A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Framework learns sentence order from paragraphs using attention and transformer networks.
The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. …
This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.
Unified framework for binary responses using AUC loss and low-rank constraint.
A new model for supervised learning to rank using gradient estimation.
Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they …
We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new rank estimator and an efficient batch training algorithm. The approach allows more accurate item rank approximation and explicit utilization of…
Sampling a fraction of pairs can match full evaluation in machine learning losses.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
Ranking is a key aspect of many applications, such as information retrieval, question answering, ad placement and recommender systems. Learning to rank has the goal of estimating a ranking model automatically from training data. In practical settings, the task often reduces to estimating a rank functional of an object …
Efficient online learning with pairwise loss functions is a crucial component in building large-scale learning system that maximizes the area under the Receiver Operator Characteristic (ROC) curve. In this paper we investigate the generalization performance of online learning algorithms with pairwise loss functions. We…
Paper studies SGD stability and optimization error in pairwise learning.
We consider the problem of rank loss minimization in the setting of multilabel classification, which is usually tackled by means of convex surrogate losses defined on pairs of labels. Very recently, this approach was put into question by a negative result showing that commonly used pairwise surrogate losses, such as ex…
Advances in collaborative filtering and ranking methods.
Develops algorithms for personalized ranking in recommender and energy systems.
HERA improves PLL by integrating heterogeneous loss and sparse-low-rank regularization.
Proposes a robust learning strategy for RS over implicit feedback.
In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher compl…
Pairwise learning usually refers to a learning task which involves a loss function depending on pairs of examples, among which most notable ones include ranking, metric learning and AUC maximization. In this paper, we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained …
This paper evaluates various loss functions for Transformer models in stock ranking.
DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints bet…
Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to improve prediction performance, the similarities between latent variables can be exploited by pairwise learning using the graph regularized mat…
Develops a statistical framework to measure uncertainty in model rankings based on human preferences.
This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of objects can be identified by standard sorting methods using pairwise comparisons. We are interested in natural situations in which relationships among the o…
We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all i…
New model reduces bias in crowdsourced pairwise comparisons.
Cross-entropy loss linked to metric learning, outperforming complex pairwise losses.
Pairwise fairness for ranking and regression models.
A ranking network improves neural architecture search efficiency.
Develops new oracle inequalities for Gaussian ranking estimators.
In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss. We show the minimization problem involves dependent random variables and provide a theoretical analysis by proving the consistency of the em…
A faster algorithm for ranking from pairwise comparisons.
DirectRanker neural net outperforms state-of-the-art learning to rank methods.
We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder t…
Algorithm recovers rankings and synchronizes networks from noisy pairwise measurements.
Rank regression from pairwise comparisons requires many comparisons to accurately learn model parameters.
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural netw…
GNNRank uses neural networks to learn global rankings from competition match data.
The paper explores new rules for analyzing label rankings and pairwise preferences.
Optimal privacy-preserving ranking from noisy comparisons.
Exact pairwise ranking is achievable but not possible under noisy comparisons.
The paper introduces metrics and a regularization method to improve fairness in recommendation rankings.
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list. Existing methods either seek a smooth convex surrogate for a non-smooth ranking metric or directly modify updating procedures to encourage top accuracy. In this work we point out that these methods…
We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it. We propose and characterize a very broad class of preference matrices giving rise to the Feature Low Rank (FLR) model, which subsumes several models ranging from the classic Bradl…
The paper addresses privacy in rank aggregation using randomized responses.
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classification. The algorithm shows a very competitive performance on standard benchmark datasets against other representative algorithms in the litera…
The paper designs tests for comparing ranked preference data and finds significant differences.
RaFM improves FM performance with variable-rank embeddings.