Improved SINDy autoencoder for identifying noisy dynamical systems.
problem Robust identification of noisy dynamical systems from data.
method Incorporates noise-separating neural network structures into SINDy autoencoder architecture.
result Accurately recovers latent dynamics and estimates measurement noise from noisy observations.
New metric compares noisy neural trajectories using optimal transport.
problem Existing metrics fail to capture differences in noisy, dynamic neural responses.
method Proposed an optimal transport distance metric for Gaussian processes.
result Metric effectively compares neural dynamics in different systems.
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.
This work introduces a method to learn dynamical systems from noisy sensor measurements using multiple shooting.
problem Learning dynamical systems from noisy sensor measurements is challenging due to system instability.
method A scalable method based on multiple shooting.
result Robust learning of latent representations of dynamical systems from noisy measurements.
DynaCor detects noisy labels by learning from corrupted training signals.
problem Label noise in real-world datasets hinders model generalization.
method DynaCor introduces label corruption to indirectly simulate noisy labels and learns to distinguish clean from noisy instances.
result DynaCor outperforms state-of-the-art competitors in noisy label detection.
Optimizes online learning with noisy gradient feedback for slowly changing minimizers.
problem Optimizing online learning performance with noisy gradient feedback for slowly changing minimizers.
method Introduces a path variation metric to analyze dynamic regret under true and noisy gradient feedback.
result Achieves optimal dynamic regret bounds under various feedback scenarios.
Deep learning scheme identifies and reconstructs chaotic and stochastic systems from noisy data.
problem Challenging identification of governing equations from noisy and partial observations.
method Jointly learns inference model and governing laws using variational deep learning.
result Framework generalizes state-of-the-art methods and accounts for stochastic variabilities.
Gradient descent finds linear systems from noisy data.
problem Identifying unknown linear dynamical systems from noisy observations.
method Stochastic gradient descent on maximum likelihood objective.
result Gradient descent efficiently converges to global optimizer in polynomial time and sample complexity.
The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
problem Challenges in capturing nonlinear dynamics from noisy time series data.
method A projected nonlinear state-space model with kernel functions applied to projected lines.
result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.
Paper improves generalization bounds for noisy stochastic algorithms.
problem Improving generalization bounds for noisy stochastic algorithms.
method Introduces Exponential Family Langevin Dynamics (EFLD) and establishes data-dependent expected stability based generalization bounds.
result Sharp generalization bounds with O(1/n) sample dependence and gradient discrepancy.
Method learns dynamics from noisy partial observations.
problem Reconstructing stochastic dynamical systems from indirect noisy data.
method Amortized path generation method for nonlinear stochastic filtering.
result Learned conditional path generator quantifies uncertainty.
Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing …
Empirical mode modeling improves state-space analysis of noisy data.
problem Analyzing nonlinear systems with noisy data.
method Combining empirical mode decomposition with empirical dynamic modeling.
result Empirical mode modeling enhances state-space representations in noisy data.
Detects anomalies in noisy data from linear systems.
problem Identifying samples of noise in a linear dynamical system.
method Robust spectral filtering and anomaly detection method.
result Guaranteed statistical performance in identifying noise samples.
Develops interpretable model for latent stochastic systems from noisy data.
problem Learning interpretable models of latent stochastic dynamical systems from noisy data.
method Semi-parametric model using Gaussian process for drift, inference of latent paths with sparse variational description.
result Flexible nonparametric model of dynamics with interpretable portraits.
Deep learning model simulates noisy dynamical systems without distributional assumptions.
problem Simulating noisy dynamical systems with unknown distributional properties.
method DE-LSTM model using LSTM network for multi-label classification and penalized maximum log likelihood.
result DE-LSTM makes accurate predictions of probability distributions for noisy dynamical systems.
Algorithm generates private continuous-time data for sensitive domains.
problem Private generation of continuous-time data for sensitive domains.
method Mean-field Langevin dynamics and noisy particle gradient descent.
result Strong privacy guarantees for one-time data contributions.
Bayesian-SINDy learns differential equations from noisy data quickly.
problem Learning correct model equations from limited and noisy data.
method Bayesian-SINDy framework using Gaussian approximations.
result Bayesian-SINDy is more robust and accurate in learning correct model equations from noisy data.
