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.
Wide-AdGraph detects ads and trackers using a graph of resource requests.
The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that …
New method estimates mean from noisy data with few outliers.
New method for estimating sparse means in noisy data.
Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural networks into latent variable models, mitigating limitations of traditional linear factor models. V…
Algorithm estimates covariance from noisy data efficiently.
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap bet…
Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items are scored independently by their similarity to the query in the latent embedding…
New algorithm clusters data with almost-linear time, robust to corruption.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
Graph database outperforms in filtering ESG stocks efficiently.
We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each…
The paper explores conditions for quasi-isometric subgroups and their filtered ends.
In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underp…
Characterizes the sample complexity of list regression tasks.
New algorithm reduces contamination in supervised learning.
Paper tackles high-accuracy list-decodable learning for mean estimation.
This work characterizes when a hypothesis class can be k-list learned.
Product categorization using text data for eCommerce is a very challenging extreme classification problem with several thousands of classes and several millions of products to classify. Even though multi-class text classification is a well studied problem both in academia and industry, most approaches either deal with …
We study symplectic (contact) structures on nilmanifolds that correspond to the filiform Lie algebras - nilpotent Lie algebras of the maximal length of the descending central sequence. We give a complete classification of filiform Lie algebras that possess a basis e_1, ..., e_n, [e_i,e_j]=c_{ij}e_{i{+}j} (N-graded Lie …
Investigates principles of generalization in list learning, refutes sample compression conjecture.
Recommender systems are widely used to predict personalized preferences of goods or services using users' past activities, such as item ratings or purchase histories. If collections of such personal activities were made publicly available, they could be used to personalize a diverse range of services, including targete…
The paper examines the Chinese market reaction to the ADR issue by comparing returns and their stochastic variances of the Chinese firms cross-listed in the U.S. stock market. First, It was implemented capital asset pricing model (CAPM) to determine expected returns A and N shares. The CAPM provided with a methodology …
New method combines score lists using joint CDFs, improving computation.
Study on list learning with noisy data, showing limits and some learnable cases.
New algorithms for privately learning decision lists and halfspaces.
New RL algorithm ensures stable, replicable policies.
Research on predicting with lists of labels, characterizing learnability and providing algorithms.
New proof of a 111-year-old result using gauge theory.
In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of Chinese stock market. This influence network shows distinct topological prope…
Polynomial-time algorithm for list-decodable linear regression with batches.
In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or i…
In the last years efforts in econophysics have been shifted to study how network theory can facilitate understanding of complex financial markets. Main part of these efforts is the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financia…
New algorithm recovers mixture means even with many outliers.
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bou…
New scoring rules compare probabilistic top lists in classification.
In 1955, Berger \cite{Ber} gave a list of irreducible reductive representations which can occur as the holonomy of a torsion-free affine connection. This list was stated to be complete up to possibly a finite number of missing entries. In this paper, we show that there is, in fact, an infinite family of representations…
NeuRules learns interpretable rule lists from data without pre-discretization.
Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such mo…
Choppy optimizes ranked list truncation using Transformer architecture.
Learning the true ordering between objects by aggregating a set of expert opinion rank order lists is an important and ubiquitous problem in many applications ranging from social choice theory to natural language processing and search aggregation. We study the problem of unsupervised rank aggregation where no ground tr…
New algorithm for batch list-decodable linear regression with stronger guarantees.
New Sauer inequality improves multiclass hypothesis class bounds.
We propose a list of open problems in pluripotential theory partially motivated by their applications to complex differential geometry. The list includes both local questions as well as issues related to the compact complex manifold setting.
Statistical Query lower bound shows difficulty in list-decodable linear regression.
Background: Many authors have described MELD as a predictor of short-term mortality in the liver transplantation waiting list. However MELD score accuracy to predict long term mortality has not been statistically evaluated. Objective: The aim of this study is to analyze the MELD score as well as other variables as a pr…
In this paper we give the list of all 7-dimensional nilpotent real Lie algebras that admit a contact structure. Based on this list, we describe all 7-dimensional nilmanifolds that admit an invariant contact structure.
Berge in [1] defined doubly primitive knots, which yield lens spaces by Dehn surgery. At the same paper he listed the knots into several types. In this paper we will prove the list is complete when . The invariant is a quantity with regard to lens space surgery, which is defined in this paper. Furthermore at t…