arXiv research

Research characterizes learnability of multilabel ranking problems.

Proposes a cross entropy loss for better ranking algorithms.

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 …

Improved CNN for HCCR with new loss function and ranking method.

Paper develops methods for non-quadratic loss low-rank matrix recovery.

Framework learns sentence order from paragraphs using attention and transformer networks.

This paper evaluates various loss functions for Transformer models in stock ranking.

A new method for forming learning objectives using the sum of ranked range.

HERA improves PLL by integrating heterogeneous loss and sparse-low-rank regularization.

This work analyzes two methods for combining multiple binary labels in bipartite ranking.

This paper improves multi-label ranking by reweighting univariate losses, enhancing consistency and performance.

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…

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…

New algorithm predicts ranked stock lists for long-short portfolios.

Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.

In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational da…

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. …

Given a set $V$ of $n$ objects, an online ranking system outputs at each time step a full ranking of the set, observes a feedback of some form and suffers a loss. We study the setting in which the (adversarial) feedback is an element in $V$, and the loss is the position (0th, 1st, 2nd...) of the item in the outputted r…

Unified framework for binary responses using AUC loss and low-rank constraint.

Introduces SoRR for aggregating losses in supervised learning.

A new multi-label classification model combining SVM and BR with low-rank learning.

Paper tackles low-rank matrix recovery with KL property and DC reformulation.

Unified framework for non-Euclidean CPD under scalable stochastic mirror descent.

TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.

Unified algorithm for tensor decomposition supports multiple loss functions and models.

A new model for supervised learning to rank using gradient estimation.

New method proves asymptotic normality for matrix sensing problems.

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…

In this paper, we introduce a novel and robust approach to Quantized Matrix Completion (QMC). First, we propose a rank minimization problem with constraints induced by quantization bounds. Next, we form an unconstrained optimization problem by regularizing the rank function with Huber loss. Huber loss is leveraged to c…

Study on deep matrix factorization with Bures-Wasserstein loss, focusing on critical points and convergence.

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 …

In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric techniq…

Data compression speeds up machine learning loss calculations.

The paper explores how the depth of neural networks affects their ability to represent data accurately.

Online boosting for multilabel ranking with limited feedback.

The paper introduces a method for multi-label classification that allows partial predictions.

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Av…

Deep ReLU networks with extra parameters have mostly good loss landscapes.

FedLoRU improves FL efficiency by using low-rank updates.

Locus scores predictions for risk, reducing large-loss events.

Algorithm learns a better sketch matrix for low-rank approximations.

A ranking network improves neural architecture search efficiency.

A framework for ranking with abstention, offering theoretical guarantees and practical effectiveness.

StochasticRank optimizes ranking metrics efficiently and guarantees global convergence.

TA-VAAL improves active learning by better utilizing task structures and overall data distribution.

A new concordance loss improves model performance and reliability in survival prediction.

Paper proposes a method to improve deep neural networks' confidence estimates.

Algorithm POLO learns low-rank MDPs with adversarial changes in full-info feedback.