A new framework for performative prediction robust to distributional misspecification.
problem Performative prediction models can be influenced by their own predictions, leading to suboptimal outcomes.
method Introduces distributionally robust performative prediction (DRPO) to approximate the true performative optimum (PO) robustly.
result DRPO provides provable guarantees as a robust approximation to the true PO when the nominal distribution map is misspecified.
This work improves certifiably robust models by distilling knowledge from adversarially robust teachers.
problem Certifiably robust models suffer from poor standard performance.
method Knowledge distillation from adversarially robust teachers to improve standard performance.
result Distillation from adversarially robust teachers consistently improves certified training performance.
The paper develops a DRO framework for models robust to distributional shifts.
problem Learning models that perform well under distributional shifts.
method Distributionally robust stochastic optimization (DRO) framework.
result The DRO approach often improves performance on real tasks.
Empirical study finds robust optimization can improve portfolio performance in Indian markets.
problem Comparing robust optimization to Markowitz model for portfolio performance.
method Three robust optimization models (box, ellipsoidal, separable uncertainty sets) tested on Indian market data.
result Robust optimization can be a viable alternative to Markowitz model in real market setups.
Paper presents robust boosting methods for label noise.
problem Boosting methods degrade in noisy environments.
method Robust Minimax Boosting (RMBoost) with theoretical guarantees.
result RMBoost provides strong classification accuracy and robustness.
The paper tackles robust reinforcement learning with performance guarantees.
problem Finding a robust policy for RMDP with state space uncertainties.
method Proposes RLSPI algorithm for learning optimal robust policy with performance bounds.
result Demonstrates the performance of RLSPI on standard benchmark problems.
This work reveals the relationship between DNN generalization and adversarial behavior.
problem Understanding the relationship between DNN generalization and adversarial behavior.
method Introducing a metric to disentangle non-robust and robust components in DNN performance.
result Current DNNs rely heavily on the non-robust component for decent performance.
New winsorized mean improves robustness to up to 50% contamination.
problem Improving robustness of mean estimation in the presence of outliers.
method Outlyingness-induced winsorized mean approach.
result Achieves up to 50% contamination robustness with sub-Gaussian performance.
Framework learns robust control policies from expert demonstrations.
problem Adversarial robustness and closed-loop generalization in feedback control policies.
method Lipschitz-constrained loss minimization for certified robustness and generalization.
result Finite sample bound on policy learning error and robust closed-loop stability.
Study shows robust optimization improves VaR and CVaR performance in Indian market data.
problem Improving risk management in financial markets with robust optimization.
method Extended robust optimization framework for VaR and CVaR minimization.
result Robust optimization models outperform base versions in higher stock scenarios.
Develops robust few-shot learning models resistant to adversarial attacks.
problem Adversarial vulnerability in few-shot learning models.
method Adversarial Querying (AQ) algorithm for robust meta-learners.
result Achieves superior robust performance on few-shot image classification tasks.
New models improve classification model performance, especially robust to small training sets.
problem Improving classification model performance, especially robust to small training sets.
method Distributionally robust AUC maximization models using Kantorovich metric and hinge loss function.
result The proposed DR-AUC models outperform standard models in general and worst-case out-of-sample performance.
Proposes a new framework for balancing average- and worst-case performance in machine learning.
problem Robustness issues in machine learning, especially in safety-critical domains.
method Probabilistic robustness framework that balances average- and worst-case performance.
result Effective algorithm balances average- and worst-case performance with lower computational cost.
New mechanisms from primate vision improve neural network robustness.
problem Demonstrating robust neural networks to small adversarial perturbations.
method Investigated two biologically plausible mechanisms: non-uniform retina sampling and receptive field diversity.
result Non-uniform retina sampling and receptive field diversity improve adversarial robustness.
Robust VAE improves model performance on corrupted data.
problem Outliers in training data degrade model performance.
method Applying robust statistics to VAEs using beta-divergence.
result Improved robustness to outliers in generated representations.
The study assesses ML model robustness under worst-case subpopulations.
problem ML model performance degradation under non-training population.
method Two-stage estimation procedure for evaluating worst-case robustness over subpopulations.
result The method certifies model robustness and prevents unreliable deployments.