Study on privacy leakage in noisy gradient descent algorithms.
problem Information leakage of iterative randomized learning algorithms about training data.
method Analyzes the dynamics of Rényi differential privacy loss in noisy gradient descent algorithms.
result Privacy loss converges exponentially fast for smooth and strongly convex loss functions.
Measures time-delay embedding for noisy, sparse data.
problem Applying Takens' embedding theorem to real-world, noisy data.
method Formulated a measure-theoretic generalization of the embedding theorem, using optimal transport.
result Reconstructed full state of dynamical systems from time-lagged partial observations robust to noise and sparsity.
Enhances network intrusion detection in noisy data.
problem Robustness against contaminated and noisy data inputs in network intrusion detection.
method Probabilistic Temporal Graph Network Support Vector Data Description (TGN-SVDD) model.
result Significant improvements in detection performance with synthetic noise.
SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.
problem Unbounded metric movement costs in bandit online convex optimization.
method SCaLE algorithm for high-dimensional dynamic quadratic hitting costs and ℓ2-norm switching costs, with spectral regret analysis. result First algorithm achieving sub-linear dynamic regret without hitting cost knowledge.
Study improves Gaussian Process Latent Variable Model for noisy longitudinal data.
problem Noisy and incomplete longitudinal data makes learning representations difficult.
method Augment variational approximation with systematic samples of unseen observations.
result Demonstrates improved learning of Gaussian Process Dynamical Systems in noisy data.
Method learns latent dynamics of complex systems from noisy data.
problem Challenging to construct ROMs from noisy high-dimensional data.
method Recurrent stochastic variational deep kernel learning (SVDKL).
result Framework accurately predicts system evolution in low-dimensional latent spaces.
Statistical mechanics models node-perturbation learning with noisy baselines.
problem Understanding learning dynamics in node-perturbation algorithms with noisy baselines.
method Developed statistical mechanics to model node-perturbation learning with noisy baselines and derived coupled differential equations.
result Derived coupled differential equations of order parameters to depict learning dynamics and calculated generalization error.
The α-Alternator adapts to varying noise levels in sequences, improving robustness and performance.
problem Current models assume uniform noise levels, limiting performance on noisy temporal data.
method Introduces α-Alternator using Vendi Score to dynamically adjust noise sensitivity. result Outperforms Alternators and state-of-the-art models in trajectory prediction, imputation, and forecasting.
New method visualizes noisy data better than existing techniques.
problem Noisy data impairs data visualization methods.
method Functional Information Geometry (FIG) adapts EIG framework using functional data analysis.
result FIG outperforms EIG variant in capturing true structure, robustness, and speed.
SINDy-PI robustly identifies implicit dynamics from noisy data.
problem Accurately modeling nonlinear dynamics from noisy data.
method Parallel, implicit SINDy algorithm with multiple optimization algorithms and model selection.
result Significantly more noise robust than previous SINDy approaches.
A new particle filter uses diffusion models to improve state estimation from noisy data.
problem Sequentially estimating the state of a dynamical system from noisy and incomplete observations.
method Uses a diffusion model to simulate and predict system dynamics, incorporating noisy observations to refine predicted states.
result An unbiased particle filtering method that rigorously fuses observational data with diffusion model simulations.
The paper analyzes generalization of noisy iterative algorithms using communication theory.
problem Generalization of models trained by noisy iterative algorithms under different distributions.
method Connecting noisy iterative algorithms to additive noise channels in communication theory.
result Distribution-dependent generalization bounds for noisy iterative algorithms.
New method models PDEs from noisy, limited data.
problem Modeling PDEs with incomplete, noisy data.
method Learned linear transformation of spatial grid points, followed by dynamics learning in a reduced basis, then back transformation.
result Rapid high-resolution simulations with smaller training data sets.
Proposes PredVAR model for reduced-dimensional dynamics from noisy data.
problem Extracting low-dimensional dynamics from high-dimensional noisy data.
method Probabilistic reduced-dimensional vector autoregressive model with oblique projection.
result Iterative algorithm yields dynamic latent variables with rank-ordered predictability.
