Skeptical binary inferences in multi-label problems with sets of probabilities

• URL: http://arxiv.org/abs/2205.00662v1
• Date: Mon, 2 May 2022 05:37:53 GMT
• Title: Skeptical binary inferences in multi-label problems with sets of probabilities
• Authors: Yonatan Carlos Carranza Alarc\'on and S\'ebastien Destercke
• Abstract summary: We consider the problem of making distributionally robust, skeptical inferences for the multi-label problem. By skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider a set of possible probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. Our experimental results are organised in three sections; (1) the first one indicates the gain computational obtained from our theoretical results by using synthetical data sets, (2) the second one indicates that our approaches produce relevant cautiousness on those hard-to-predict instances where its precise counterpart fails, and (3) the last one demonstrates experimentally how our approach copes with imperfect information (generated by a downsampling procedure) better than the partial abstention [31] and the rejection rules.

Related papers

• A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition [6.702661996542768]
  This paper introduces a novel information-theoretic perspective on the relationship between prominent group fairness notions in machine learning. Our main contribution lies in elucidating an exact relationship between these three measures of (un)fairness by leveraging a body of work in information theory called partial information decomposition (PID) In this work, we leverage PID to identify the granular regions where these three measures of (un)fairness overlap and where they disagree with each other leading to potential tradeoffs.
  arXiv  Detail & Related papers  (2024-06-07T00:38:51Z)
• Identifiable Latent Neural Causal Models [82.14087963690561]
  Causal representation learning seeks to uncover latent, high-level causal representations from low-level observed data. We determine the types of distribution shifts that do contribute to the identifiability of causal representations. We translate our findings into a practical algorithm, allowing for the acquisition of reliable latent causal representations.
  arXiv  Detail & Related papers  (2024-03-23T04:13:55Z)
• Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects [1.9253333342733674]
  Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. We propose a unified and model-agnostic inferential approach for a wide class of partially identified estimands.
  arXiv  Detail & Related papers  (2023-10-12T08:17:30Z)
• Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples [21.144077993862652]
  We operate in the unsupervised regime where only a small set of unlabeled test samples along with a labeled training set is available. We experimentally verify that optimizing with our loss formulation outperforms a number of state-of-the-art baselines. We show that our proposed method significantly outperforms them.
  arXiv  Detail & Related papers  (2023-10-11T14:39:51Z)
• Correcting Underrepresentation and Intersectional Bias for Classification [49.1574468325115]
  We consider the problem of learning from data corrupted by underrepresentation bias. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out rates. We show that our algorithm permits efficient learning for model classes of finite VC dimension.
  arXiv  Detail & Related papers  (2023-06-19T18:25:44Z)
• Dealing with Unknown Variances in Best-Arm Identification [11.975934323118752]
  We introduce and analyze two approaches to deal with unknown variances, either by plugging in the empirical variance or by adapting the transportation costs. We illustrate the theoretical and empirical performances of our two sampling rule wrappers on Track-and-Stop and on a Top Two algorithm.
  arXiv  Detail & Related papers  (2022-10-03T14:42:48Z)
• Bounding Counterfactuals under Selection Bias [60.55840896782637]
  We propose a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal.
  arXiv  Detail & Related papers  (2022-07-26T10:33:10Z)
• Multi-label Chaining with Imprecise Probabilities [0.0]
  We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates. The main reasons one could have for using such estimations are (1) to make cautious predictions when a high uncertainty is detected in the chaining and (2) to make better precise predictions by avoiding biases caused in early decisions in the chaining. Our experimental results on missing labels, which investigate how reliable these predictions are in both approaches, indicate that our approaches produce relevant cautiousness on those hard-to-predict instances where the precise models fail.
  arXiv  Detail & Related papers  (2021-07-15T16:43:31Z)
• Nested Counterfactual Identification from Arbitrary Surrogate Experiments [95.48089725859298]
  We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
  arXiv  Detail & Related papers  (2021-07-07T12:51:04Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• Tractable Inference in Credal Sentential Decision Diagrams [116.6516175350871]
  Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. We develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with credal sets of mass functions. For a first empirical validation, we consider a simple application based on noisy seven-segment display images.
  arXiv  Detail & Related papers  (2020-08-19T16:04:34Z)

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.