Target inductive methods for zero-shot regression

• URL: http://arxiv.org/abs/2402.01252v1
• Date: Fri, 2 Feb 2024 09:19:45 GMT
• Title: Target inductive methods for zero-shot regression
• Authors: Miriam Fdez-D\'iaz, Jos\'e Ram\'on Quevedo, Elena Monta\~n\'es
• Abstract summary: This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings) This paper proposes two zero-shot methods for regression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings). Frequently, the surrounding information is not considered in the learning process. This information is known beforehand in the absence of unobserved weather conditions and remains constant for the same station. Considering the surrounding information as side information facilitates the generalization for predicting pollutants in new stations, leading to a zero-shot regression scenario. Available methods in zero-shot typically lean towards classification, and are not easily extensible to regression. This paper proposes two zero-shot methods for regression. The first method is a similarity based approach that learns models from features and aggregates them using side information. However, potential knowledge of the feature models may be lost in the aggregation. The second method overcomes this drawback by replacing the aggregation procedure and learning the correspondence between side information and feature-induced models, instead. Both proposals are compared with a baseline procedure using artificial datasets, UCI repository communities and crime datasets, and the pollutants. Both approaches outperform the baseline method, but the parameter learning approach manifests its superiority over the similarity based method.

Related papers

• Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples [11.727747752958436]
  Deep learning models often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task.<n>Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some empirical assumptions.<n>We propose a data-oriented approach to mitigate the spurious correlation in deep learning models.
  arXiv  Detail & Related papers  (2025-12-28T10:54:51Z)
• The Principles of Diffusion Models [81.12042238390075]
  Diffusion modeling starts by defining a forward process that gradually corrupts data into noise.<n>The goal is to learn a reverse process that transforms noise back into data while recovering the same intermediates.<n>The score-based view, rooted in energy-based modeling, learns the gradient of the evolving data distribution.<n>The flow-based view, related to normalizing flows, treats generation as following a smooth path that moves samples from noise to data.
  arXiv  Detail & Related papers  (2025-10-24T02:29:02Z)
• Kernel Representation and Similarity Measure for Incomplete Data [55.62595187178638]
  Measuring similarity between incomplete data is a fundamental challenge in web mining, recommendation systems, and user behavior analysis.<n>Traditional approaches either discard incomplete data or perform imputation as a preprocessing step, leading to information loss and biased similarity estimates.<n>This paper presents a new similarity measure that directly computes similarity between incomplete data in kernel feature space without explicit imputation in the original space.
  arXiv  Detail & Related papers  (2025-10-15T09:41:23Z)
• Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems [3.082385853653964]
  Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias.<n>This paper proposes a learning algorithm based on likelihood to learn a prediction model.
  arXiv  Detail & Related papers  (2025-06-09T07:50:21Z)
• Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs [25.784316302130875]
  Covariance information is available for free from training data and the curvature of the generative trajectory. We integrate these sources of information using em (i) a novel method to transfer covariance estimates across noise levels. We validate the method on linear inverse problems, where it outperforms recent baselines.
  arXiv  Detail & Related papers  (2024-10-15T00:23:09Z)
• Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information [75.36597470578724]
  Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. We propose gUided Purification (COUP) algorithm, which purifies while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods.
  arXiv  Detail & Related papers  (2024-08-12T02:48:00Z)
• Direct side information learning for zero-shot regression [0.0]
  Zero-shot learning provides models for targets for which instances are not available, commonly called unobserved targets. The availability of target side information becomes crucial in this context in order to properly induce models for these targets. The proposal of this paper is a novel method that jointly takes features and side information in a one-phase learning process.
  arXiv  Detail & Related papers  (2024-02-02T09:36:06Z)
• Data augmentation and explainability for bias discovery and mitigation in deep learning [0.0]
  This dissertation explores the impact of bias in deep neural networks and presents methods for reducing its influence on model performance. The first part begins by categorizing and describing potential sources of bias and errors in data and models, with a particular focus on bias in machine learning pipelines. The next chapter outlines a taxonomy and methods of Explainable AI as a way to justify predictions and control and improve the model.
  arXiv  Detail & Related papers  (2023-08-18T11:02:27Z)
• Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive? [93.10694819127608]
  We propose a unified evaluation pipeline for forecasting methods with real-world perception inputs. Our in-depth study uncovers a substantial performance gap when transitioning from curated to perception-based data.
  arXiv  Detail & Related papers  (2023-06-15T17:03:14Z)
• CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions [2.543865489517869]
  We present a novel approach to locally contrast the prediction of any classifier. Our Contrastive Entropy-based explanation method, CEnt, approximates a model locally by a decision tree to compute entropy information of different feature splits. CEnt is the first non-gradient-based contrastive method generating diverse counterfactuals that do not necessarily exist in the training data while satisfying immutability (ex. race) and semi-immutability (ex. age can only change in an increasing direction)
  arXiv  Detail & Related papers  (2023-01-19T08:23:34Z)
• Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization [73.04187954213471]
  We introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval. The proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline.
  arXiv  Detail & Related papers  (2022-11-14T14:25:40Z)
• Residual Overfit Method of Exploration [78.07532520582313]
  We propose an approximate exploration methodology based on fitting only two point estimates, one tuned and one overfit. The approach drives exploration towards actions where the overfit model exhibits the most overfitting compared to the tuned model. We compare ROME against a set of established contextual bandit methods on three datasets and find it to be one of the best performing.
  arXiv  Detail & Related papers  (2021-10-06T17:05:33Z)
• Calibration of prediction rules for life-time outcomes using prognostic Cox regression survival models and multiple imputations to account for missing predictor data with cross-validatory assessment [0.0]
  Methods are described to combine imputation with predictive calibration in survival modeling subject to censoring. Prediction-averaging appears to have superior statistical properties, especially smaller predictive variation, as opposed to a direct application of Rubin's rules.
  arXiv  Detail & Related papers  (2021-05-04T20:10:12Z)
• Continual Learning for Fake Audio Detection [62.54860236190694]
  This paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. Experiments are conducted on the ASVspoof 2019 dataset.
  arXiv  Detail & Related papers  (2021-04-15T07:57:05Z)
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning [78.83598532168256]
  Marginal-likelihood based model-selection is rarely used in deep learning due to estimation difficulties. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable.
  arXiv  Detail & Related papers  (2021-04-11T09:50:24Z)

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.