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.
Paper proposes a method to estimate true positive proportion without knowing it.
Paper improves deep learning for instance-level classification from label proportions.
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known. The task is to learn a model to predict the class labels of the individual instances. LLP has broad applications in political science, market…
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
New learning rules achieve optimal sample complexity for weakly supervised classification.
learn2mix trains neural nets faster by adjusting class proportions dynamically.
We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or SVM, which explicitly models the latent unknown instance labels together with the known group …
A reinsurance contract should address the conflicting interests of the insurer and reinsurer. Most of existing optimal reinsurance contracts only considers the interests of one party. This article combines the proportional and stop-loss reinsurance contracts and introduces a new reinsurance contract called proportional…
Study examines how insurance affects households prone to proportional losses, especially those near poverty.
Paper proposes a new method for estimating mixture proportions without irreducibility assumption.
Paper improves signal proportion estimation by accounting for variable dependence.
RLSbench benchmarks domain adaptation under label proportion shifts, revealing widespread failures and proposing a two-step meta-algorithm.
Proportional transaction costs present difficult theoretical problems in trading algorithm design, on account of their lack of analytical tractability. The author derives a solution of DT-NT-DT form for an arbitrary model in which the the traded asset has diffusive dynamics described by one or more stochastic risk fact…
The paper explores learning from label proportions, showing differences in efficiency between LLP and PAC learning.
Framework simplifies vision-based control and goal discovery.
New framework for weakly supervised learning from label proportions.
Introduces MPR to measure and optimize representation across intersectional groups in retrieval.
Training on mixed distributions improves test performance even when components are unrelated.
Estimates proportions of LLM-generated text in mixed documents.
Stability result for a popular algorithm in optimal transport.
Proposes a proportional masking strategy for better tabular data imputation.
We show that the lack of arbitrage in a model with both fixed and proportional transaction costs is equivalent to the existence of a family of absolutely continuous single-step probability measures, together with an adapted process with values between the bid-ask spreads that satisfies the martingale property with resp…
DSDE improves OoD detection by estimating model library proportions.
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …
Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showe…
Paper finds closed-form solutions for tontine with bequest motive.
Score-based methods fail with isolated components and incorrect mixing proportions.
The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic abilit…
Unified formula for arbitrary liquidity operations in weighted AMMs
The theory of optimal trading under proportional transaction costs has been considered from a variety of perspectives. In this paper, we show that all the results can be interpreted using a universal law, illustrating the results in trading algorithm design.
Federated Cox model handles non-proportional hazards in siloed data.
PRESTO improves rare event prediction by shrinking towards proportional odds model.
We study the problem of separating a mixture of distributions, all of which come from interventions on a known causal bayesian network. Given oracle access to marginals of all distributions resulting from interventions on the network, and estimates of marginals from the mixture distribution, we want to recover the mixi…
We propose a learning algorithm capable of learning from label proportions instead of direct data labels. In this scenario, our data are arranged into various bags of a certain size, and only the proportions of each label within a given bag are known. This is a common situation in cases where per-data labeling is lengt…
In the Cayley graph of the mapping class group of a closed surface, with respect to any generating set, we look at a ball of large radius centered on the identity vertex, and at the proportion among the vertices in this ball representing pseudo-Anosov elements. A well-known conjecture states that this proportion should…
We determine the optimal amount to invest in a Black-Scholes financial market for an individual who consumes at a rate equal to a constant proportion of her wealth and who wishes to minimize the expected time that her wealth spends in drawdown during her lifetime. Drawdown occurs when wealth is less than some fixed pro…
Suppose the Riemannian metrics and on a closed connected manifold are geodesically equivalent and strictly non-proportional at least at one point. Then the topological entropy of the geodesic flow of vanishes.
Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfe…
Transaction costs appear in financial markets in more than one form. There are several results in the literature on small proportional transaction cost and not that many on fixed transaction cost. In the present work, we heuristically study the effect of both types of transaction cost by focusing on a portfolio optimiz…
We introduce a new pension product that offers retirees the opportunity for a lifelong income and a bequest for their estate. Based on a tontine mechanism, the product divides pension savings between a tontine account and a bequest account. The tontine account is given up to a tontine pool upon death while the bequest …
Duality for robust hedging with proportional transaction costs of path dependent European options is obtained in a discrete time financial market with one risky asset. Investor's portfolio consists of a dynamically traded stock and a static position in vanilla options which can be exercised at maturity. Both the stock …
New approach predicts generalization of deep neural networks in proportional-width regime.
Estimates watermarked content proportions in mixed-source texts.
Aioli unifies language model data mixing methods and improves performance.
Paper connects Plackett-Luce and Cox models for preference estimation.
Derives TAP approximation for Bayesian linear regression.
Study on estimating Gumbel--Max watermark proportions in edited documents.
This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set…