Learning an ordering of items based on noisy pairwise comparisons is useful
when item-specific labels are difficult to assign, for example, when annotators
have to make subjective assessments. Algorithms have been proposed for actively
sampling comparisons of items to minimize the number of annotations necessary
for learning an accurate ordering. However, many ignore shared structure
between items, treating them as unrelated, limiting sample efficiency and
precluding generalization to new items. In this work, we study active learning
with pairwise preference feedback for ordering items with contextual
attributes, both in- and out-of-sample. We give an upper bound on the expected
ordering error incurred by active learning strategies under a logistic
preference model, in terms of the aleatoric and epistemic uncertainty in
comparisons, and propose two algorithms designed to greedily minimize this
bound. We evaluate these algorithms in two realistic image ordering tasks,
including one with comparisons made by human annotators, and demonstrate
superior sample efficiency compared to non-contextual ranking approaches and
active preference learning baselines.

学习基于有噪声的配对比较的项目顺序，在无法分配具体标签的情况下很有用，尤其是在标注者必须进行主观评估的情况下。本研究在具有上下文属性的有序项目中研究了配对偏好反馈的主动学习，提出了两种算法来贪婪地最小化排序错误，证明了这些算法相对于非上下文排名方法和主动偏好学习基线具有更好的样本效率。

主动偏好学习用于样本内外排序项目

Active Preference Learning for Ordering Items In- and Out-of-sample

Conditional graph generation tasks involve training a model to generate a
graph given a set of input conditions. Many previous studies employ
autoregressive models to incrementally generate graph components such as nodes
and edges. However, as graphs typically lack a natural ordering among their
components, converting a graph into a sequence of tokens is not
straightforward. While prior works mostly rely on conventional heuristics or
graph traversal methods like breadth-first search (BFS) or depth-first search
(DFS) to convert graphs to sequences, the impact of ordering on graph
generation has largely been unexplored. This paper contributes to this problem
by: (1) highlighting the crucial role of ordering in autoregressive graph
generation models, (2) proposing a novel theoretical framework that perceives
ordering as a dimensionality reduction problem, thereby facilitating a deeper
understanding of the relationship between orderings and generated graph
accuracy, and (3) introducing "latent sort," a learning-based ordering scheme
to perform dimensionality reduction of graph tokens. Our experimental results
showcase the effectiveness of latent sort across a wide range of graph
generation tasks, encouraging future works to further explore and develop
learning-based ordering schemes for autoregressive graph generation.

本文提出了基于学习的排序方案来解决自回归模型中的图生成和排序问题，并通过实验证明该方案在各种图生成任务上的有效性，鼓舞未来工作进一步探索和发展学习排序方案。

重塑自回归图生成中的排序思路

Let There Be Order: Rethinking Ordering in Autoregressive Graph  Generation

The predominant approach for language modeling is to process sequences from
left to right, but this eliminates a source of information: the order by which
the sequence was generated. One strategy to recover this information is to
decode both the content and ordering of tokens. Existing approaches supervise
content and ordering by designing problem-specific loss functions and
pre-training with an ordering pre-selected. Other recent works use iterative
search to discover problem-specific orderings for training, but suffer from
high time complexity and cannot be efficiently parallelized. We address these
limitations with an unsupervised parallelizable learner that discovers
high-quality generation orders purely from training data -- no domain knowledge
required. The learner contains an encoder network and decoder language model
that perform variational inference with autoregressive orders (represented as
permutation matrices) as latent variables. The corresponding ELBO is not
differentiable, so we develop a practical algorithm for end-to-end optimization
using policy gradients. We implement the encoder as a Transformer with
non-causal attention that outputs permutations in one forward pass.
Permutations then serve as target generation orders for training an
insertion-based Transformer language model. Empirical results in language
modeling tasks demonstrate that our method is context-aware and discovers
orderings that are competitive with or even better than fixed orders.

该研究提出了一种无监督、可并行学习的语言模型生成算法，使用置换矩阵表示自回归顺序，并通过多步生成式注意力将其作为目标顺序进行训练，并在语言建模任务中获得了有竞争力或者优秀的表现。