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.
Proposes indifference pricing to estimate weak information value.
Study shows financial value of weak information converges in discrete vs continuous markets.
Improves label propagation for weakly supervised learning.
Tensor completion requires fewer samples with weak side information.
ProbKT uses probabilistic logical reasoning to train object detection models with weak supervision.
Enhances weak lensing inference with neural summaries.
Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a 2D convolutional neural network to simulated noiseless lensi…
Introduces weak -Dirac structures in geometric settings.
We investigated publicly reported security breaches of internal controls in corporate systems to determine whether SOX assessments are information bearing with respect to breaches which can lead to materially significant losses and misstatements. SOX Section 404 adverse decisions on effectiveness of controls occurred i…
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian ap…
Study shows how information loss and operation loss are related in feature representations.
Paper introduces methods for more reliable probabilistic predictions with confidence intervals.
Paper proposes an algorithm to recover full supervision from weakly labeled data.
Weak diffusion priors can still perform well in inverse problems.
Unified framework for policy learning using weak supervision.
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propose an unbiased estimate of the loss using a randomized prediction, allowing the model to update its weak learners with limited information. …
Market efficiency at least requires the absence of weak arbitrage opportunities, but this is not sufficient to establish a situation where the market is sensitive, i.e., where it "fully reflects" or "rapidly adjusts to" some information flow including the evolution of asset prices. By contrast, No Weak Arbitrage togeth…
New theory explains how strong models can learn from weak ones.
An algorithm learns from multiple models to match an oracle's risk.
Develops weak PINNs for efficient manifold solutions of hyperbolic equations.
A new method for averaging probability distributions based on optimal weak mass transport.
Active WeaSuL uses active learning to improve weak supervision for better model performance.
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our re…
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We foc…
Nemo improves WS learning pipeline by 20%.
BERT model improves cross-lingual document retrieval.
The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatori…
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution…
We study the effect of the quality and quantity of side information on the recovery of a hidden community of size in a graph of size . Side information for each node in the graph is modeled by a random vector with the following features: either the dimension of the vector is allowed to vary with , while …
Study shows reverberant phase is not essential for weakly-supervised dereverberation.
A quantitative check of weak efficiency in US dollar/German mark exchange rates is developed using high frequency data. We show the existence of long term return anomalies. We introduce a technique to measure the available information and show it can be profitable following a particular trading rule.
New method uses weak labels to create valid confidence sets for predictions.
Paper introduces HRPCFD for efficient training of stochastic processes.
Decomposing market impact into diffusive components
Deep convolutional neural networks (CNNs) based approaches are the state-of-the-art in various computer vision tasks, including face recognition. Considerable research effort is currently being directed towards further improving deep CNNs by focusing on more powerful model architectures and better learning techniques. …
Learning new tasks with few samples using related task evaluations.
The equivalence (or weak equivalence) classes of orientation-preserving free actions of a finite group G on an orientable 3-dimensional handlebody of genus g can be enumerated in terms of sets of generators of G. They correspond to the equivalence classes of generating n-vectors of elements of G, where n=1+(g-1)/|G|, u…
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We propose a novel surrogate loss function and unbiased estimator, allowing weak learners to update themselves with limited information. Using…
Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using weak or noisy signals like the output of heuristic methods or user click-through data for training. In a semi-supervised setting, we can use a…
The study establishes stability in WMOT, crucial for finance with imprecise data.
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals. Two types of domain knowledge are commonly available in scientific applications: 1. feature sparsity (fraction of features deemed relevant)…
Improves data labeling efficiency in machine learning.
Improved PINNs for solving PDEs with unknown measurement noise.
Study sets limits for detecting a subhypergraph in uniform hypergraphs.
In this paper are presented methods of impact analysis on informatics system security accidents, qualitative and quantitative methods, starting with risk and informational system security definitions. It is presented the relationship between the risks of exploiting vulnerabilities of security system, security level of …
Model explains how stablecoin runs are influenced by large sales and reserve quality.
We discuss general notions of metrics and of Finsler structures which we call weak metrics and weak Finsler structures. Any convex domain carries a canonical weak Finsler structure, which we call its tautological weak Finsler structure. We compute distances in the tautological weak Finsler structure of a domain and we …
Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoo…