Paper shows robust estimators converge to true risk minimizers at optimal rates.
problem Understanding asymptotic properties of robust risk minimizers.
method Investigates robust analogues of empirical risk minimization, focusing on median of means estimator.
result Robust minimizers converge to true minimizers at optimal rates and have similar asymptotic variance.
Paper tackles heavy-tailed data without finite variance, proposing robust risk minimization.
problem Empirical risk minimization under heavy-tailed data with finite p-th moment. method Minimizes risk values robustly estimated via Catoni's method, using generalized generic chaining.
result Shows better performance of optimizer based on empirical risks via Catoni-style estimation.
The paper proves deep learning can be robust with certain loss functions.
problem The robustness of deep learning models under flawed data.
method Empirical-risk minimization with unbounded, Lipschitz-continuous loss functions.
result These loss functions provide efficient prediction under minimal data assumptions.
New method uses robust estimators for Newton's method in empirical risk minimization.
problem Improving robustness in empirical risk minimization.
method Robust Newton's method with gradient and Hessian replaced by robust estimators.
result Faster convergence rates in high-dimensional settings.
Online TERM improves robustness and fairness in streaming data.
problem Streaming data's lack of worst-case fairness and robustness in ERM.
method Proposes an online TERM formulation to balance average-case accuracy with worst-case fairness and robustness.
result Negative tilting effectively suppresses outlier influence, positive tilting improves recall with minimal precision loss.
This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empirical risk minimization is based on the fact that for a "well-behaved" stochastic process {f(X), f∈F} indexed b…
Develops a robust learning method for unknown context distributions.
problem Learning from data in different, unknown contexts.
method Focuses on excess risks, constructs distribution sets with statistical coverage.
result Shows robustness in worst-case scenarios without sacrificing nominal performance.
New method reduces sample complexity for robust learning.
problem Developing simple, sample-efficient learning algorithms for robust classification.
method Tolerant empirical risk minimization (RERM) for robust learning.
result Tolerant RERM requires only O(VC(H)dlog(D/γδ)/ε^2) samples for robustness regions of diameter D.
We propose a robust risk measurement approach that minimizes the expectation of overestimation plus underestimation costs. We consider uncertainty by taking the supremum over a collection of probability measures, relating our approach to dual sets in the representation of coherent risk measures. We provide results that…
Proposes methods to learn from biased samples, ensuring robust decision rules.
problem Learning from biased samples can lead to poor performance in real-world applications.
method Modeling sampling bias, using distributionally robust optimization and deep learning.
result Proposes a method to minimize worst-case risk under various test distributions.
Bayesian nonparametrics improves data-driven risk optimization under distributional uncertainty.
problem Improving out-of-sample performance in machine learning models due to distributional uncertainty.
method Combining Bayesian nonparametric theory and decision-theoretic preferences to propose a robust optimization criterion.
result The proposed robust optimization procedure provides favorable statistical guarantees and tractable approximations.
Unified framework TERM improves fairness and robustness.
problem Outliers and subgroup fairness in empirical risk minimization.
method Unified framework TERM with a hyperparameter tilt.
result TERM improves fairness and robustness.
This study connects Jacobian regularization to adversarial robustness and improves generalization.
problem Adversarial attacks make deep neural networks vulnerable.
method Developed a connection between Jacobian regularization and adversarial training, and established robust generalization gaps.
result Jacobian norms are related to both standard and robust generalization.
A new DP algorithm for weighted ERM protects sensitive data in predictive models.
problem Protecting sensitive personal information in predictive models trained via ERM.
method Proposes the first differentially private algorithm for weighted ERM with formal privacy guarantees.
result Demonstrates strong DP guarantees while maintaining robust performance in real-world data.
Simplifies risk minimization combining mean and standard deviation.
problem Minimizing mean and standard deviation under heavy-tailed losses.
method Adapting robust mean estimation technique to include standard deviation.
result Simple approach performs as well or better than alternative risk criteria.
New method improves adversarial robustness of neural networks.
problem Vulnerability of neural networks to adversarial attacks.
method Integrates φ-divergence regularization into distributionally robust risk function.
result Achieves near-optimal sample complexity with efficient stochastic gradient methods.
The problem of data uncertainty has motivated the incorporation of robust optimization in various arenas, beyond the Markowitz portfolio optimization. This work presents the extension of the robust optimization framework for the minimization of downside risk measures, such as Value-at-Risk (VaR) and Conditional Value-a…
We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimization and Owen's empiric…
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
problem Classical risk minimization approaches are not robust to distribution shifts.
method Robust risk minimization approach for minimax risk classifiers.
result MRCs provide performance guarantees and adapt to distribution shifts.
Paper explores using bootstrap methods to improve SGD's stability and robustness.
problem Improving the stability and robustness of SGD.
method Investigates empirical bootstrap approaches for SGD from algorithmic stability and statistical robustness perspectives.
result Demonstrates construction of purely distribution-free confidence intervals using bootstrap SGD.
Paper proposes a new method for WDRO with local perturbations, achieving better accuracy.
problem Wasserstein distributionally robust optimization's theoretical understanding needs improvement.
method Develops a new approximation theorem and risk consistency results for WDRO.
result The proposed method achieves significantly higher accuracy on noisy datasets.
Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. In this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process …
The paper improves semi-supervised learning using f-divergences and α-Rényi divergences.
problem Improving semi-supervised learning with noisy pseudo-labels.
method Inspired by f-divergences and α-Rényi divergences, the paper develops new empirical risk functions and regularization techniques. result The new methods show better performance than traditional self-training methods, especially in noisy pseudo-label scenarios.
Adversarial training is a technique for training robust machine learning models. To encourage robustness, it iteratively computes adversarial examples for the model, and then re-trains on these examples via some update rule. This work analyzes the performance of adversarial training on linearly separable data, and prov…
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a…
RAD improves robustness to domain annotation noise without explicit domain annotations.
problem Robustness to domain annotation noise in training data.
method Regularized Annotation of Domains (RAD) for last layer retraining.
result RAD outperforms state-of-the-art methods even with 5% noise in training data.
DORO improves DRO's performance and stability in tasks with subpopulation shift.
problem DRO's poor performance and instability in tasks with subpopulation shift.
method DORO, a refined risk function that prevents overfitting to outliers.
result DORO improves DRO's performance and stability on large modern datasets.
We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Huber epsilon-contamination model and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide cond…
We tackle imbalanced classification by weighting losses and derive robust risks.
problem Imbalanced classification where a label has low marginal probability.
method We examine convergence rates of weighted risks, define robust risks, and derive new robust risk problems.
result We show that particular weightings lead to conditional value at risk (CVaR) and derive new robust risk problems.
Minimizing the empirical risk is a popular training strategy, but for learning tasks where the data may be noisy or heavy-tailed, one may require many observations in order to generalize well. To achieve better performance under less stringent requirements, we introduce a procedure which constructs a robust approximati…
This work tackles robust multi-source domain adaptation under label shift.
problem Label shift and data contamination in multi-source domain adaptation.
method Domain-weighted empirical risk minimization framework with refinement procedure.
result The proposed method achieves superior performance in multi-category classification problems.
DRSS method identifies unnecessary samples and features in DR covariate shift.
problem Identifying unnecessary samples and features in DR covariate shift.
method Combines DR learning and safe screening techniques.
result DRSS method provides reliable identification of unnecessary samples and features under specified distribution uncertainty.
As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets …
New algorithm improves robustness by more regularization on less robust samples.
problem Improving robustness against adversarial attacks.
method Proposes a new adversarial training algorithm that applies more regularization to data vulnerable to adversarial attacks.
result Empirically superior to other existing algorithms, achieving state-of-the-art performance in generalization and robustness.
