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 explores two methods for optimal portfolio selection in financial markets.
New algorithm solves online resource allocation problems efficiently.
A new strategy selects k in k-NN regression without hold-out data.
Bayesian principles improve neural additive models for better feature selection and uncertainty.
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The lasso and other variable selection techniques have been successfully used in the SVM to perform automatic variable selec…
Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for clas…
A new principle for optimizer selection improves training speed and performance.
PASTIS selects minimal models from stochastic dynamics data.
Eliashberg simplifies singularities in geometry.
A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statist…
ERM uses energy-based selection to improve recursive reasoning.
New criterion selects optimal number of clusters based on stability.
Model selection is crucial to high-dimensional learning and inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality. Yet both …
A novel feature selection method using noise-based hypothesis testing improves feature selection accuracy.
Bayesian methods detect clusters in noisy data more reliably.
This work improves policy evaluation and selection using logarithmic smoothing for pessimistic off-policy estimation.
Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implicitly that the model is correctly specified or of fixed dimensions. Yet model misspecification and high dimensionality are common in real app…
During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results from the standpoint of standard measures in…
Dynamic abstention improves LLM accuracy by selectively terminating unpromising reasoning.
New model improves portfolio selection by analyzing tensor data.
In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As…
Unified algorithm for efficient pure exploration using dual variables.
We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the penalty function is high-dimensional. In this situation, the luckiness-normalized-maximum-likelihood(LNML)-minimization approach is favorab…
Study optimizes investment strategies in markets with contagious price jumps.
Framework selects optimal historical data windows for non-stationary learning.
The paper solves portfolio selection for complex preferences in continuous time.
Both the human brain and artificial learning agents operating in real-world or comparably complex environments are faced with the challenge of online model selection. In principle this challenge can be overcome: hierarchical Bayesian inference provides a principled method for model selection and it converges on the sam…
New architectures improve KANs, making them more interpretable and accurate.
Bayesian approach optimizes in-context learning for state space models.
MaxEnt framework recovers standard model selection procedures and identifies the most generalizable model.
Variationality of conformal geodesics fails in higher dimensions.
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-optimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a funct…
New criteria improve imputation model selection using MOO.
Paper proposes a new uncertainty measure for active learning in neural networks.
We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polyno…
Truncated Singular Value Decomposition (SVD) calculates the closest rank- approximation of a given input matrix. Selecting the appropriate rank defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization prob…
Directed graphical models provide a useful framework for modeling causal or directional relationships for multivariate data. Prior work has largely focused on identifiability and search algorithms for directed acyclic graphical (DAG) models. In many applications, feedback naturally arises and directed graphical models …
Unified method for learning from selectively labeled data.
AXE evaluates explanations to avoid misleading Rashomon set model selection.
A principled approach to understand network structures is to formulate generative models. Given a collection of models, however, an outstanding key task is to determine which one provides a more accurate description of the network at hand, discounting statistical fluctuations. This problem can be approached using two p…
Efficient Bayesian variable selection for binomial and negative binomial data.
Study reveals Data Shapley's inconsistent performance in data selection tasks.
ACS is an interactive framework for model-free selection with guaranteed error control.
The algebra of transactions as fundamental measurements is constructed on the basis of the analysis of their properties and represents an expansion of the Boolean algebra. The notion of the generalized economic measurements of the economic quantity and quality of objects of transactions is introduced. It has been shown…
Typical dimensionality reduction (DR) methods are often data-oriented, focusing on directly reducing the number of random variables (features) while retaining the maximal variations in the high-dimensional data. In unsupervised situations, one of the main limitations of these methods lies in their dependency on the sca…
Bayesian method tackles variable selection in high-dimensional data.
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel), which are determined by a model selection criterion. The functions to be compared do not just differ in their parametrization but in thei…
The paper establishes principles for initializing and designing GNNs with ReLU activations to avoid oversmoothing and correlation collapse.