Improved state estimation in high-dimensional models using Zig-Zag Sampler.
problem Weight degeneracy in particle filtering methods for high-dimensional state space models.
method Discrete Zig-Zag Sampler applied within the Composite MH Kernel of SMCMC framework.
result Improves estimation accuracy and increases acceptance ratio in high-dimensional state estimation.
A new particle filter avoids resampling to improve state estimation in high dimensions.
problem Particle deprivation in high-dimensional state spaces.
method A resampling-free particle filter designed to mitigate particle deprivation.
result The filter offers a near-accurate representation of the posterior distribution in high-dimensional contexts.
A new method reduces high-dimensional state space for dynamic choice models.
problem Estimation of dynamic discrete choice models is computationally intensive and infeasible in high-dimensional settings.
method Recursive partitioning algorithm to reduce dimensionality of high-dimensional state space.
result Our method reduces estimation bias and makes estimation feasible.
The paper uses Gaussian variational approximation for high-dimensional state space models.
problem High-dimensional state space models with complex covariance structures.
method Gaussian variational approximation with dynamic factor model for reduced covariance structure.
result The approach provides a reduced and conditional independence structure for high-dimensional state vectors.
AD-EnKFs use machine learning to improve data assimilation in high-dimensional systems.
problem Data assimilation in high-dimensional, unknown dynamics systems.
method Auto-differentiable ensemble Kalman filters blending machine learning and ensemble Kalman filters.
result AD-EnKFs outperform existing methods in the Lorenz-96 model.
ETGPSSM efficiently models high-dimensional, non-stationary systems with reduced complexity.
problem Prohibitive computational and parametric complexity in high-dimensional, non-stationary dynamical systems.
method ETGPSSM integrates a single shared GP with input-dependent normalizing flows for scalable and flexible modeling.
result ETGPSSM outperforms existing models in computational efficiency and accuracy.
Method learns low-dim. state vars from noisy high-dim. data.
problem Discovering dynamical models from noisy high-dimensional data.
method Stochastic Variational Deep Kernel Learning with encoder and latent model.
result Effective denoising, compact state representation, and uncertainty quantification.
Solves high-dimensional observation learning for control models.
problem Learning dynamics from high-dimensional images is challenging.
method Proposes a Beta DVBF approach to handle latent and observable space discrepancies.
result Demonstrates improved model learning from high-dimensional observations.
A new method for analyzing high-dimensional time-series data using deep neural networks.
problem Challenges in modeling high-dimensional time-series data with explicit state and observation processes.
method Deep Direct Discriminative Decoders (D4) for high-dimensional observation processes.
result D4 outperforms traditional SSMs and RNNs in various time-series data applications.
IBPF algorithm tackles high-dimensional parameter learning for complex systems.
problem Learning high-dimensional parameters in complex, partially observed, and nonlinear systems.
method Iterated Block Particle Filter (IBPF) for graphical state space models.
result IBPF algorithm consistently beats the curse of dimensionality across various experiments.
HashReward improves imitation learning in high-dimensional environments by balancing reward generation and dimensionality reduction.
problem Making policies generalize well in high-dimensional state-action spaces, especially in game playing with raw pixel inputs.
method HashReward uses supervised hashing to balance reward generation and dimensionality reduction.
result HashReward outperforms state-of-the-art methods in high-dimensional environments.
Novel model selection method outperforms current state-of-the-art in high-dimensional graphical models.
problem Accurate model selection in high-dimensional graphical models.
method Graphical Neighbour Information (GNI) criterion.
result Demonstrates oracle performance in high-dimensional model selection, outperforming current methods.
Anomaly detection for high-dimensional data using large deviations principle.
problem Challenges in anomaly detection for high-dimensional data.
method Large Deviations Anomaly Detection (LAD) algorithm.
result Outperforms state-of-the-art methods on high-dimensional data sets.
Algorithm learns diffusion processes with high-dimensional state spaces.
problem Stochastic control of unbounded diffusion processes with high-dimensional state spaces.
method Adaptive partitioning and learning algorithm that refines discretization based on estimation bias and statistical confidence.
result Established regret bounds that depend on problem parameters, extending to unbounded diffusion processes.
Review of algorithms for linear system approximations.
problem Linear approximation of high-dimensional dynamical systems.
method State-of-the-art algorithms for low-rank DMD.
result Provides additional details for comprehensive understanding.
