning transferability robustness class collapse permutation invariance

2022-04-15abs ↗pdf ↗

Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsitystat.ML

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past the common approach has been to use various "group sparsity-inducing" norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also po…

2016-06-19abs ↗pdf ↗