A hybrid neural network improves robustness in estimating vehicle parameters from noisy data.
problem Estimating parameters of a mechanical vehicle model from noisy acceleration data.
method Introduced a convolutional neural network with two objective functions: naive and hybrid.
result The hybrid objective function outperforms the naive one in robustness on noisy input data.
Quantum reservoir computing tackles noisy quantum computers for temporal tasks.
problem Efficiently process input sequences on noisy quantum computers.
method Quantum reservoir computing using dissipative quantum dynamics.
result Small and noisy quantum reservoirs can handle high-order nonlinear temporal tasks.
Study of accelerated dynamics for convex function minimization with noisy gradients.
problem Minimizing smooth convex functions with noisy gradients.
method Formulate and study continuous-time stochastic dynamics, prove convergence rates.
result Derive estimates of convergence rates for function values, both persistent and asymptotic.
Resetting from checkpoints improves DNN training with noisy labels.
problem Latent gradient bias induced by noisy labels causes overfitting.
method Stochastic resetting applied to SGD to mitigate latent gradient bias.
result Resetting significantly improves DNN generalization performance.
Consensus NN learns from noisy data only for medical image denoising.
problem Lack of clean training data for medical image denoising.
method Trains neural network using only noisy data by splitting and combining subsets.
result Improved performance on denoising medical images compared to existing methods.
Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
problem Change detection in noisy dynamical systems
method Partition-based empirical approximations and finite-state stationary distribution stability
result Finite-sample bound for empirical stationary density
New bounds for noisy iterative algorithms reduce overfitting risk.
problem Bounding generalization error for noisy, iterative algorithms.
method Derive bounds on generalization error using mutual information and sub-Gaussian loss function.
result Generalization error bounds for a broad class of iterative algorithms.
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
TRS-ODENs learn dynamics with time-reversal symmetry for more efficient learning.
problem Learning dynamics with time-reversal symmetry for more efficient learning.
method Proposed a loss function and a new framework (TRS-ODENs) to learn dynamics efficiently.
result TRS-ODENs can learn dynamics from noisy and complex trajectories efficiently.
A new framework for generating predictive features in noisy multivariate time series.
problem Predicting noisy multivariate time series with limited user effort.
method Develops a feature programming framework based on spin-gas dynamical Ising models.
result Validated the method on synthetic and real-world datasets.
Improved quantum control fidelity for noisy systems using differential evolution.
problem Stagnation in non-convex optimization for noisy quantum dynamics.
method Employed differential evolution algorithms to optimize quantum control parameters.
result Achieved superior fidelity and scalability in quantum phase estimation and gate design.
Proposes a new method to handle noisy labels without needing accurate noise transition estimation.
problem Learning with noisy labels in the presence of class-conditional noise.
method Introduces a Latent Class-Conditional Noise (LCCN) model that embeds noise transition in a Bayesian framework and iteratively infers latent labels.
result Demonstrates superior performance compared to state-of-the-art methods on various noisy label datasets.
Study agnostic feature-based dynamic pricing models with linear policies and noisy valuations.
problem Tackles dynamic pricing with unknown noise and no assumptions on data.
method Studies two agnostic models: linear policy and linear noisy valuation, presenting algorithms and regret bounds.
result Demonstrates no-regret learning is possible under weak assumptions, but noisy feedback is not significantly more useful than bandit feedback.
New method learns chaotic dynamics from single noisy trajectory.
problem Chaos in complex systems is hard to model accurately with machine learning.
method Adversarial optimal transport objectives to learn summary statistics and emulator from single noisy data.
result Emulators trained with proposed objectives have significantly improved long-term statistical fidelity.
GP-NODE combines Gaussian processes and NeuralODEs for Bayesian system identification.
problem Bayesian systems identification from partial, noisy and irregular observations.
method Differentiable programming, Hamiltonian Monte Carlo, Gaussian Process priors, sparsity-promoting priors.
result Efficient inference of posterior distributions over plausible models with quantified uncertainty.
Algorithm identifies bilinear dynamical systems from noisy data.
problem Learning a realization of a partially observed bilinear dynamical system.
method Regression of outputs to highly correlated covariates for Markov-like parameters.
result High probability error bounds on identification algorithm under uniform stability assumption.