大规模数据中基于鲁棒二分类和并行参数估计的排序

Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment

Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets . However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In…

Bernoulli Embeddings for Graphs

Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tas…

Fairness Through Regularization for Learning to Rank

Given the abundance of applications of ranking in recent years, addressing fairness concerns around automated ranking systems becomes necessary for increasing the trust among end-users. Previous work on fair ranking has mostly focused on application-specific fairness notions, often tailored to online advertising, and it rarely considers learning as part of the process. In this work, we show how to transfer numerous fairness notions from binary classification to a learning to rank setting. Our formalism allows us to design methods for incorporating fairness objectives with provable generalizati…

ROC曲线下面积在二分类任务中具有最一致的评估性能

Area under the ROC Curve has the Most Consistent Evaluation for Binary Classification

本研究通过分析156个数据场景和18种评估指标，发现ROC曲线下面积（AUC）因考虑所有决策阈值，在二分类模型评估和排名中具有最小方差，是最一致的评估指标。

MLPSVM:A new parallel support vector machine to multi-label learning

Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision h…

Constrained Classification and Ranking via Quantiles

In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the $F_β$ score, area under the precision-recall curve, Precision at K, and more. The maximization of many of these metrics can be expressed as a constrained optimization problem, where the constraint is a function of the classifier's predictions. In this paper we propose a novel framework for learning with constraints that can be expressed as a predicted positive rate (or negative rate) on a subset of the training data. We e…

排序算法的渐近性

The asymptotics of ranking algorithms

我们考虑的预测问题的监督排名，其中的任务是排名集的候选项返回响应查询。虽然存在统计程序，保证在这种情况下的一致性，这些程序要求个人提供所有项目的完整排名，这在实践中很少可行。相反，个人经常提供部分偏好信息，如项目的成对比较，更实用的排名方法旨在直接建模这种部分偏好数据。然而，正如我们所展示的，这种方法提出了严重的理论挑战。事实上，我们证明，许多常用的替代损失成对比较数据不产生一致性，令人惊讶的是，我们显示不一致，即使在低噪声设置。这些负面的结果作为动机，我们提出了一种新的方法，监督排名的基础上聚集的部分偏好，我们开发了$U$ -基于经验的风险最小化程序。我们提出了一个渐近分析，这些新的程序，表明它们产生的一致性结果，平行的分类。我们补充我们的理论结果与实验研究的新程序在一个大规模的网络排名任务。

有序集学习及其在协作排名中的应用

Learning From Ordered Sets and Applications in Collaborative Ranking

当用户在多组项目之间进行选择时，会出现对集合的排名。例如，一个组可以是那些被认为是他们的$5$明星的电影，或者是定制的旅游套餐。事实证明，为了正确地建模这种数据类型，我们需要研究划分集合和排序子集的一般组合问题。在这里，我们构建了一个概率对数线性模型在一组有序子集。在这个组合空间中的推理是非常具有挑战性的：当$N$接近无穷大时，空间大小接近$(N!/2)6.93145^{N+1}$。我们提出了一个\texttt{split-and-merge} Metropolis-Hastings过程，可以有效地探索状态空间。为了发现数据中隐藏的方面，我们用潜在的二进制变量丰富模型，以便可以有效地评估后验。最后，我们评估了所提出的模型对大规模的协同过滤任务，并证明了它是有竞争力的国家的最先进的方法。

Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two indepen…

Ranking Data with Continuous Labels through Oriented Recursive Partitions

We formulate a supervised learning problem, referred to as continuous ranking, where a continuous real-valued label Y is assigned to an observable r.v. X taking its values in a feature space $\mathcal{X}$ and the goal is to order all possible observations x in $\mathcal{X}$ by means of a scoring function $s:\mathcal{X}\rightarrow \mathbb{R}$ so that s(X) and Y tend to increase or decrease together with highest probability. This problem generalizes bi/multi-partite ranking to a certain extent and the task of finding optimal scoring functions s(x) can be naturally cast as optimization of a dedic…

基于总指数的因子重要性排序与选择

Factor Importance Ranking and Selection using Total Indices

本文提出FIRST方法，利用总Sobol'指数实现与算法无关的因子重要性排序与选择，无需模型拟合，并通过模拟实验验证其在回归和分类任务上的优势。