arXiv research

Extends PD-NJ-ODE to noisy observations and dependent observation times.

Study optimal policy regret in partially observable Markov games with adaptive opponents.

Improves active learning efficiency by warping input space based on observed outputs.

The analysis of observed conditional distributions of both lagged and simultaneous intraday price increments of a basket of stocks reveals phenomena of dependence - induced volatility smile and kurtosis reduction. A model based on multivariate t-Student distribution shows that the observed effects are caused by colelct…

Consider an experiment involving a potentially small number of subjects. Some random variables are observed on each subject: a high-dimensional one called the "observed" random variable, and a one-dimensional one called the "outcome" random variable. We are interested in the dependencies between the observed random var…

Theoretical analysis of deep neural networks for time series data.

A Hawkes process with state-dependent factor models order flows in limit order books.

We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that severa…

Study on future-dependent value functions for off-policy evaluation in complex environments.

New model captures state-dependent variability in partially observed systems.

New method uncovers zero entropy in dependent observations after finite samples.

Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from…

Paper improves neural ODEs for forecasting non-Markovian processes.

Network models have been popular for modeling and representing complex relationships and dependencies between observed variables. When data comes from a dynamic stochastic process, a single static network model cannot adequately capture transient dependencies, such as, gene regulatory dependencies throughout a developm…

New method infers human sensorimotor costs from behavior.

We revisit the Kolmogorov-Smirnov and Cramér-von Mises goodness-of-fit (GoF) tests and propose a generalisation to identically distributed, but dependent univariate random variables. We show that the dependence leads to a reduction of the "effective" number of independent observations. The generalised GoF tests are not…

Deep learning predicts path-dependent processes from historical data.

Estimates binary labels from dependent data using Markov Random Fields.

We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…

New method uses Winsorized mean estimators for privacy-preserving statistics on dependent data.

New empirical process bounds reveal trade-off between dependence and complexity in nonparametric learning.

Hybrid framework merges data and domain knowledge for better spatial interpolation.

This paper models default data to capture dynamic dependence across sectors.

Algorithm detects unmeasured confounding in observational data.

New findings control FDR for online testing methods under positive dependence.

We apply both distance-based (Jin and Matteson, 2017) and kernel-based (Pfister et al., 2016) mutual dependence measures to independent component analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA, minimizing empirical dependence measures as an objective function in both deflation and parallel m…

Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building.…

We tackle linear bandits with partially observable features, achieving sublinear regret.

Study shows observability from a measurable set for Gevrey functions.

New method identifies causal variables from partially observed data.

Estimates copula density for complex data distributions.

New method models asymmetric data with improved tail dependence.

The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collec…

Many robotic applications require the agent to perform long-horizon tasks in partially observable environments. In such applications, decision making at any step can depend on observations received far in the past. Hence, being able to properly memorize and utilize the long-term history is crucial. In this work, we pro…

New method for off-policy evaluation in POMDPs using future-dependent value functions.

New method for evaluating policies in complex decision-making models with hidden variables.

We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action $i$, the learner receives information about the payoff of every action $j$ in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair $(i,j)$ (and may…

Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.

Study learns linear system dynamics from noisy bilinear data.

Study shows DQN's performance degrades with temporal dependence in data.

A new sampling method balances multi-label datasets by preserving category frequency order.

Estimates dependent parameters using Markovian dependence with shrinkage.

We investigate the prediction capability of the orthogonal greedy algorithm (OGA) in high-dimensional regression models with dependent observations. The rates of convergence of the prediction error of OGA are obtained under a variety of sparsity conditions. To prevent OGA from overfitting, we introduce a high-dimension…

Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.

The `observer space' of a Lorentzian spacetime is the space of future-timelike unit tangent vectors. Using Cartan geometry, we first study the structure a given spacetime induces on its observer space, then use this to define abstract observer space geometries for which no underlying spacetime is assumed. We propose ta…

The edge structure of the graph defining an undirected graphical model describes precisely the structure of dependence between the variables in the graph. In many applications, the dependence structure is unknown and it is desirable to learn it from data, often because it is a preliminary step to be able to ascertain c…

Causal relationships in time series with latent variables are discovered using LPCMCI.

This paper considers an often forgotten relationship, the time delay between a cause and its effect in economies and finance. We treat the case of Foreign Direct Investment (FDI) and economic growth, - measured through a country Gross Domestic Product (GDP). The pertinent data refers to 43 countries, over 1970-2015, - …