Extends PD-NJ-ODE to noisy observations and dependent observation times.
problem Predicting continuous-time stochastic processes with irregular and noisy observations.
method Extends PD-NJ-ODE to handle conditional independence and noisy observations.
result Theoretical guarantees and empirical examples for handling noisy observations and dependent observation times.
Detects dependencies between high-dimensional data and outcomes.
problem Analyzing educational data with high-dimensional student skills.
method n-TARP clustering to quantify and validate dependencies.
result Valid dependencies between student skills and course grades observed.
Study optimal policy regret in partially observable Markov games with adaptive opponents.
problem Optimal sequential decision-making in partially observable environments against strategic, adaptive opponents.
method An epoch-based optimistic maximum-likelihood algorithm that selects one policy per epoch using confidence sets built cumulatively from past data.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) policy regret for fixed problem parameters, with explicit dependence on horizon, adversary memory, confidence radius, and aggregate Eluder dimension. Improves active learning efficiency by warping input space based on observed outputs.
problem Insensitivity of Gaussian process uncertainty to actual observations.
method Input warping with learned monotone reparameterization to adjust acquisition function behavior.
result Significantly improved sample efficiency across various benchmarks, especially in non-stationary conditions.
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…
OGA and HDAIC improve high-dimensional regression with dependent data.
problem High-dimensional linear regression with dependent observations.
method Orthogonal greedy algorithm (OGA) and high-dimensional Akaike's information criterion (HDAIC).
result OGA and HDAIC achieve optimal convergence rate without knowing sparsity.
Theoretical analysis of deep neural networks for time series data.
problem Theoretical development for deep neural networks on temporally dependent observations is lacking.
method Established non-asymptotic bounds for prediction error of deep neural networks under mixing-type assumptions.
result Deep neural networks can model non-linear time series data with additional logarithmic factors due to dependence.
A Hawkes process with state-dependent factor models order flows in limit order books.
problem Modeling order flows in limit order books for better market prediction.
method A Hawkes process with a state-dependent factor for conditional intensity estimation.
result State-dependent formulations improve the fit of LOB models to financial data.
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…
New model captures state-dependent variability in partially observed systems.
problem Structured stochasticity not captured by constant-variance models.
method State-coupled stochastic volatility framework with particle expectation-maximization.
result Model consistently reduces recovery bias under partial observation.
Study on future-dependent value functions for off-policy evaluation in complex environments.
problem Exponential dependence on horizon in off-policy evaluation for complex observations.
method Developed novel coverage assumptions for POMDPs to achieve polynomial bounds.
result Achieved polynomial bounds on previously exponential quantities, improving off-policy evaluation.
New method uncovers zero entropy in dependent observations after finite samples.
problem Understanding uncertainty reduction in dependent observations.
method Minimum list entropy coupling, greedy algorithm.
result Zero entropy achieved with O(log(1/P_min)) samples for dependent observations.
Improved sex and age-specific model for Parkinson's diagnosis.
problem Accurate diagnosis of Parkinson's disease for better patient care.
method Sex-specific and age-dependent handwriting classification model.
result Significantly improved accuracy (83.75% for females, 79.55% for older age group) compared to generalized model.
Paper improves neural ODEs for forecasting non-Markovian processes.
problem Forecasting irregularly observed time series with incomplete data.
method Path-dependent Neural Jump ODEs with signature transform.
result Path-dependent NJ-ODE outperforms original framework in non-Markovian data.
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.
problem Inferring human sensorimotor costs from observed behavior.
method Inverse optimal control with signal-dependent noise.
result Recovering costs and benefits in sensorimotor 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.
problem Predicting path-dependent processes using historical data.
method Nonparametric regression with deep neural networks.
result Deep learning method converges to theoretical predictions as observation frequency increases.
Estimates binary labels from dependent data using Markov Random Fields.
problem Statistical estimation from dependent data across spatial, temporal, and social domains.
method Modeling dependencies as Markov Random Fields and providing efficient estimation algorithms.
result Statistically efficient estimation rates for Ising models from a single sample.
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.
problem Privacy-preserving statistics on dependent data with sensitive information.
method Adapting noisy Winsorized mean estimators to handle dependence via log-Sobolev inequalities.
result Asymptotic and finite sample guarantees for item-level and user-level mean estimation similar to \iid{} settings.
New empirical process bounds reveal trade-off between dependence and complexity in nonparametric learning.
problem Understanding generalization in nonparametric learning with temporal dependencies.
method Developed bounds on expected supremum of empirical processes under β / ρ β/ρ β / ρ -mixing assumptions. result Achieved rates similar to i.i.d. setting under long-range dependence with complex function classes.
Hybrid framework merges data and domain knowledge for better spatial interpolation.
problem Spatial interpolation overlooks domain knowledge and limits to spatial coordinates.
method Integrates data-driven features with rule-assisted spatial dependency function mapping.
result Superior performance in two application scenarios, capturing localized features.
The paper tackles sequential learning with Gaussian payoffs and side observations, providing lower bounds and algorithms.
problem Sequential learning with Gaussian payoffs and side information.
method Non-asymptotic lower bounds and algorithms for minimizing regret.
result Proved non-asymptotic lower bounds and provided algorithms achieving these bounds.
Personalized Influence Estimation helps understand key factors influencing individual observations.
problem Understanding key factors influencing individual observations in various business problems.
method Joint behavior of feature dimensions and relative feature importance.
result Encouraging results justify key reasons for churn in majority of the sample.
This paper models default data to capture dynamic dependence across sectors.
problem Static models fail to explain monthly default dependence.
method Dynamic low-rank state-space model for monthly multi-sector default-count data.
result Effective correlation matrices and copulas are induced from monthly data.
