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.
A new criterion selects models in overparameterized settings.
A new tradeoff between regularization and sharpness improves model performance in overparameterized settings.
IIC provides a PAC-Bayes bound for interpolating models, revealing factors affecting generalization.
The study evaluates different parameter selection methods for Gaussian process interpolation.
GNNs outperform NNs in interpolating bandlimited functions on Euclidean cubes.
In this study the Voronoi interpolation is used to interpolate a set of points drawn from a topological space with higher homology groups on its filtration. The technique is based on Voronoi tessellation, which induces a natural dual map to the Delaunay triangulation. Advantage is taken from this fact calculating the p…
Unified framework explains why overfitting is benign in interpolating learning.
We introduce a new criterion to determine the order of an autoregressive model fitted to time series data. It has the benefits of the two well-known model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bay…
A new GP interpolation method for better predictive distributions in ranges of interest.
New criterion improves predictive evaluation in weighted inference scenarios.
A new method for early stopping in neural networks without validation sets.
A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian information criterion. Adjustment of…
The paper optimizes interpolation schedules in generative models to improve sampling accuracy.
A contamination in a 3-manifold is an object interpolating between the contact structure and the lamination. Contaminations seem to provide a link between 3-dimensional contact geometry and the classical topology of 3-manifolds, as described in a separate paper. In this paper we deal with contaminations carried by bran…
Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.
SIC detects elbows in error curves automatically.
New principle controls graph-informed adversarial discrepancies.
Study shows the corrected Akaike criterion is inadmissible for estimating Kullback-Leibler discrepancy.
Complete criterion for VoI in multi-decision influence diagrams established.
In the information-based paradigm of inference, model selection is performed by selecting the candidate model with the best estimated predictive performance. The success of this approach depends on the accuracy of the estimate of the predictive complexity. In the large-sample-size limit of a regular model, the predicti…
LS improves model selection for singular statistical models.
SplitWise enhances stepwise regression by adaptively encoding numeric predictors into binary features.
In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy …
Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce …
In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method can generate high-quality interpolations between two given data samples. Specifically, we use an autoencoder network to map data samples into latent space and perform interpolation via an int…
A new criterion HBIC improves model selection for factor analysis with missing data.
Hybrid framework merges data and domain knowledge for better spatial interpolation.
New approach uses interpolation models and error bounds for verifiable scientific machine learning.
A semi-supervised framework using stochastic interpolation and latent representations.
Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…
This paper introduces Kernel-based Information Criterion (KIC) for model selection in regression analysis. The novel kernel-based complexity measure in KIC efficiently computes the interdependency between parameters of the model using a variable-wise variance and yields selection of better, more robust regressors. Expe…
Paper finds formulas for mutual information and MMSE in matrix tensor product problems.
The paper derives an equation linking WAIC and WBIC for singular models.
Two methods for interpolating manifold-valued data are presented.
In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolatio…
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natura…
Study finds exact limits for sparse regression with fewer observations than usual.
We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the…
When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjust…
Gradient descent converges linearly for neural networks with specific conditions.
Study geometric structures in transfer learning to avoid negative transfer.
Proposes CLSM for better subsequence generation in music sequences.
We study tick-by-tick financial returns belonging to the FTSE MIB index of the Italian Stock Exchange (Borsa Italiana). We can confirm previously detected non-stationarities. However, scaling properties reported in the previous literature for other high-frequency financial data are only approximately valid. As a conseq…
Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is a crucial step, which remains an open problem in the research community. Despite a large amount of investigations, most current implementat…
We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently. For …
Paper improves feature selection accuracy using transfer learning.
In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples. In this context, works have proposed to introduce Graph Neural Networks (GNNs) aiming at exploiting the information contained in other samples treated concurrently, what is commonly referre…
Neural Bayes methods simplify fitting complex bivariate extremal models.