arXiv research

Paper presents a robust Kalman filter for state estimation.

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage b…

Robust method estimates state, input, and parameters of linear systems online.

Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization

New method for robust fixed-point smoothing without state augmentation.

PINNs solve neuronal parameter and state estimation problems with limited data.

RORL improves offline RL robustness with conservative smoothing.

This paper achieves first-order regret bounds in reinforcement learning with large state spaces.

New method estimates robust mean in high dimensions with minimized outliers.

New method estimates covariance matrices without restrictive assumptions.

New method reduces state redundancy in HSMM for driving patterns.

The study assesses ML model robustness under worst-case subpopulations.

Robust Kalman filtering method for outlier detection.

Estimates intrinsic dimension of data sets robustly to noise.

The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep ne…

ROME improves density estimation for multi-modal, non-normal data.

RATIO improves neural network robustness and explainability.

Study examines robustness of NPI effectiveness models against COVID-19.

Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the c…

Improves robust transfer learning with side information.

Proposes MRIV framework for unbiased CATE estimation using binary IVs.

We propose a new framework for designing estimators for off-policy evaluation in contextual bandits. Our approach is based on the asymptotically optimal doubly robust estimator, but we shrink the importance weights to minimize a bound on the mean squared error, which results in a better bias-variance tradeoff in finite…

Examines WENDy-IRLS algorithm's noise robustness and efficiency in various differential equations.

New methods estimate policy value and gradients for deterministic policies from off-policy data.

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimat…

Improved neural likelihood estimation for SSMs with truncated-SNL.

We study the problem of robust subspace recovery (RSR) in the presence of adversarial outliers. That is, we seek a subspace that contains a large portion of a dataset when some fraction of the data points are arbitrarily corrupted. We first examine a theoretical estimator that is intractable to calculate and use it to …

Paper uses variational inference to estimate nonlinear models.

Develops a two-stage approach for robust tensor completion of visual data.

Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate serious…

SCOPE estimator improves covariance and precision matrix estimation.

The fundamental frequency (F0) represents pitch in speech that determines prosodic characteristics of speech and is needed in various tasks for speech analysis and synthesis. Despite decades of research on this topic, F0 estimation at low signal-to-noise ratios (SNRs) in unexpected noise conditions remains difficult. T…

He et al. (2018) have called into question the utility of pre-training by showing that training from scratch can often yield similar performance to pre-training. We show that although pre-training may not improve performance on traditional classification metrics, it improves model robustness and uncertainty estimates. …

New method improves neural network robustness to adversarial attacks.

Novel causal effect estimators and distributionally robust prediction methods.

Study shows refugee matching gains are robust to different evaluation methods.

Shifts dataset evaluates uncertainty in real-world tasks across modalities.

Improved model robustness against corruptions using online adaptation.

In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational i…

Bayesian neural networks improve likelihood-free inference efficiency.

Density-Softmax improves uncertainty estimation and robustness without sampling, reducing model size and latency.

Paper introduces GDR-learners for estimating potential outcomes from observational data.

Paper evaluates and proposes a new method for robust curvature estimation from noisy point cloud data.

State-space models are used in a wide range of time series analysis formulations. Kalman filtering and smoothing are work-horse algorithms in these settings. While classic algorithms assume Gaussian errors to simplify estimation, recent advances use a broader range of optimization formulations to allow outlier-robust e…

Proposes MR estimator for unbiased recommendation models.

We propose and analyze a block coordinate descent proximal algorithm (BCD-prox) for simultaneous filtering and parameter estimation of ODE models. As we show on ODE systems with up to d=40 dimensions, as compared to state-of-the-art methods, BCD-prox exhibits increased robustness (to noise, parameter initialization, an…

Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can tolerate only a tiny fraction of errors. Recent work in theoretical computer science has shown that, in appropriate distributional models, it…

C-kNN-LSH identifies similar patient histories for causal inference in longitudinal data.