arXiv research

Paper introduces GAMs for interpretable learning-to-rank models.

CRS model improves ranking data modeling with theoretical guarantees.

Hierarchical framework for model evaluation on leaderboards

Ranking recommendation algorithms across datasets using Bradley-Terry model

Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict. Datasets often exhibit multimodality, intransitivity, or incomplete rankings---particularly when generated by humans---yet popular probabilistic models are often too rigid to capture such complexities. In…

The paper tackles learning true rankings from noisy, incomplete data.

We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image recognition to achieve both effectiveness and efficiency. We propose a KD technique for learning to rank problems, called \emph{ranking dist…

This paper protects rankings from differential privacy breaches.

Develops methods to estimate high rank tensors from noisy data.

Ranking models are typically designed to provide rankings that optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed rankings, from fueling the spread of misinformation and increasing polariz…

RCPO uses ranked choice modeling for better LLM alignment.

Uplift modeling has effectively been used in fields such as marketing and customer retention, to target those customers that are most likely to respond due to the campaign or treatment. Uplift models produce uplift scores which are then used to essentially create a ranking. We instead investigate to learn to rank direc…

Low-rank framework for task-specific LLM ranking from sparse comparisons.

This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.

Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algori…

Paper identifies tensor ranks via prior predictive matching, solving system of equations.

We consider the problem of noisy matrix completion, in which the goal is to reconstruct a structured matrix whose entries are partially observed in noise. Standard approaches to this underdetermined inverse problem are based on assuming that the underlying matrix has low rank, or is well-approximated by a low rank matr…

New ranking models for time series data using GARCH-type approach.

Algorithm ensures fair ranking by minority groups alongside majority groups.

Unified model combines scores and rankings for grant panel review.

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

Bayesian model improves image completion accuracy by automatically learning low rank structure.

Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of da…

The paper addresses calibration in label ranking, a structured prediction task.

Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related subjects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute…

The paper addresses privacy in rank aggregation using randomized responses.

New estimator GMIPS reduces variance in ranking policy evaluation.

Proposes a method for explaining ranking decisions in learning systems.

The paper proposes methods to identify and sample from mixtures of Mallows models for top-k rankings.

Develops a method to infer partial rankings from sparse comparisons.

Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why the low rank structure ap…

We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives. In contrast to most existing methods, we explicitly model the process of turning a full ranking into an incomplete on…

Low-rank approximation is an effective model compression technique to not only reduce parameter storage requirements, but to also reduce computations. For convolutional neural networks (CNNs), however, well-known low-rank approximation methods, such as Tucker or CP decomposition, result in degraded model accuracy becau…

Researchers show mixtures of ranking models are generally identifiable.

Dropping a tiny fraction of preferences can significantly alter the rankings of top LLMs.

Proposes a method to infer ranking properties and top-K rankings with uncertainty quantification.

We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG …

New algorithm improves deep learning models' robustness without sacrificing accuracy.

Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.

GeLoRA optimizes LoRA fine-tuning by dynamically adjusting ranks based on intrinsic dimensionality.

The paper establishes theoretical foundations for low-rank knowledge distillation in LLMs.

New statistical models for predicting ranked preferences from partial orders.

Develops a statistical framework to measure uncertainty in model rankings based on human preferences.

Protocol for constructing tailored evaluation datasets for semantic models.

A low-rank tensor model simplifies multi-dimensional Markov chains.

Efficiently reduces rank of non-negative matrices with quadratic time complexity.

Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distri…

This paper examines how skip connections prevent rank collapse in sequence models.