New method for efficient proximal mapping of 1-path-norm in shallow networks.
problem Efficiently handling the 1-path-norm of shallow neural networks.
method Closed-form proximal operator for efficient computation and upper bound on Lipschitz constant.
result Proximal mapping allows robust training against adversarial perturbations.
MetaPhysiCa tackles robust physics-informed machine learning for OOD tasks.
problem Designing robust PIML methods for OOD forecasting tasks in physics.
method Meta-learning procedure for causal structure discovery including invariant risk minimization.
result Significantly outperforms existing PIML and deep learning methods in OOD tasks.
This work analyzes CVaR under heavy-tailed data, providing generalization and robustness bounds.
problem Understanding CVaR's behavior under heavy-tailed data and rare high-impact losses.
method Learning-theoretic analysis of CVaR-based empirical risk minimization.
result Sharp, high-probability generalization and excess risk bounds under minimal moment assumptions.
Self-adaptive training improves deep learning robustness.
problem Improving deep learning performance on corrupted data.
method Dynamic correction of problematic labels using model predictions.
result Self-adaptive training significantly improves generalization over ERM under various levels of noise.
Stable Adversarial Learning improves robustness to distributional shifts.
problem Vulnerability of machine learning algorithms to distributional shifts.
method SAL algorithm that constructs a practical uncertainty set and conducts differentiated robustness optimization based on covariate stability.
result The proposed method uniformly improves performance across unknown distributional shifts.
The paper examines the tilted empirical risk's generalization and robustness under negative tilt.
problem The generalization error of machine learning algorithms under negative tilt.
method Uniform and information-theoretic bounds on the tilted generalization error under negative tilt.
result The tilted empirical risk's generalization error has a convergence rate of \(O(n^{-ε/(1+ε)})\).
Develops Bayesian approach for end-to-end learning in stochastic optimization.
problem Stochastic optimization problems under uncertainty.
method Bayesian interpretation and new end-to-end learning algorithms.
result Improved decision maps for empirical risk minimization and distributionally robust optimization.
Recent work shows GRW approaches do not improve over ERM in distributional shift.
problem Improving robustness to distributional shift in machine learning models.
method Generalized Reweighting (GRW) algorithms, which iteratively update model parameters based on reweighting of training samples.
result GRW approaches do not significantly improve over ERM in real applications with distribution shift.
Machine learning models (e.g., speech recognizers) are usually trained to minimize average loss, which results in representation disparity---minority groups (e.g., non-native speakers) contribute less to the training objective and thus tend to suffer higher loss. Worse, as model accuracy affects user retention, a minor…
The paper introduces a new measure of robustness for partially identifiable risks.
problem Achieving robustness when the robust risk is only partially identified.
method Introduces the worst-case robust risk and evaluates existing methods.
result Existing robustness methods are suboptimal in the partially identifiable case.
The paper analyzes the performance of empirical risk minimization for p-norm linear regression.
problem Empirical risk minimization on p-norm linear regression. method Analyzes performance under various conditions and moment assumptions.
result High probability excess risk bounds for empirical risk minimizer, matching asymptotic rates.
Improves speech recognition in noisy environments using robust acoustic models.
problem Adverse environments with significant mismatch between training and test conditions.
method Theoretical analysis of data augmentation as vicinal risk minimization, using mixture of Gaussians to incorporate robust inductive bias.
result Waveform-based approach shows 150% relative improvement in out-of-distribution generalization.
We analyze the generalization and robustness of the batched weighted average algorithm for V-geometrically ergodic Markov data. This algorithm is a good alternative to the empirical risk minimization algorithm when the latter suffers from overfitting or when optimizing the empirical risk is hard. For the generalization…
Paper extends transfer learning for decision rules, improving treatment rule estimation.
problem Estimating optimal individualized treatment rules under changing conditions.
method Bayes decision rules and low-dimensional empirical risk minimization.
result Consistent estimators and risk bounds established under mild conditions.