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.
I introduce a new geometrical approach to thermo--statistical mechanics. Here I highlight the main physical ideas, and how do they translate into geometrical language. I contrast the present approach with previous thermo--statistical--geometrical formalisms, (pseudo-)Riemannian [Weinhold 1975; Ruppeiner 1979] as well a…
A neural network approach unifies Lasso for variable selection.
Flexible approach for normal approximations in geometric and topological statistics.
We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimistic boost to the value estimate at each state-action pair and select actions that are greedy with respect to the resulting optimistic value f…
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
Modern technologies are generating ever-increasing amounts of data. Making use of these data requires methods that are both statistically sound and computationally efficient. Typically, the statistical and computational aspects are treated separately. In this paper, we propose an approach to entangle these two aspects …
Unified approach to private statistics from empirical to population data.
The paper provides a statistical decision-theoretical derivation of the Two-Stage approach for parameter estimation.
New approach improves AI's handling of incomplete data.
Machine learning and statistical modeling complement each other in healthcare analytics.
Adaptive data fusion boosts efficiency in multi-task optimization.
The paper proves statistical consistency and fairness guarantees for a plug-in algorithm.
Bayesian approach controls FDR in high-dimensional models.
New approach for distributed learning of Gaussian mixtures.
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood appro…
Multiplicative noise models are often used instead of additive noise models in cases in which the noise variance depends on the state. Furthermore, when Poisson distributions with relatively small counts are approximated with normal distributions, multiplicative noise approximations are straightforward to implement. Th…
This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applications to American option pricing and the Canadian lynx data are given to illustrate this approach.
The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to…
We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variation…
Statistical learning theory provides bounds of the generalization gap, using in particular the Vapnik-Chervonenkis dimension and the Rademacher complexity. An alternative approach, mainly studied in the statistical physics literature, is the study of generalization in simple synthetic-data models. Here we discuss the c…
Novel framework for ML-assisted inference valid for any statistical task.
Develops a deep learning approach for statistical arbitrage.
Paper introduces data-dependent SSP for private linear and logistic regression.
New DP framework using data truncation for efficient estimation.
New ML method detects incomplete bid-rigging cartels.
Enhances U-statistics for semi-supervised datasets using unlabeled data.
Chentsov's theorem characterizes the Fisher information metric on statistical models as essentially the only Riemannian metric that is invariant under sufficient statistics. This implies that each statistical model is naturally equipped with a geometry, so Chentsov's theorem explains why many statistical properties can…
Statistical query algorithms and low-degree tests are nearly equivalent in high-dimensional hypothesis testing.
New method improves statistical inference using machine learning-imputed data.
Active inference framework improves -statistic estimation efficiency.
Researchers explore statistical perspectives to understand GNN generalization.
New method reconstructs data subsets from limited published statistics.
Research aims to bridge statistical learning to causal models in AI.
Boosting improves data fitting while maintaining fairness guarantees.
Improved likelihood-free inference by localizing and refining low-dimensional approximations.
The scalability of statistical estimators is of increasing importance in modern applications. One approach to implementing scalable algorithms is to compress data into a low dimensional latent space using dimension reduction methods. In this paper we develop an approach for dimension reduction that exploits the assumpt…
Paper tackles complex risk in deep neural networks.
MegazordNet combines stats and ML for better financial time series forecasting.
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of k-generalized statistics, is derived that is particularly suitable to describe the whole spectrum of incomes, from the low-middle income region up…
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored. In this paper, we compare four fundamental st…
Paper introduces statistical learning for point processes.
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the t…
New method uses quantum annealing and VAN for better statistical mechanics calculations.
Flexible framework for deep distributional regression models.
New statistical theory explains contrastive learning effectiveness.
Optimal learning via moderate deviations theory improves statistical accuracy.
We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to non-uniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat…