Abstract notes on robust statistical learning theory.
problem Developing robust estimators for statistical learning.
method Stressing principles of robust estimators construction and analysis.
result Emphasizes main principles of robust estimators construction and analysis.
Paper develops robust policy evaluation for reinforcement learning with outlier and heavy-tailed rewards.
problem Outlier contamination and heavy-tailed rewards in reinforcement learning.
method Develops a fully online robust policy evaluation procedure and efficient statistical inference.
result Establishes the Bahadur-type representation of the estimator and develops an online inference procedure.
Paper develops efficient algorithms for robust distributed learning with statistical guarantees.
problem Limited communication power and adversarial node behaviors in distributed learning.
method Surrogate likelihood framework and median/trimmed mean operations.
result Provable robustness against Byzantine failures and optimal statistical rates.
Survey on efficient robust estimators for high-dimensional statistics.
problem Efficient robust mean estimation in high dimensions.
method Emerging algorithmic techniques in theoretical computer science.
result First efficient robust estimators for fundamental tasks.
New federated learning protocols resist Byzantine failures and offer privacy guarantees.
problem Resisting Byzantine failures in federated learning.
method Proposes robust federated learning protocols with optimal statistical rates and privacy guarantees.
result Achieves nearly optimal statistical rates and tight rate in terms of all parameters for strongly convex losses.
The paper tackles robust policy learning in MDPs using statistical methods.
problem Offline data-driven sequential decision making in MDPs.
method Evaluates policies using average rewards centered at policy-induced stationary distributions. Developed a statistically efficient method for estimating robust optimal policies.
result Established a rate-optimal regret bound up to a logarithmic factor.
Robust learning method minimizes risk with corrupted data.
problem Statistical learning with unknown corrupted data fraction.
method Develops a robust learning method with specified corrupted data fraction upper bound.
result Optimal weights provide robustness against corrupted data.
Bandit algorithms struggle with consistent performance and robustness.
problem Achieving consistent and robust performance in stochastic multi-armed bandit settings.
method Analyzing regret minimization trade-offs and proposing distribution-oblivious algorithms.
result Logarithmic regret is inconsistent and super-logarithmic regret is necessary for consistent learning.
Improved estimator reduces bias in statistical learning models.
problem Asymptotic bias in classic WDRO estimator.
method Adjusted Wasserstein distributionally robust estimator.
result Asymptotic unbiased estimator with smaller MSE.
Robust Federated Learning tackles statistical and computational challenges in heterogeneous data and Byzantine machines.
problem Statistical and computational challenges in Federated Learning with heterogeneous data and Byzantine machines.
method Proposed a general statistical model for heterogeneous Federated Learning, solved the problem optimally, and proved statistical guarantees for outlier-robust clustering.
result Our algorithm matches the lower bound on estimation error and outperforms non-Byzantine-robust algorithms significantly.
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new cha…
E-ROBOT improves robust statistics and ML via Schrödinger bridge theory.
problem Statistical and machine learning tasks in high dimensions.
method Entropic-regularized Robust Optimal Transport (E-ROBOT) framework.
result E-ROBOT avoids the curse of dimensionality with O(n−1/2) sample complexity. RobPy offers robust statistical methods in Python.
problem Lack of robust statistical methods in Python.
method Built on NumPy, SciPy, and scikit-learn, RobPy includes robust tools for various statistical tasks.
result RobPy enables more users to perform robust data analysis in Python.
Survey of DRO, a robust optimization framework.
problem Risk-aversion and chance-constrained optimization challenges.
method Distributionally robust optimization (DRO) framework.
result DRO's growing importance in operations research and statistics.
Study on robustness in linear regression models, focusing on adversarial perturbations.
problem Understanding and improving robustness in linear regression models to adversarial perturbations.
method Developed a two-stage adversarial learning framework that incorporates model structure information.
result Proved the consistency and developed the Bahadur representation of the adversarially robust estimator.
This thesis improves practical reinforcement learning methods with robustness, scalability, and efficiency.
problem Improving reinforcement learning methods for practical applications.
method Analyzes and develops robust, scalable, and efficient reinforcement learning algorithms.
result Proves the efficiency and robustness of new RL methods.