The paper analyzes neural networks for solving high-dimensional Schrödinger eigenvalue problems.
problem Analyzing generalization error of neural networks for high-dimensional Schrödinger eigenvalue problems.
method Proves convergence rate of generalization error independent of dimension d under spectral Barron space assumption. Verifies assumption by proving regularity estimate. result Generalization error rate is independent of dimension d under spectral Barron space assumption. This paper analyzes AMP's performance in high-dimensional regression problems with finite sample size.
problem Statistical estimation in high-dimensional problems like compressed sensing and low-rank matrix estimation.
method Finite sample analysis of AMP for high-dimensional regression problems.
result AMP's performance can be accurately characterized by state evolution with high probability for moderately large dimensions.
MFMCi factors out state variables to make Monte Carlo simulation work in high-dimensional MDPs.
problem High-dimensional MDPs make Monte Carlo simulation impractical.
method Factoring out some state and action variables to enable Model-Free Monte Carlo.
result MFMCi enables Monte Carlo simulation in high-dimensional MDPs.
BOIDS optimizes high-dimensional problems by guiding optimization with one-dimensional lines.
problem Scaling Bayesian Optimization to high-dimensional problems.
method BOIDS uses a sequence of one-dimensional direction lines guided by an adaptive selection technique and incorporates subspace embedding for efficiency.
result BOIDS outperforms state-of-the-art methods on various synthetic and real-world problems.
An adaptive dropout approach improves high-dimensional Bayesian optimization.
problem High-dimensional black-box optimization problems.
method Adaptive dropout of variables in the acquisition function.
result AdaDropout effectively tackles high-dimensional challenges and improves solution quality.
Algorithm estimates human decision-making in high-dimensional states with finite-time guarantees.
problem Estimating optimal policies and measures of fit in dynamic decision models with high-dimensional state spaces.
method Single-loop estimation algorithm with stochastic gradient steps for reward maximization.
result Algorithm converges to a stationary solution with finite-time guarantees and approximates maximum likelihood sublinearly.
This paper improves entropy calculation for policy gradient in high-dimensional action spaces.
problem Calculating entropy and its gradient for high-dimensional action spaces is computationally infeasible.
method Developed unbiased estimators for entropy bonus and its gradient.
result Entropy estimators substantially improve performance with minimal additional computational cost.
A new method identifies critical transitions in high-dimensional data.
problem Challenges in identifying critical transitions in high-dimensional time-series data.
method Spatial-temporal Principal Component Analysis (stPCA)
result Identifies tipping points before critical transitions reliably.
High-dimensional data simplifies problems, contrary to the curse of dimensionality.
problem Exponential difficulty in high-dimensional problems.
method Analysis of high-dimensional datasets and their geometric properties.
result Generic high-dimensional datasets exhibit simple geometric properties.
Study of Langevin algorithm in noisy high-dimensional inference.
problem Analyzing the Langevin algorithm's performance in noisy high-dimensional inference.
method Analytic study of Langevin algorithm's performances using the spiked matrix-tensor model.
result The algorithmic threshold of the Langevin algorithm is sub-optimal compared to AMP.
NSMC improves SMC accuracy in high-dimensional filtering.
problem Challenges of SMC in high-dimensional Bayesian filtering.
method Nested Sequential Monte Carlo (NSMC) generalizes SMC by using approximate, properly weighted samples.
result NSMC achieves improved accuracy on spatio-temporal state space models.
The paper analyzes Q-learning convergence rates with asynchronous updates.
problem Analyzing convergence rates of asynchronous Q-learning algorithms.
method Derives rates of convergence using high-dimensional central limit theorems.
result Establishes a rate of order up to n−1/6log4(nSA) for hyper-rectangles. Method uses neural networks for high-dimensional committor function calculations.
problem Computing committor functions for high-dimensional stochastic processes.
method Parameterizes committor function with neural networks and optimizes weights using stochastic algorithms.
result Achieves moderate accuracy for high-dimensional problems.
AF improves sampling from high-dimensional, multi-modal distributions.
problem Sampling from high-dimensional, multi-modal distributions is challenging.
method Annealing Flow (AF) using Continuous Normalizing Flow (CNF) with dynamic Optimal Transport (OT) objective and annealing procedures.
result AF significantly improves training efficiency and stability, outperforming state-of-the-art methods.
LCD improves causal discovery in high-dimensional gene data.
problem Predicting causal effects in large-scale gene expression data.
method Local Causal Discovery (LCD) with practical estimators, ICP algorithm inspiration, preselection method, and statistical tests.
result LCD estimator closely matches ICP's accuracy but is simpler and faster.
GAIfO learns from videos without knowing actions.
problem Learning from observation without access to actions.
method Generative adversarial networks for state-only demonstrations.
result GAIfO performs comparably to classical methods and significantly outperforms existing IfO methods.
