Evaluation of Active Feature Acquisition Methods for Static Feature Settings

• URL: http://arxiv.org/abs/2312.03619v2
• Date: Thu, 7 Dec 2023 18:45:10 GMT
• Title: Evaluation of Active Feature Acquisition Methods for Static Feature Settings
• Authors: Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
• Abstract summary: We introduce a semi-offline reinforcement learning framework for active feature acquisition performance evaluation (AFAPE) Here, we study and extend the AFAPE problem to cover static feature settings, where features are time-invariant. We derive and adapt new inverse probability weighting (IPW), direct method (DM), and double reinforcement learning (DRL) estimators within the semi-offline RL framework.
• Score: 6.645033437894859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active feature acquisition (AFA) agents, crucial in domains like healthcare where acquiring features is often costly or harmful, determine the optimal set of features for a subsequent classification task. As deploying an AFA agent introduces a shift in missingness distribution, it's vital to assess its expected performance at deployment using retrospective data. In a companion paper, we introduce a semi-offline reinforcement learning (RL) framework for active feature acquisition performance evaluation (AFAPE) where features are assumed to be time-dependent. Here, we study and extend the AFAPE problem to cover static feature settings, where features are time-invariant, and hence provide more flexibility to the AFA agents in deciding the order of the acquisitions. In this static feature setting, we derive and adapt new inverse probability weighting (IPW), direct method (DM), and double reinforcement learning (DRL) estimators within the semi-offline RL framework. These estimators can be applied when the missingness in the retrospective dataset follows a missing-at-random (MAR) pattern. They also can be applied to missing-not-at-random (MNAR) patterns in conjunction with appropriate existing missing data techniques. We illustrate the improved data efficiency offered by the semi-offline RL estimators in synthetic and real-world data experiments under synthetic MAR and MNAR missingness.

Related papers

• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings [6.082810456767599]
  Machine learning methods often assume that input features are available at no cost. In domains like healthcare, where acquiring features could be expensive harmful, it is necessary to balance a features acquisition against its predictive positivity. We present a problem of active feature acquisition performance evaluation (AFAPE)
  arXiv  Detail & Related papers  (2023-12-03T23:08:29Z)
• Augmenting Unsupervised Reinforcement Learning with Self-Reference [63.68018737038331]
  Humans possess the ability to draw on past experiences explicitly when learning new tasks. We propose the Self-Reference (SR) approach, an add-on module explicitly designed to leverage historical information. Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark.
  arXiv  Detail & Related papers  (2023-11-16T09:07:34Z)
• TRIAGE: Characterizing and auditing training data for improved regression [80.11415390605215]
  We introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings.
  arXiv  Detail & Related papers  (2023-10-29T10:31:59Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data [28.445166861907495]
  We develop theory for the TMIS Offline Policy Evaluation (OPE) estimator. We derive high-probability, instance-dependent bounds on its estimation error. We also recover minimax-optimal offline learning in the adaptive setting.
  arXiv  Detail & Related papers  (2023-06-24T21:48:28Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Robust Disentanglement of a Few Factors at a Time [5.156484100374058]
  We introduce population-based training (PBT) for improving consistency in training variational autoencoders (VAEs) We then use Unsupervised Disentanglement Ranking (UDR) as an unsupervised to score models in our PBT-VAE training and show how models trained this way tend to consistently disentangle only a subset of the generative factors. We show striking improvement in state-of-the-art unsupervised disentanglement performance and robustness across multiple datasets and metrics.
  arXiv  Detail & Related papers  (2020-10-26T12:34:23Z)
• Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring [25.32973996508579]
  Non-Intrusive Load Monitoring (NILM) is a field of research focused on segregating constituent electrical loads in a system based only on their aggregated signal. We quantify the degree of surprise between the predictive distribution (termed postdictive surprise) and the transitional probabilities (termed transitional surprise) This work provides clear evidence that a point of diminishing returns of model performance with respect to dataset size exists.
  arXiv  Detail & Related papers  (2020-09-16T15:39:08Z)

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.