Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings

• URL: http://arxiv.org/abs/2312.01530v2
• Date: Thu, 7 Dec 2023 18:47:53 GMT
• Title: Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
• Authors: Henrik von Kleist, Alireza Zamanian, Ilya Shpitser, Narges Ahmidi
• Abstract summary: Machine learning methods often assume input features are available at no cost. In domains like healthcare, where acquiring features could be expensive or harmful, it is necessary to balance a feature's acquisition against its predictive cost. We present a problem of active feature acquisition performance evaluation (AFAPE)
• Score: 6.645033437894859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning methods often assume input features are available at no cost. However, in domains like healthcare, where acquiring features could be expensive or harmful, it is necessary to balance a feature's acquisition cost against its predictive value. The task of training an AI agent to decide which features to acquire is called active feature acquisition (AFA). By deploying an AFA agent, we effectively alter the acquisition strategy and trigger a distribution shift. To safely deploy AFA agents under this distribution shift, we present the problem of active feature acquisition performance evaluation (AFAPE). We examine AFAPE under i) a no direct effect (NDE) assumption, stating that acquisitions don't affect the underlying feature values; and ii) a no unobserved confounding (NUC) assumption, stating that retrospective feature acquisition decisions were only based on observed features. We show that one can apply offline reinforcement learning under the NUC assumption and missing data methods under the NDE assumption. When NUC and NDE hold, we propose a novel semi-offline reinforcement learning framework, which requires a weaker positivity assumption and yields more data-efficient estimators. We introduce three novel estimators: a direct method (DM), an inverse probability weighting (IPW), and a double reinforcement learning (DRL) estimator.

Related papers

• Evaluation of Active Feature Acquisition Methods for Static Feature Settings [6.645033437894859]
  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.
  arXiv  Detail & Related papers  (2023-12-06T17:07:42Z)
• 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)
• Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL [86.0987896274354]
  We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training. For the first time, our theory can reliably decide whether the training will diverge at an early stage.
  arXiv  Detail & Related papers  (2023-10-06T17:57:44Z)
• Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition [16.350351668269415]
  We develop methodology for active feature acquisition (AFA) We show that we can bypass many challenges with a novel, nonparametric oracle based approach.
  arXiv  Detail & Related papers  (2023-02-27T17:02:11Z)
• Differential Assessment of Black-Box AI Agents [29.98710357871698]
  We propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. We leverage sparse observations of the drifted agent's current behavior and knowledge of its initial model to generate an active querying policy. Empirical evaluation shows that our approach is much more efficient than re-learning the agent model from scratch.
  arXiv  Detail & Related papers  (2022-03-24T17:48:58Z)
• Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization [52.7137956951533]
  We argue that devising simpler methods for learning predictors on existing features is a promising direction for future research. We introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift. Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions.
  arXiv  Detail & Related papers  (2022-02-14T16:42:16Z)
• Direct Advantage Estimation [63.52264764099532]
  We show that the expected return may depend on the policy in an undesirable way which could slow down learning. We propose the Direct Advantage Estimation (DAE), a novel method that can model the advantage function and estimate it directly from data. If desired, value functions can also be seamlessly integrated into DAE and be updated in a similar way to Temporal Difference Learning.
  arXiv  Detail & Related papers  (2021-09-13T16:09:31Z)
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning [63.53407136812255]
  Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. Existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly.
  arXiv  Detail & Related papers  (2021-05-17T20:16:46Z)
• Active Feature Acquisition with Generative Surrogate Models [11.655069211977464]
  In this work, we consider models that perform active feature acquisition (AFA) and query the environment for unobserved features. Our work reformulates the Markov decision process (MDP) that underlies the AFA problem as a generative modeling task. We propose learning a generative surrogate model ( GSM) that captures the dependencies among input features to assess potential information gain from acquisitions.
  arXiv  Detail & Related papers  (2020-10-06T02:10:06Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.