Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)

• URL: http://arxiv.org/abs/2408.00108v2
• Date: Sat, 3 Aug 2024 08:51:46 GMT
• Title: Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
• Authors: Adam Gould, Guilherme Paulino-Passos, Seema Dadhania, Matthew Williams, Francesca Toni,
• Abstract summary: We introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P) This allows users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We show empirically that our approach outperforms other interpretable machine learning models on a real-world medical dataset.
• Score: 9.5382175632919
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.

Related papers

• Improving Context-Aware Preference Modeling for Language Models [62.32080105403915]
  We consider the two-step preference modeling procedure that first resolves the under-specification by selecting a context, and then evaluates preference with respect to the chosen context. We contribute context-conditioned preference datasets and experiments that investigate the ability of language models to evaluate context-specific preference.
  arXiv  Detail & Related papers  (2024-07-20T16:05:17Z)
• Generative vs. Discriminative modeling under the lens of uncertainty quantification [0.929965561686354]
  In this paper, we undertake a comparative analysis of generative and discriminative approaches. We compare the ability of both approaches to leverage information from various sources in an uncertainty aware inference. We propose a general sampling scheme enabling supervised learning for both approaches, as well as semi-supervised learning when compatible with the considered modeling approach.
  arXiv  Detail & Related papers  (2024-06-13T14:32:43Z)
• When factorization meets argumentation: towards argumentative explanations [0.0]
  We propose a novel model that combines factorization-based methods with argumentation frameworks (AFs) Our framework seamlessly incorporates side information, such as user contexts, leading to more accurate predictions.
  arXiv  Detail & Related papers  (2024-05-13T19:16:28Z)
• Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood [1.8798171797988192]
  In supervised learning, automatically assessing the quality of the labels before any learning takes place remains an open research question. In this paper we show how similar procedures can be followed when the underlying model is a product of Local Maximum Likelihood Estimation. This different view allows for wider applicability of the tests by offering users access to a richer model class.
  arXiv  Detail & Related papers  (2023-12-15T22:14:58Z)
• Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation [50.93536377097659]
  This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors.
  arXiv  Detail & Related papers  (2023-04-17T00:10:56Z)
• Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation [24.65301562548798]
  We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation. We conduct an empirical analysis to benchmark the surrogate model selection metrics introduced in the literature, as well as the novel ones introduced in this work.
  arXiv  Detail & Related papers  (2022-11-03T16:26:06Z)
• Instance-Based Neural Dependency Parsing [56.63500180843504]
  We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set.
  arXiv  Detail & Related papers  (2021-09-28T05:30:52Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• Causal Collaborative Filtering [50.22155187512759]
  Causal Collaborative Filtering is a framework for modeling causality in collaborative filtering and recommendation. We show that many traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We propose a conditional intervention approach for $do$-operations so that we can estimate the user-item causal preference.
  arXiv  Detail & Related papers  (2021-02-03T04:16:11Z)
• Achieving Equalized Odds by Resampling Sensitive Attributes [13.114114427206678]
  We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This differentiable functional is used as a penalty driving the model parameters towards equalized odds. We develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature.
  arXiv  Detail & Related papers  (2020-06-08T00:18:34Z)

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.