Adaptive methods learn from multiple datasets, leveraging similarities and robust to outliers.
problem Simultaneously analyze multiple datasets with possible similarities and differences.
method Adaptive multi-task learning methods that automatically utilize similarities and handle differences.
result Sharp statistical guarantees and robustness against outlier tasks demonstrated.
Paper introduces robust learning methods using coordinate gradient descent.
problem Supervised learning with corrupted features and labels.
method Coordinate gradient descent combined with robust estimators of partial derivatives.
result Robust learning methods with nearly identical numerical complexity to non-robust ones.
Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.
problem Byzantine robustness in distributed learning systems.
method Variance reduced median-of-means (VRMOM) estimator for Byzantine robust distributed inference.
result Achieves a fast convergence rate with only a constant number of rounds of communications.
In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithm…
Targeted Learning uses robust statistics for reproducible research.
problem Improving reproducibility and rigor in statistical analyses.
method Principled standard for statistical estimation and inference, minimizing assumptions.
result Enhances reliability of statistical conclusions.
Paper improves robustness certification by integrating ML and logical reasoning.
problem Limited robustness certification under perturbation radius.
method Integrates statistical ML models with logical reasoning using Markov logic networks.
result First certified robustness bound for MLN derived and experimentally validated.
Paper explores robust estimators for kernel exponential families using smoothed total variation distances.
problem Outliers can severely impact classical estimators in statistical inference.
method Proposes smoothed total variation (STV) distance as a class of IPMs for robust estimation of kernel exponential families.
result STV-based estimators are robust against distribution contamination for kernel exponential families.
Robust estimation under Huber's ε-contamination model has become an important topic in statistics and theoretical computer science. Statistically optimal procedures such as Tukey's median and other estimators based on depth functions are impractical because of their computational intractability. In this paper, we est…
Develops robust learning framework under distributional perturbations.
problem Learning robust to data distributional changes.
method Distributionally Robust Optimization (DRO) under Wasserstein metric.
result Establishes performance guarantees and tractable formulations.
A framework integrates machine learning with robust control for safer, more reliable systems.
problem Combining machine learning with robust control for systems with stringent safety and reliability requirements.
method Integrates Gaussian Process Regression and state-of-the-art robust controller synthesis within a framework that provides rigorous guarantees.
result Demonstrated improved performance with more data while maintaining rigorous guarantees.
Study statistical guarantees for DRO with OT and OT-regularized divergences.
problem Enhancing adversarial robustness in machine learning models.
method Derive concentration inequalities for supervised learning via DRO-based adversarial training.
result First to cover soft-constraint costs and reweighting mechanisms in adversarial training.
Huber regression assessed for robustness in statistical learning.
problem Understanding Huber regression in nonparametric statistical learning.
method Assessment from statistical learning perspective, focusing on risk consistency, adaptive tuning, and convergence rates.
result Huber regression can be asymptotically mean regression calibrated under (1+ε)-moment conditions, justifying its robustness. Paper proposes robust estimators for heavy-tailed data with infinite variance.
problem Developing robust estimators for heavy-tailed data with infinite variance.
method Proposes two robust estimators: ridge log-truncated M-estimator and elastic net log-truncated M-estimator.
result Demonstrates robustness of log-truncated estimations over standard estimations through simulations and real data analysis.
Paper shows robustness of kernel-based pairwise learning without strict assumptions.
problem Statistical robustness of kernel-based pairwise learning under minimal conditions.
method No assumptions on input and output spaces; derives influence function and robustness.
result Qualitative robustness of kernel-based estimator established.
New framework for privacy-preserving statistical inference using robust statistics.
problem Privacy-preserving statistical inference with robust statistics.
method Introducing a general framework for parametric inference with differential privacy guarantees using M-estimators and test statistics.
result Demonstrated that differential privacy is weaker than robustness and can be achieved by randomizing robust M-estimators.
Survey of robust streaming techniques and their relationships.
problem Challenges in robust streaming and online learning.
method Overview and survey of robust streaming techniques, unifying theorems.
result Proved the relationship between robust streaming techniques.