Deep learning reduces complex data to simpler predictors.
problem High-dimensional data reduction in input-output models.
method Hierarchical layers of latent features for constructing predictors.
result Deep learning is a black-box method for high-dimensional function estimation.
New AMP algorithm detects change points in high-dimensional GLMs.
problem Detecting change points in high-dimensional GLMs.
method Approximate Message Passing (AMP) algorithm for estimating signals and change points.
result Characterization of AMP algorithm's performance in high-dimensional limit.
Proposes GPLFR for predicting high-dimensional outputs with few data.
problem Predicting high-dimensional outputs from limited data.
method GPLFR combines Gaussian process and linear-Gaussian decoding for high-dimensional prediction.
result GPLFR outperforms existing methods in predicting high-dimensional outputs.
High-dimensional GAN training analyzed with exact dynamics.
problem Training dynamics of high-dimensional GANs.
method Exact analysis of microscopic and macroscopic dynamics in high-dimensional limit.
result Noise level is critical for feature recovery and convergence.
Nested model averaging improves high-dimensional linear regression performance.
problem High-dimensional linear regression with predictor ordering impact.
method Combining model averaging with regularized estimators on the solution path.
result Nested model averaging with lasso and SLOPE outperforms competing methods.
A new framework for time series analysis using state-space learning.
problem Ineffectiveness of traditional Kalman filtering in handling big data and multiple explanatory variables.
method State Space Learning (SSL) framework using statistical learning for high-dimensional regression.
result SSL outperforms traditional methods in subset selection and forecasting accuracy.
GTSNE improves data visualization for high-dimensional data.
problem Visualizing high-dimensional data points in a 2D map.
method GTSNE is a variation of t-SNE that captures both local and macro structures.
result GTSNE produces better visualizations of high-dimensional data compared to other methods.
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
problem Challenges in high-dimensional, nonlinear Bayesian filtering with sparse observations.
method A novel data assimilation method using latent representations and a coupled VAE for efficient state encoding and reconstruction.
result Latent-EnSF outperforms traditional methods in accuracy, convergence, and efficiency for complex systems.
EGL optimizes complex functions without fitting them, achieving state-of-the-art results.
problem Optimizing high-dimensional, non-convex functions in AI tasks.
method EGL trains a neural network to estimate the objective gradient directly, not fitting the function.
result EGL achieves state-of-the-art results in challenging optimization problems.
Minimum attention improves reinforcement learning performance in high-dimensional dynamics.
problem Improving reinforcement learning performance in high-dimensional nonlinear dynamics.
method Applying minimum attention as a regularization technique in reinforcement learning, including model-based and model-free approaches.
result Minimum attention outperforms state-of-the-art algorithms in few-shot adaptation and variance reduction.
A new VAR model with low-rank constraint for high-dimensional correlated series.
problem Predicting high-dimensional correlated series with hidden factors.
method Vector auto-regressive (VAR) model with low-rank transition matrix.
result Our method shows excellent performances on various simulated datasets and competitive/predictive in real macro-economic data.
Federated framework learns causal states to predict counterfactuals without centralizing data.
problem Decentralized counterfactual reasoning in coupled industrial systems with private data.
method Federated causal representation learning in state-space systems.
result Proves convergence to centralized oracle and provides privacy guarantees.
The paper introduces a method for interpretable principal component analysis of high-dimensional time series.
problem Inconsistent and difficult-to-interpret principal component estimates in high-dimensional regimes.
method Localized sparse principal component analysis of spectral density matrices in frequency domain.
result Efficient algorithm for sparse-localized estimates of principal subspaces.
A new distributed learning method for high-dimensional linear classification.
problem Efficiently performing linear classification on large-scale, high-dimensional data.
method Feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification.
result FD-SVRG outperforms other distributed methods in terms of communication cost and wall-clock time.
Successor Options discovers reusable skills using landmark states.
problem Discovering reusable skills in reinforcement learning.
method Leverages Successor Representations to build a state space model and learns intra-option policies using a novel pseudo-reward.
result Demonstrates the approach's efficacy on grid-worlds and high-dimensional robotic control environments.
New methods combine MALA and mGRAD for scalable Bayesian inference in high-dimensional state-space models.
problem Bayesian inference in high-dimensional state-space models with limited scalability.
method Combines gradient-based MALA and prior-informed mGRAD for scalable inference.
result Extends classical MCMC methods to handle multiple time steps and particles.
A new reward learning module improves imitation learning in high-dimensional environments.
problem Challenges in high-dimensional environments for imitation learning.
method Generative model to generate intrinsic reward signals.
result Our method outperforms state-of-the-art IRL methods on Atari games.