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.
D-REX learns reward functions from ranked demonstrations to beat the demonstrator's performance.
Ranking recommendation algorithms across datasets using Bradley-Terry model
New algorithm improves asset ranking for better cross-sectional portfolios.
GANs improve missing data imputation for ranking tasks.
New methods rank players using covariates and comparisons, outperforming existing algorithms.
TensorGuide improves LoRA efficiency and expressivity through joint tensor-train optimization.
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may ha…
We present an algorithm, AROFAC2, which detects the (CP-)rank of a degree 3 tensor and calculates its factorization into rank-one components. We provide generative conditions for the algorithm to work and demonstrate on both synthetic and real world data that AROFAC2 is a potentially outperforming alternative to the go…
This paper protects rankings from differential privacy breaches.
The paper establishes theoretical foundations for low-rank knowledge distillation in LLMs.
We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks by the average value and generalise Spearman's ρto extreme ranks. Our main …
We consider the link prediction problem in a partially observed network, where the objective is to make predictions in the unobserved portion of the network. Many existing methods reduce link prediction to binary classification problem. However, the dominance of absent links in real world networks makes misclassificati…
Rank-one measurements limit feasible sets for low-rank PSD matrices.
Develops new oracle inequalities for Gaussian ranking estimators.
This paper compares rank aggregation methods for partial label ranking.
Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random de…
Algorithm ensures fair ranking by minority groups alongside majority groups.
The Frank-Wolfe (FW) algorithm has been widely used in solving nuclear norm constrained problems, since it does not require projections. However, FW often yields high rank intermediate iterates, which can be very expensive in time and space costs for large problems. To address this issue, we propose a rank-drop method …
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…
Aims to learn optimal behavior from ranked experts in MDPs.
Proposes a cross entropy loss for better ranking algorithms.
StealthRank subtly boosts LLM rankings without detectable anomalies.
Efficiently reduces tensor ranks using mean-field approximation.
The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our…
A composite loss framework is proposed for low-rank modeling of data consisting of interesting and common values, such as excess zeros or missing values. The methodology is motivated by the generalized low-rank framework and the hurdle method which is commonly used to analyze zero-inflated counts. The model is demonstr…
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regularization techniques are used within classical algorithms for low-rank approximation in order to exploit the possible sp…
Develops methods to estimate high rank tensors from noisy data.
New method unifies and formalizes data partitioning using a single vector.
Proposes a model for identifying edges in low-rank dynamical networks.
Sparse PCA is a widely used technique for high-dimensional data analysis. In this paper, we propose a new method called low-rank principal eigenmatrix analysis. Different from sparse PCA, the dominant eigenvectors are allowed to be dense but are assumed to have a low-rank structure when matricized appropriately. Such a…
New estimator GMIPS reduces variance in ranking policy evaluation.
RAMPART ranks top-k features more accurately than existing methods.
A faster algorithm for ranking from pairwise comparisons.
Matrix factorization is a well-studied task in machine learning for compactly representing large, noisy data. In our approach, instead of using the traditional concept of matrix rank, we define a new notion of link-rank based on a non-linear link function used within factorization. In particular, by applying the round …
Improved machine learning with reduced tensor rank constraints and dropout.
Improved stability for matrix recovery from rank-one measurements.
IRMAE learns compact latent spaces by minimizing rank.
Paper extends ranking metrics theory for financial positions.
DRSVM uses deep learning to rank relative attributes between image pairs.
Paper extends ranking metrics theory for financial positions.
Improved deep learning performance in financial markets by using rank space.
Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it…
FedLoRU improves FL efficiency by using low-rank updates.
The paper critiques the ambiguity of rank-based evaluation methods for entity alignment and link prediction.
In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first establish that a class of special rank minimization problems has closed-form solutions. Using this result, we then propose penalty decomposition methods for general rank minimization pro…
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instan…
New method solves nonsmooth low-rank matrix optimization problems efficiently.
Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…