Data Excellence for AI: Why Should You Care

• URL: http://arxiv.org/abs/2111.10391v1
• Date: Fri, 19 Nov 2021 19:06:03 GMT
• Title: Data Excellence for AI: Why Should You Care
• Authors: Lora Aroyo, Matthew Lease, Praveen Paritosh, Mike Schaekermann
• Abstract summary: Benchmark datasets define the entire world within which models exist and operate. If "data is the new oil," we are still missing work on the refineries by which the data itself could be optimized for more effective use.
• Score: 9.421161233914251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficacy of machine learning (ML) models depends on both algorithms and data. Training data defines what we want our models to learn, and testing data provides the means by which their empirical progress is measured. Benchmark datasets define the entire world within which models exist and operate, yet research continues to focus on critiquing and improving the algorithmic aspect of the models rather than critiquing and improving the data with which our models operate. If "data is the new oil," we are still missing work on the refineries by which the data itself could be optimized for more effective use.

Related papers

• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter [7.886307329450978]
  Dyna-style algorithms combine two approaches by using simulated data from an estimated environmental model to accelerate model-free training. Previous works address this issue by using model ensembles or pretraining the estimated model with data collected from the real environment. We introduce an out-of-distribution data filter that removes simulated data from the estimated model that significantly diverges from data collected in the real environment.
  arXiv  Detail & Related papers  (2024-10-16T01:49:03Z)
• Certain and Approximately Certain Models for Statistical Learning [4.318959672085627]
  We show that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We build efficient algorithms with theoretical guarantees to check this necessity and return accurate models in cases where imputation is unnecessary.
  arXiv  Detail & Related papers  (2024-02-27T22:49:33Z)
• Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes [72.75421975804132]
  Learning Active Learning (LAL) suggests to learn the active learning strategy itself, allowing it to adapt to the given setting. We propose a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem. Our approach is based on learning from a myopic oracle, which gives our model the ability to adapt to non-standard objectives.
  arXiv  Detail & Related papers  (2023-09-11T14:16:37Z)
• RLBoost: Boosting Supervised Models using Deep Reinforcement Learning [0.0]
  We present RLBoost, an algorithm that uses deep reinforcement learning strategies to evaluate a particular dataset and obtain a model capable of estimating the quality of any new data. The results of the article show that this model obtains better and more stable results than other state-of-the-art algorithms such as LOO, DataShapley or DVRL.
  arXiv  Detail & Related papers  (2023-05-23T14:38:33Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning [92.89846887298852]
  Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data. Give access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
  arXiv  Detail & Related papers  (2022-10-11T10:20:31Z)
• Fix your Models by Fixing your Datasets [0.6058427379240697]
  Current machine learning tools lack streamlined processes for improving the data quality. We introduce a systematic framework for finding noisy or mislabelled samples in the dataset. We demonstrate the efficacy of our framework on public as well as private enterprise datasets of two Fortune 500 companies.
  arXiv  Detail & Related papers  (2021-12-15T02:41:50Z)
• Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis [17.811597734603144]
  We propose an approach to automatically generating counterfactual data for data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance.
  arXiv  Detail & Related papers  (2021-06-29T10:27:01Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.