Study robust best-arm identification in linear bandits with lower bounds and algorithms.
problem Identify a near-optimal robust arm in linear bandits with adversarial actions.
method Propose instance-dependent lower bounds and both static and adaptive bandit algorithms.
result Sample complexity matches the lower bound and algorithms effectively identify robust arms.
New method improves neural network robustness by identifying functions rather than parameters.
problem Neural networks' lack of robustness to distribution shifts.
method Identify the function represented by quadratic networks, not their parameters.
result Obtain robust generalization bounds for neural networks.
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the column sparse outlier model. The existing methods for column sparse outlier model assumes either the knowledge of the dimension of the lower dimensional subspace or the …
Paper introduces Prob-SSI for robust OMA in noisy data.
problem Challenges in estimating modal parameters from noisy data.
method Probabilistic formulation of SSI, robust Prob-SSI algorithm.
result Robust Prob-SSI outperforms conventional SSI in corrupted data.
Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimizat…
New algorithms for best arm identification in bandits robust to misspecified parameters.
problem Inconsistent learning performance of traditional MAB algorithms when parameters are misspecified.
method Proposes two classes of asymptotically near-optimal algorithms for statistically robust MAB under fixed-budget pure exploration.
result Establishes fundamental performance limits and proposes algorithms that are asymptotically near-optimal.
A new clustering algorithm fuses heat diffusion and turning angle for robustness.
problem Cluster similar elements in various fields.
method Combines heat diffusion and maximal turning angle for robust fission clustering.
result The SARFC algorithm outperforms other methods in clustering performance.
In this paper, we propose an outlier-robust regularized kernel-based method for linear system identification. The unknown impulse response is modeled as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential …
Robust PCA, the problem of PCA in the presence of outliers has been extensively investigated in the last few years. Here we focus on Robust PCA in the outlier model where each column of the data matrix is either an inlier or an outlier. Most of the existing methods for this model assumes either the knowledge of the dim…
Bayesian methods reduce variance in subspace identification for small data sets.
problem High variance in traditional subspace identification methods for large models or small sample sizes.
method Investigation of Bayesian estimation solutions (regularized and shrinkage estimators) for subspace identification.
result Bayesian estimators reduce estimation risk by up to 40% compared to traditional methods.
Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robust…
WSINDy for PDEs robustly identifies models from noisy data.
problem Identifying nonlinear dynamics from noisy partial differential equations data.
method Weak formulation of PDEs, Fourier-based model identification, sequential-thresholding least-squares.
result WSINDy enables robust identification of PDEs in noisy conditions.
A novel algorithm for best-arm identification in non-stationary linear bandits reduces error probability.
problem Non-stationary environments in A/B testing scenarios.
method Proposes a novel algorithm P1-RAGE for robust best-arm identification. result Error probability decreases as exp(−TΔ(1)2/d), demonstrating robustness to non-stationarity. The paper provides robustness guarantees for mode estimation in bandits.
problem Understanding robustness in mode estimation under adversarial data contamination.
method Simple randomization and theoretical analysis of multi-armed bandits.
result Regret guarantees for various modal bandit problems.
End-to-end algorithm for controlling bilinear systems with probabilistic noise.
problem Controlling bilinear systems with noisy data.
method Proposes an end-to-end algorithm using statistical learning theory and robust controller design.
result Derived finite sample identification error bounds and structurally suitable for control.
New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.
problem Inaccurate error bounds for kernel-based system identification with unknown hyperparameters.
method Construct a high-probability set for true hyperparameters from marginal likelihood, then find worst-case posterior covariance.
result Proposed bounds contain true model with high probability and verified in simulations.
Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identificat…
The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.
problem Assessing causal mediation in the presence of post-treatment events like noncompliance or clinical events.
method Identifies natural mediation effects for entire populations and principal strata, derives efficient influence functions, and proposes multiply robust estimators.
result Multiply robust estimators are consistent under four types of misspecifications and efficient when all models are correct.
Unified method for learning from selectively labeled data.
problem Classification with selectively labeled data from multiple decision-makers.
method Unified cost-sensitive learning (UCL) approach.
result Unified method for robust classification in selective labeling.
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 algorithm optimally identifies best arm in both stochastic and adversarial settings.
problem Best arm identification in stochastic and adversarial reward scenarios.
method Parameter-free algorithm designed to be robust to adversarial rewards and optimal in stochastic problems.
result Algorithm's error rate matches optimal bounds in stochastic problems and is robust to adversarial rewards.
Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…
Bayesian framework for robust model discovery from noisy data.
problem Robust model discovery from noisy, sparse and irregular observations of nonlinear systems.
method Bayesian differential programming using Hamiltonian Monte Carlo and sparsity-promoting priors.
result Efficient inference of posterior distributions over plausible models with quantified uncertainty.
An adversarial process between two deep neural networks is a promising approach to train a robust model. In this paper, we propose an adversarial process using cosine similarity, whereas conventional adversarial processes are based on inverted categorical cross entropy (CCE). When used for training an identification mo…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly from the thermal response of the final device enables more robust and precise the…
New algorithm STCV improves sparse model discovery from normalised data.
problem Distortion of sparse model discovery due to data scaling.
method STCV, a novel sparse regression algorithm robust to data scaling.
result STCV outperforms standard methods on normalised, noisy datasets.
Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.
problem Estimating optimal dynamic treatment regimes using historical observational data when unconfoundedness is violated.
method Utilizes proximal causal inference framework to propose three nonparametric identification methods, a (K+1)-robust method, and establish a semiparametric efficiency bound.
result Establishes the (K+1)-robust method for learning optimal dynamic treatment regimes, validating its efficiency and multiple robustness through numerical experiments.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
problem Parameter identification and prediction in Volterra integral equations driven by Gaussian noise.
method Improved deep neural networks framework that incorporates inter-output relationships into the loss function.
result The framework enhances parameter estimation accuracy and provides accurate solutions for modeling stochastic systems.
Algorithm identifies Pareto set in bandits with contaminated feedback.
problem Identifying Pareto set in multi-objective bandits with adversarial contamination.
method Sample median-based multi-objective adaptive elimination algorithm.
result Sample complexity bound that depends on contamination probability.
This paper uses Bayesian optimization to efficiently identify stochastic dynamical systems.
problem Efficiently identifying linear stochastic dynamical systems with unknown coefficients and noise variances.
method Adaptive Bayesian optimization with ensemble Gaussian processes (EGP) and Kalman filter recursion.
result BO-based estimator achieves RMSE below the Cramer-Rao bound, improving robustness and consistency.
This paper studies active learning in the context of robust statistics. Specifically, we propose a variant of the Best Arm Identification problem for \emph{contaminated bandits}, where each arm pull has probability ε of generating a sample from an arbitrary contamination distribution instead of the true und…
Bayesian system ID improves robustness to sparse, noisy data.
problem Robust system identification with sparse, noisy data.
method Probabilistic formulation of system identification using Bayesian posterior.
result The log posterior is more robust and less affected by multiple minima.
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate differen…
In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and partially observed autonomous linear time invariant system driven by process and sensor noise. To do so, we propose studying the following two step process: first, using system identification tools rooted in subspace methods, we ob…
Enhances clustering for functional data, robust to outliers.
problem Challenges of clustering infinite-dimensional functional data and outlier sensitivity.
method Extends OCLUST algorithm to handle functional data, trimming outliers.
result Strong performance in clustering and outlier identification on simulated and real-world datasets.
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.
Solves parameter non-identifiability in Bayesian LTI system identification.
problem Parameter non-identifiability in standard Bayesian approaches for LTI system identification.
method Embedding canonical forms of LTI systems within the Bayesian framework.
result Unlocking the use of meaningful priors and robust uncertainty estimates.
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment. The normalized maximum correntropy criterion (NMCC) is derived from a correntropy based…
The paper addresses bias in survival analysis due to informative censoring.
problem Bias in treatment effect estimates due to informative censoring in survival analysis.
method Assumption-lean framework using partial identification to derive bounds on CATE.
result Proposes a meta-learner, SurvB-learner, to estimate bounds on CATE.
The paper provides a non-asymptotic error bound for linear system identification under nonlinear policies.
problem System identification for linear systems with nonlinear and/or time-varying policies under i.i.d. random excitation noises.
method Least square estimation with non-asymptotic error bound for bounded state and action trajectories.
result The error bound is consistent with linear policies and generalizes existing guarantees.
New method for group testing robust to errors in group membership specifications.
problem Errors in specifying group memberships during group testing.
method Debiased Robust Lasso Test Method (DRLT) based on Lasso debiasing.
result Extends LASSO bias mitigation to handle group membership specification errors.
Identifies arms with rewards significantly different from the majority.
problem Identifying arms with rewards deviating substantially from the majority.
method Computing outlier threshold using median and median absolute deviation.
result Proposes two δ-PAC algorithms for ROAI with upper bounds on sample complexity and worst case lower bound.
GNN improves financial risk detection in dynamic networks.
problem Complex, changing financial networks make traditional risk identification methods ineffective.
method Graph Neural Networks (GNN) for embedded representation learning of financial data.
result GNN enhances the detection of hidden risks and abnormal behaviors in financial networks.
Study relaxes identification assumptions for natural direct effects in non-randomized settings.
problem Identifying causal direct effects under unmeasured confounding.
method Developed relaxed conditions for identifying natural direct effects in non-randomized settings.
result Identified natural direct effect under unmeasured confounding conditions.
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning,…
RAMEN corrects observational data biases for multiple environments.
problem Bias in observational data for causal inference.
method RAMEN algorithm that leverages heterogeneity of multiple data sources.
result RAMEN produces unbiased treatment effect estimates.
We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the prediction of the outputs of the LDS and identification of the samples of noise, and prove guarantees on its statistical performance. One app…
Paper shows faster core identification in matching markets.
problem Core Identification Problem in one-sided matching markets.
method Randomized SVD on preference-derived Markov matrix.
result CIP solved in O(Ln) time, matching lower bound.