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.
CAggNet improves medical image segmentation by fusing coarse and fine features.
New method tests Granger non-causality in panel data with cross-sectional dependencies.
AAggFF improves federated learning fairness through sequential decision making.
Masked LARk prevents cross-site tracking while training models.
This paper examines pricing and hedging strategies for cross-currency equity protection swaps.
This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The measure relies on the decomposition of systemic risk into sub-components …
Paper develops a new estimator for high-dimensional panel data with common shocks.
Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split). We provide the first theoretical guarantees on Agghoo, ensuring that it can be used safely: Agghoo performs at worst like the hold-out when the risk is convex. The same holds true…
Aggregates predictions from multiple regression models using random projections and kernel methods.
FedBuff improves federated learning scalability with asynchronous updates.
Paper proposes BSP to find stable bimodules of cross-correlated features.
We study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992-2003 period, comparing them with those of the aggregate-trade network, known in the literature as the international-trade network (ITN). We show that link-weight distributions of commodity-s…
Cross-GCN models cross features in GCN for better performance.
A method for fast, accurate cross-temporal forecasts using machine learning.
Proposes C2RM to mine cross-cryptocurrency relationships for better Bitcoin price prediction.
Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for…
CDA framework infers channel influence from aggregated data without user identifiers.
This paper proposes an empirical test of financial contagion in European equity markets during the tumultuous period of 2008-2011. Our analysis shows that traditional GARCH and Gaussian stochastic-volatility models are unable to explain two key stylized features of global markets during presumptive contagion periods: s…
Probabilistic pseudo knots model uncertain knot diagrams.
Existing graph convolutional networks focus on the neighborhood aggregation scheme. When applied to semi-supervised learning, they often suffer from the overfitting problem as the networks are trained with the cross-entropy loss on a small potion of labeled data. In this paper, we propose an unsupervised manifold smoot…
Study identifies contagion in aggregated defaults despite environmental changes.
Improved financial network predictability using LLM for edge filtering.
Study on costs of manipulating AMM-based price oracles.
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
Recently, Convolution Neural Networks (CNNs) obtained huge success in numerous vision tasks. In particular, DenseNets have demonstrated that feature reuse via dense skip connections can effectively alleviate the difficulty of training very deep networks and that reusing features generated by the initial layers in all s…
We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 mi…
In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related can…
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
We propose a new approach for constructing synthetic pseudo-panel data from cross-sectional data. The pseudo panel and the preferences it intends to describe is constructed at the individual level and is not affected by aggregation bias across cohorts. This is accomplished by creating a high-dimensional probabilistic m…
Paper proposes a uniqueness Shapley measure to compare variable importance.
Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new model that learns coupled representations of domains, intents, and slots by taking advantage of their hierarchical dependency in a Spoken Lang…
Unified framework improves cross-corpus EEG emotion recognition by aligning prototypes and refining decision boundaries.
Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data. While the traditional description of conformal prediction starts with a nonconformity score, we provide an alternate (but equivalent) view…
This paper optimizes retraining models using their own predictions and noisy labels.
Statistical arbitrageurs have inelastic demand, contrary to classical models.
DUPLE tackles cross-deployment recognition in fiber-optic perimeter security with meta-learning.
In general, a self-attention mechanism has been applied for speaker embedding encoding. Previous studies focused on training the self-attention in a high-level layer, such as the last pooling layer. However, the effect of low-level features was reduced in the speaker embedding encoding. Therefore, we propose masked cro…
Random forests perform bootstrap-aggregation by sampling the training samples with replacement. This enables the evaluation of out-of-bag error which serves as a internal cross-validation mechanism. Our motivation lies in using the unsampled training samples to improve each decision tree in the ensemble. We study the e…
In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estim…
In finance, one usually deals not with prices but with growth rates , defined as the difference in logarithm between two consecutive prices. Here we consider not the trading volume, but rather the volume growth rate , the difference in logarithm between two consecutive values of trading volume. To this end…
Paper proposes a novel method to assess treatment effect estimators using cross-validation.
New methods estimate point-wise dependency from neural MI models.
We study cross-country GDP losses due to financial crises in terms of frequency (number of loss events per period) and severity (loss per occurrence). We perform the Loss Distribution Approach (LDA) to estimate a multi-country aggregate GDP loss probability density function and the percentiles associated to extreme eve…
Multiple query criteria active learning (MQCAL) methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection. A central issue related to MQCAL methods concerns the development of an integration criteria strategy (ICS) that makes full …
This paper reformulates for better model performance and interpretation.
News novelty predicts negative stock market returns.
Proposes a new optimization-based method for aggregating sets in neural networks.
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their statu…