Algorithm detects unmeasured confounding in observational data.
problem Estimating treatment effects in observational studies with untestable conditions.
method Two-stage procedure that detects dependencies between causal mechanisms.
result Algorithm efficiently detects confounding on simulated and semi-synthetic data.
Improves ICA via novel mutual dependence measures.
problem Improving Independent Component Analysis (ICA) for better component independence.
method Combines distance-based and kernel-based mutual dependence measures, introduces Latin hypercube sampling and Bayesian optimization for initialization.
result MDMICA outperforms other methods in terms of mutual independence of estimated components, especially when the ICA model is misspecified.
A simple algorithm predicts well with recent observations and a few summary stats.
problem Predicting future observations from past data with complex dependencies.
method A simple Markov model using recent observations and summary stats.
result A simple algorithm achieves optimal prediction error with minimal memory.
New findings control FDR for online testing methods under positive dependence.
problem Maintaining FDR control for online testing methods under positive dependence.
method Developed new methods to control FDR for online testing procedures under positive dependence.
result SAFFRON and LORD control FDR under positive dependence, not just conditional superuniformity.
We tackle linear bandits with partially observable features, achieving sublinear regret.
problem Linear regret due to unobserved features in partially observable linear bandits.
method Feature augmentation with orthogonal basis vectors and a doubly robust estimator.
result Sublinear regret bound of i l d e O ( ( d + d h ) T ) ilde{O}(\sqrt{(d + d_h)T}) i l d e O ( ( d + d h ) T ) . New method identifies causal variables from partially observed data.
problem Learning from unpaired observations with instance-dependent partial observability.
method Proposes two methods enforcing sparsity in the inferred representation.
result Establishes two identifiability results for linear and piecewise linear mixing functions.
Estimates copula density for complex data distributions.
problem Estimating copula density from observed data.
method Neural network-based copula density neural estimation (CODINE).
result Novel approach capable of modeling complex distributions.
Study shows observability from a measurable set for Gevrey functions.
problem Determining observability from a subset for Gevrey functions.
method Used measurable sets and inequalities for Gevrey regular functions.
result Established observability estimates from measurable sets for Gevrey functions.
New method models asymmetric data with improved tail dependence.
problem Asymmetric data and tail dependence modeling.
method Generalized Skew-t Probabilistic Principal Component Analysis.
result Improved modeling of asymmetric data with tail effects.
New algorithm improves treatment effect estimation from observational data.
problem Estimating the benefits and harms of interventions from observational data.
method Develops a deep kernel regression algorithm and posterior regularization framework.
result Substantially outperforms state-of-the-art on various benchmarks datasets.
Efficient methods for linear/logistic regression with network-dependent responses.
problem Regression with dependent responses in networked data.
method Projected gradient descent on negative log-likelihood, proving strong convexity and consistency.
result Strong consistency results for vector of coefficients and dependency strength.
New method for off-policy evaluation in POMDPs using future-dependent value functions.
problem Curse of horizon in off-policy evaluation for POMDPs.
method Develops future-dependent value functions and minimax learning method.
result PAC result and Bellman completeness for the proposed OPE estimator.
New algorithm estimates partially-observed linear systems with better rates than previous methods.
problem Estimating parameters of partially-observed linear systems with long-term dependencies and semi-parametric noise.
method Prefiltered least squares estimator with semi-parametric noise model.
result First algorithm provably estimates parameters of partially-observed linear systems with rates not dependent on dependency decay rate.
Improved classification by using past observations in hidden Markov models.
problem Improving classification accuracy in multiclass settings with dependencies.
method Hidden Markov model and kernel classification rules.
result Using past observations can significantly reduce misclassification risk.
SMT improves robotic long-horizon tasks by embedding and utilizing past observations.
problem Long-horizon tasks in partially observable environments require effective long-term memory.
method Scene Memory Transformer (SMT) embeds and uses attention to exploit spatio-temporal dependencies.
result SMT outperforms existing policies in visual navigation tasks.
New method for evaluating policies in complex decision-making models with hidden variables.
problem Evaluating policies in partially observable Markov decision processes with hidden confounders.
method Introduces novel identification methods and minimax estimation techniques for linking target policy's value and observed data distribution.
result Proposes three estimators for off-policy evaluation in POMDPs with latent confounders, demonstrating their effectiveness through nonasymptotic and asymptotic analysis.
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.
Study learns linear system dynamics from noisy bilinear data.
problem Learning linear dynamics from bilinear observations with process and measurement noise.
method Regression with Kronecker product design, data-dependent and independent error bounds.
result Upper bounds on statistical error rates and sample complexity for learning dynamics matrices.
Study shows DQN's performance degrades with temporal dependence in data.
problem Temporal dependence in replayed data affects DQN's performance.
method Modelled τ τ τ -mixing data, derived risk bounds, and empirical validation. result Temporal dependence leads to a degradation in DQN's performance rate.
A new measure of model complexity based on Fisher Information.
problem Model complexity measurement in statistical models.
method Effective dimension defined by the number of cubes needed to cover the model space.
result The effective dimension is scale-dependent and measures model complexity.
A new sampling method balances multi-label datasets by preserving category frequency order.
problem Sampling challenges in multi-label datasets with varying label frequencies.
method Uses multivariate Bernoulli distribution and label dependencies to estimate and weight label combinations.
result Produces a more balanced sub-sample with enhanced representation of minority categories.
Estimates dependent parameters using Markovian dependence with shrinkage.
problem Estimating dependent parameters from a hidden Markov model.
method Developed a novel non-parametric shrinkage algorithm combining Tweedie-based ideas and efficient state estimation.
result Superior performance compared to non-shrinkage methods in hidden Markov models.