A new framework for robustness analysis of deep neural networks using PAC-model learning.
problem Analyzing local robustness of deep neural networks.
method Black-box model learning with scenario optimisation to abstract DNN behaviour via an affine model with PAC guarantee.
result DeepPAC outperforms state-of-the-art statistical methods in practical robustness analysis.
Robust optimization and statistical robustness improve robot navigation policies.
problem Efficiently finding optimal robot navigation policies in uncertain environments.
method Combining robust optimization and statistical robustness with improved Bayesian optimization techniques.
result Safe and repeatable robot navigation policies are achieved with improved robust optimization methods.
New normalization method makes neural networks more robust to adversarial attacks.
problem Adversarial vulnerability of BatchNorm in deep neural networks.
method Identified distribution shift caused by adversarial images, proposed RobustNorm to use inference-time statistics.
result RobustNorm makes models more robust to adversarial attacks without sacrificing BatchNorm benefits.
Study enhances robustness of In-CVaR based regression models under perturbation and contamination.
problem Enhancing robustness of nonlinear regression models under perturbation and contamination.
method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.
result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.
RESIST improves decentralized learning resilience against MITM attacks.
problem Decentralized learning's vulnerability to MITM attacks.
method Multistep consensus gradient descent framework with robust statistics-based screening methods.
result Achieves algorithmic and statistical convergence for various ERM problems.
A robust aggregation method improves federated learning's accuracy in corrupted settings.
problem Making federated learning robust to corrupted updates from devices.
method Robust aggregation oracle based on geometric median for constant iterations of non-robust averaging.
result The robust aggregation oracle outperforms classical methods in high corruption levels.
Unified framework improves neural network robustness against label noise and adversarial attacks.
problem High sensitivity of neural networks to data contamination, including label noises and adversarial perturbations.
method Unified minimum-divergence estimation problem, rSDNet framework.
result Improves robustness to label corruption and adversarial attacks while maintaining competitive accuracy on clean data.
A debiasing method improves nonparametric regression's statistical properties.
problem Lack of theoretical guarantees for modern nonparametric regression methods.
method Model-free debiasing method incorporating a correction term.
result Debiased estimator satisfies pointwise and uniform risk convergence, asymptotic normality.
The paper investigates AI robustness through experiments and statistical analysis.
problem Inaccurate AI predictions can lead to safety and adoption issues.
method Design of experiments framework to study AI classification robustness.
result AI algorithms' robustness is influenced by various factors.
Paper proposes a shape-constrained approach to distributionally robust learning.
problem Challenges in statistical learning under distribution shift.
method Shape-constrained approach to distributionally robust learning (DRL). Assumes isotonic density ratio.
result Improved accuracy demonstrated in empirical studies.
Novel algorithm reduces privacy noise in machine learning.
problem High privacy noise in machine learning algorithms.
method Robust statistics, specifically median and trimmed mean, to bound sensitivity of SGD iterates.
result Improved privacy-utility trade-off with reduced noise and computational efficiency.
PRIME algorithm estimates mean while ensuring privacy and robustness.
problem Privacy and robustness in shared data analysis.
method Introduces PRIME, the first efficient algorithm for both privacy and robustness.
result Achieves both privacy and robustness for a wide range of distributions.
New robust method for optimal transportation improves statistical inference.
problem Sensitivity to outliers and undefinedness in optimal transportation methods.
method Robust optimal transportation with a tuning parameter λ, leading to robust Wasserstein distance.
result The robust method provides statistical guarantees and improves machine learning applications.
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (CLoss) as a speci…
Privacy improves robustness in statistical estimation.
problem Sparse mean estimation under privacy constraints.
method Sum-of-Squares method and exponential-time mechanisms.
result Private algorithms matching optimal tradeoffs are not known, but achieved via Sum-of-Squares.
The paper explores the limits of tight PAC-Bayes bounds for cheap models in robust statistics.
problem The challenge of obtaining meaningful bounds on the error of learning algorithms without prior assumptions.
method Investigates tight PAC-Bayes bounds for robust models with minimal cost.
result Demonstrates the limits of obtaining tight PAC-Bayes bounds for cheap models.