Parallel Belief Contraction via Order Aggregation

• URL: http://arxiv.org/abs/2501.13295v1
• Date: Thu, 23 Jan 2025 00:42:16 GMT
• Title: Parallel Belief Contraction via Order Aggregation
• Authors: Jake Chandler, Richard Booth,
• Abstract summary: We consider how to extend serial contraction operations that obey stronger properties.<n>We also consider the iterated case: the behaviour of beliefs after a sequence of parallel contractions.<n>We propose a general method for extending serial iterated belief change operators to handle parallel change.
• Score: 1.474723404975345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The standard ``serial'' (aka ``singleton'') model of belief contraction models the manner in which an agent's corpus of beliefs responds to the removal of a single item of information. One salient extension of this model introduces the idea of ``parallel'' (aka ``package'' or ``multiple'') change, in which an entire set of items of information are simultaneously removed. Existing research on the latter has largely focussed on single-step parallel contraction: understanding the behaviour of beliefs after a single parallel contraction. It has also focussed on generalisations to the parallel case of serial contraction operations whose characteristic properties are extremely weak. Here we consider how to extend serial contraction operations that obey stronger properties. Potentially more importantly, we also consider the iterated case: the behaviour of beliefs after a sequence of parallel contractions. We propose a general method for extending serial iterated belief change operators to handle parallel change based on an n-ary generalisation of Booth & Chandler's TeamQueue binary order aggregators.

Related papers

• Parallel Belief Revision via Order Aggregation [1.474723404975345]
  This paper offers a method for extending serial iterated belief revision operators to handle parallel change.<n>Based on a family of order aggregators known as TeamQueue aggregators, it provides a principled way to recover the independently plausible properties.
  arXiv  Detail & Related papers  (2025-05-20T04:26:01Z)
• A Set-Sequence Model for Time Series [0.0]
  In many financial prediction problems, the behavior of individual units is influenced by observable unit-level factors and macroeconomic variables.<n>We propose a Set-Sequence model that eliminates the need for handcrafted features.<n>Our approach harnesses the set nature of the cross-section and is computationally efficient, generating set summaries in linear time relative to the number of units.
  arXiv  Detail & Related papers  (2025-05-16T13:36:07Z)
• A Theory of Learning with Autoregressive Chain of Thought [46.4004951842894]
  We formalize the learning problems both when the chain-of-thought is observed and when training only on prompt-answer pairs. We present a simple base class that allows for universal representability and computationally tractable chain-of-thought learning.
  arXiv  Detail & Related papers  (2025-03-11T00:21:32Z)
• The Role of Sparsity for Length Generalization in Transformers [58.65997625433689]
  We propose a new theoretical framework to study length generalization for the next-token prediction task. We show that length generalization occurs as long as each predicted token depends on a small (fixed) number of previous tokens. We introduce Predictive Position Coupling, which trains the transformer to predict the position IDs used in a positional coupling approach.
  arXiv  Detail & Related papers  (2025-02-24T03:01:03Z)
• Interaction Asymmetry: A General Principle for Learning Composable Abstractions [27.749478197803256]
  We show that interaction asymmetry enables both disentanglement and compositional generalization. We propose an implementation of these criteria using a flexible Transformer-based VAE, with a novel regularizer on the attention weights of the decoder.
  arXiv  Detail & Related papers  (2024-11-12T13:33:26Z)
• Gumbel Counterfactual Generation From Language Models [64.55296662926919]
  We show that counterfactual reasoning is conceptually distinct from interventions.<n>We propose a framework for generating true string counterfactuals.<n>We show that the approach produces meaningful counterfactuals while at the same time showing that commonly used intervention techniques have considerable undesired side effects.
  arXiv  Detail & Related papers  (2024-11-11T17:57:30Z)
• From Loops to Oops: Fallback Behaviors of Language Models Under Uncertainty [67.81977289444677]
  Large language models (LLMs) often exhibit undesirable behaviors, such as hallucinations and sequence repetitions. We categorize fallback behaviors - sequence repetitions, degenerate text, and hallucinations - and extensively analyze them. Our experiments reveal a clear and consistent ordering of fallback behaviors, across all these axes.
  arXiv  Detail & Related papers  (2024-07-08T16:13:42Z)
• REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning [64.08293076551601]
  We propose a novel method of using a learned measure for identifying positive pairs. Our Retrieval-Based Reconstruction measure measures the similarity between two sequences. We show that the REBAR error is a predictor of mutual class membership.
  arXiv  Detail & Related papers  (2023-11-01T13:44:45Z)
• Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance [31.875925637190328]
  In this paper, we present Banana, a Banach fixed-point network for equivariant segmentation with inter-part equivariance by construction. Our key insight is to iteratively solve a fixed-point problem, where point-part assignment labels and per-part SE(3)-equivariance co-evolve simultaneously. Our formulation naturally provides a strict definition of inter-part equivariance that generalizes to unseen inter-part configurations.
  arXiv  Detail & Related papers  (2023-05-25T17:59:32Z)
• Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects [92.80955339180119]
  mainstream crowd counting methods regress density map and integrate it to obtain counting results. Inspired by this, we propose a rational and anthropoid crowd counting framework.
  arXiv  Detail & Related papers  (2022-12-02T07:00:53Z)
• Extending Momentum Contrast with Cross Similarity Consistency Regularization [5.085461418671174]
  We present Extended Momentum Contrast, a self-supervised representation learning method founded upon the legacy of the momentum-encoder unit proposed in the MoCo family configurations. Under the cross consistency regularization rule, we argue that semantic representations associated with any pair of images (positive or negative) should preserve their cross-similarity. We report a competitive performance on the standard Imagenet-1K linear head classification benchmark.
  arXiv  Detail & Related papers  (2022-06-07T20:06:56Z)
• Approaching Reflex Predictions as a Classification Problem Using Extended Phonological Alignments [0.0]
  This work describes an implementation of the "extended alignment" (or "multitiers") approach for cognate reflex prediction. The technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific traits.
  arXiv  Detail & Related papers  (2022-05-19T14:00:42Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• Relieving Long-tailed Instance Segmentation via Pairwise Class Balance [85.53585498649252]
  Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority samples) against the tailed ones. We propose a novel Pairwise Class Balance (PCB) method, built upon a confusion matrix which is updated during training to accumulate the ongoing prediction preferences.
  arXiv  Detail & Related papers  (2022-01-08T07:48:36Z)
• 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)

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.