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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5 results for “datamodels”

Datamodels: Predicting Predictions from Training Datastat.ML

Datamodels predict model outcomes from training data subsets.

problem Understanding model behavior from training data.

method Conceptual framework for analyzing model behavior using datamodels.

result Simple linear datamodels can accurately predict model outputs.

datamodels model behavior training data deep neural networks counterfactuals

2022-02-01abs ↗pdf ↗

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selectioncs.LG

D3M debiases models by selectively removing problematic examples.

problem Model failures on underrepresented subgroups.

method Isolates and removes specific training examples that cause failures.

result Efficiently trains debiased classifiers with minimal example removal.

machine learning model debiasing subgroup robustness data selection example removal

2024-06-24abs ↗pdf ↗

ModelDiff: A Framework for Comparing Learning Algorithmscs.LG

ModelDiff compares learning algorithms by identifying feature transformations.

problem Comparing models trained with different learning algorithms.

method ModelDiff uses datamodels framework to find distinguishing feature transformations.

result ModelDiff can compare models trained with/without data augmentation, pre-training, and different SGD hyperparameters.

algorithm comparison feature transformations learning algorithms data augmentation pre-training

2022-11-22abs ↗pdf ↗

DsDm: Model-Aware Dataset Selection with Datamodelscs.LG

DsDm selects data to improve model performance, avoiding handpicked notions of quality.

problem Selecting data for model training can lead to worse performance than random selection.

method Formulates dataset selection as an optimization problem, maximizing model performance.

result Selected datasets improve language model performance by 2x over baseline methods.

dataset selection model performance learning algorithms data quality language models

2024-01-23abs ↗pdf ↗

Most Influential Subset Selection: Challenges, Promises, and Beyondcs.LG

MISS identifies influential subsets in ML models.

problem Capturing collective influence of training samples.

method Analyzed and compared influence-based greedy heuristics and adaptive versions.

result Adaptive heuristics can better capture sample interactions.

Machine Learning Subset Selection Influence Function Greedy Algorithms Adaptivity

2024-09-25abs ↗pdf ↗