arXiv research

Random forests handle categorical predictors natively but overlook 'absent levels' can bias models.

SBIC learns patterns in imbalanced datasets using empirical similarity and synthetic data.

Researchers show how to secretly train models with hidden data, detect usage with high confidence.

High-dimensional spectroscopy data makes ML models achieve near-perfect accuracy, even when chemical distinctions are absent.

In arXiv:0905.2622v1 and arXiv:0910.4785v1, Bray and Khuri outlined an approach to prove the Penrose inequality for general initial data sets of the Einstein equations. In this paper we extend this approach so that it may be applied to a charged version of the Penrose inequality. Moreover, assuming that the initial dat…

Study bounds noise level in linear regression with dependent data.

An extension of the Heath--Jarrow--Morton model for the development of instantaneous forward interest rates with deterministic coefficients and Gaussian as well as Lévy field noise terms is given. In the special case where the Lévy field is absent, one recovers a model discussed by D.P.~Kennedy.

Model learns alignments between high-dimensional data without supervision.

We generalize the Arbitrage Pricing Theory (APT) to include the contribution of virtual arbitrage opportunities. We model the arbitrage return by a stochastic process. The latter is incorporated in the APT framework to calculate the correction to the APT due to the virtual arbitrage opportunities. The resulting relatio…

Detect hidden confounding in observational data using multiple environments.

Paper improves deep learning models using contrastive predictive coding for semi-supervised learning.

Model reconstructs missing EKG data more accurately than baseline.

New method improves OSSL by learning from all unlabeled data.

We give geometric explanations and proofs of various mirror symmetry conjectures for $T^{n}$-invariant Calabi-Yau manifolds when instanton corrections are absent. This uses fiberwise Fourier transformation together with base Legendre transformation. We discuss mirror transformations of (i) moduli spaces of complex stru…

Deep learning enhances art market valuation by incorporating visual data.

Bayesian method infers ages of 700M Twitter users from follow data.

A framework for document classification using keywords and unlabeled data.

New method for estimating class proportions in open-set label shift data.

Developed a diffusion model on spherical data, addressing geometric and stochastic challenges.

Newtonian, Lagrangian, and Hamiltonian dynamical systems are well formalized mathematically. They give rise to geometric structures describing motion of a point in smooth manifolds. Riemannian metric is a different geometric structure formalizing concepts of length and angle. The interplay of Riemannian metric and its …

Paper tackles missing data shifts in domains, showing how to adapt models.

A new model family of zero-inflated Gaussian processes improves prediction and interpretability of rare event data.

Algorithm learns multiple LDS models from unlabeled short trajectories.

BCF models estimate causal effects on multiple outcomes in TIMSS data.

Study shows sample complexity for logistic regression with normal covariates.

FedForest adapts RF for federated learning, improving performance and efficiency.

New approach improves domain adaptation by relaxing distribution alignment constraints.

Paper proves regularity and existence of Riemannian splines.

Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…

Pairwise discriminators stabilize adversarial training by ensuring the generator's alignment is preserved.

Model learns code representations from comments for data analysis tasks.

Improved neural keyphrase generation by beam search with reward functions.

SynC generates synthetic population from aggregated data using Gaussian copula.

Paper discovers sub-interval relationships in time series data.

Paper models non-linear dynamics from time series data.

Combining self-training and contrastive learning improves performance under distribution shift.

New method improves neural network robustness without sacrificing generalization.

We discuss the local and global problems for the equivalence of geometric structures of an arbitrary order and, in later sections, attention is given to what really matters, namely the equivalence with respect to transformations belonging to a given pseudo-group of transformations. We first give attention to general pr…

Analysis of ridge regression under concept shift reveals nontrivial effects on generalization performance.

Retail investors set interest rates for P2P loans based on borrower characteristics.

Global well-posedness and asymptotic convergence for vacuum Einstein's equations proved.

Methodology to measure lag relevance in time series models.

We investigate statistical properties of daily international market indices of seven countries, and high-frequency $S&P500$ and KOSDAQ data, by using the detrended fluctuation method and the surrogate test. We have found that the returns of international stock market indices of seven countries follow a universal power-…

Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior probability of the enti…

Derives metrics for DeFi vaults, addressing credit risk.

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the obs…

Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading …

In this paper we propose a new robust algorithm to find the optimal static replicating portfolios for general nonlinear payoff functions and give the estimate of the rate of convergence that is absent in the literature. We choose the static replication by minimizing the error bound between the nonlinear payoff function…