arXiv research

Novel upper bound for unsupervised domain adaptation considers joint error.

Bayesian framework improves robustness in nonlinear regression models.

Estimates multiple linear systems on a graph with smoothness constraints.

Project aims to improve time series prediction intervals.

Develops a framework to quantify uncertainties in multiple ML models.

Paper proposes IABF to improve NECST robustness.

The paper tackles joint learning of linear systems, improving accuracy with pooled data.

Proposes IPT for modeling complex joint distributions.

We consider assets for which price $X_t$ and squared volatility $Y_t$ are jointly driven by Heston joint stochastic differential equations (SDEs). When the parameters of these SDEs are estimated from $N$ sub-sampled data $(X_{nT}, Y_{nT})$, estimation errors do impact the classical option pricing PDEs. We estimate thes…

This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.

We consider the problem of approximate joint triangularization of a set of noisy jointly diagonalizable real matrices. Approximate joint triangularizers are commonly used in the estimation of the joint eigenstructure of a set of matrices, with applications in signal processing, linear algebra, and tensor decomposition.…

Mutually interacting components form complex systems and the outputs of these components are usually long-range cross-correlated. Using wavelet leaders, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call joint multifractal analysis based on wavel…

Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs. Given a set of graphs, the jo…

A new method predicts causal relationships without joint data.

Estimates changes in parameters from sparse binomial observations.

Optimizes high-dimensional portfolios using joint shrinkage.

A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint mod…

Paper presents J-RFDL for robust DL in compressed space, improving data representation robustness and accuracy.

Sharp error bounds derived for bidirectional GANs without restrictive assumptions.

Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.

JANET improves time series prediction with adaptive uncertainty regions.

This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.

We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical m…

The paper develops a framework for abstracting causal models using category theory.

Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structu…

FreST Loss decorrelates spatio-temporal dependencies in graph signals.

This paper improves speech recognition by distilling knowledge from acoustic models.

Estimates parameters of interconnected linear systems using total variation penalization.

In math.GT/0002110 the author's Theorems 1.1 and 1.2, combined, implied that iterated torus knots are transversally simple. This result is in error and this erratum pin points the error. In "An addendum on iterated torus knots" a more subtle result is proven resulting in giving a geometric realization of the Honda-Etny…

Proposes a regularization method for unsupervised domain adaptation that aligns predictions with target data's top singular vectors.

We present a new method for high-dimensional linear regression when a scale parameter of the additive errors is unknown. The proposed estimator is based on a penalized Huber $M$-estimator, for which theoretical results on estimation error have recently been proposed in high-dimensional statistics literature. However, t…

A new FFT method for Heston model option pricing with explicit error bounds.

Sharp bounds on uniform generalization errors in binary linear classification.

An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph. Nevertheless, though state-of-the-art graph-based methods have been successful for many learning tasks, they do not consider time-evolving sig…

Due to the ability of deep neural nets to learn rich representations, recent advances in unsupervised domain adaptation have focused on learning domain-invariant features that achieve a small error on the source domain. The hope is that the learnt representation, together with the hypothesis learnt from the source doma…

This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.

Improved Q-learning for multi-agent reinforcement learning by weighting joint action values.

Proposes a Structural Matrix Autoregressive model for joint analysis of asset returns, realized volatility, and trading volume.

Paper proposes a method to maintain ASR performance on new tasks without forgetting old ones.

Any two compact, complete, one-dimensional geodesic spaces with identical marked length spectrum have isometric $π_1$-hull. The present version contains errors, notably in Lemmas 2.2 and 2.3 (path cancellations can be more complicated), which then propagate through the paper. The main result is correct as stated, and a…

DSL estimates heterogeneous treatment effects over time in survival settings.

The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.

New algorithms for private generalized linear contextual bandits.

Calibrated probabilistic solvers improve accuracy of ODE estimates.

Study tests adequacy of FARIMA models with uncorrelated but non-independent errors.

A new method avoids quantile crossing in time series forecasting.

While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement f…