New method improves adversarial robustness without extra training steps.
problem Improving robustness of deep learning models against adversarial attacks.
method Guided Complement Entropy (GCE) training paradigm.
result GCE achieves better adversarial robustness with improved performance.
Improves target data performance of adaptive classifiers.
problem Adaptive classifiers perform poorly on target data due to distribution shift.
method Constructs robust discriminant analysis estimators.
result Robust discriminant analysis outperforms non-adaptive classifiers on target data.
Adversarial robustness improved by abstaining from decisions.
problem Improving classification accuracy in the presence of adversarial perturbations.
method Introducing an abstain option in binary classification problems, using metrics to quantify performance and robustness.
result There is a tradeoff between nominal performance and adversarial robustness.
Paper studies robust MDPs, improving sample complexity and asymptotic performance.
problem Optimal robust policy and value function in robust MDPs with generative models.
method Improves prior results on non-asymptotic and asymptotic performances of robust MDPs, considering various uncertainty sets.
result Improved sample complexity and asymptotic normality of optimal robust value function.
Ditto improves fairness and robustness in federated learning.
problem Fairness and robustness in statistically heterogeneous federated learning networks.
method Personalized federated learning framework (Ditto) with a scalable solver.
result Ditto achieves competitive performance and superior fairness and robustness compared to existing methods.
New robustness certificates for streaming models with a sliding window.
problem Applying robustness certificates to streaming data with correlated inputs.
method Deriving robustness certificates for models using a sliding window over a sequence of potentially correlated inputs.
result Guarantees hold for the average model performance across the entire stream, independent of stream size.
New method improves robustness of smoothed classifiers against adversarial attacks.
problem Improving robustness of smoothed classifiers against adversarial attacks.
method Proposes worst-case adversarial loss over input distributions as a robustness certificate, and uses duality and smoothness properties to provide an easy-to-compute upper bound.
result Shows superior robustness performance over state-of-the-art certified or heuristic methods.
Proposes robust model through Wasserstein geodesic interpolation of training data.
problem Improving model robustness through data augmentation.
method Augment data by finding worst-case Wasserstein barycenter on geodesic path.
result Improves robustness on CIFAR-10 up to 7.7% and on CIFAR-100 up to 16.8%.
New testing method quantifies ML vs AV robustness against evasion.
problem Benchmarking ML vs AV for malware detection.
method Developed new testing methodology to evaluate performance changes on modified files.
result ML systems show greater robustness against evasion than AV products.
Paper benchmarks adversarial robustness methods on image classification.
problem Vulnerability of deep neural networks to adversarial examples.
method Established a comprehensive benchmark with robustness curves.
result Found important findings on adversarial attack and defense methods.
The DoD needs a robust process to evaluate AI/ML model performance and robustness.
problem AI/ML models are brittle and nonrobust, posing risks in national security.
method Reviews AI/ML development process and best practices for evaluation.
result Recommendations for DoD evaluators to ensure robust AI/ML capabilities.
Study reveals layers in deep networks can be robust or critical, affecting model performance.
problem Understanding the role of different layers in deep neural networks.
method Empirical study of layer robustness and re-initialization effects.
result Layers in deep neural networks can be categorized as robust or critical, impacting model performance.
Develops robust MDPs for unknown disturbances with performance guarantees.
problem Unknown disturbance distribution in MDPs.
method Empirical distribution, sublevel set of distance function, weak convergence, concentration inequality.
result Robust optimal value function converges to true optimal value function with increasing sample sizes.
Enhances GCNs' robustness to graph perturbations.
problem Vulnerability of GCNs to graph structure perturbations.
method Generates edge-dithered auxiliary graphs and uses them in an adaptive GCN.
result Significantly improved performance in SSL tasks with noisy inputs and adversarial attacks.
Framework for robust RL in continuous control with model misspecification.
problem Model misspecification in reinforcement learning for continuous control.
method Integrates robustness into MPO algorithm through worst-case expected return objective and entropy regularization.
result Robust and soft-robust policies outperform non-robust policies in various domains.
Extends ML fairness to handle minority groups over time.
problem Limitations of existing fairness criteria.
method Performative Distributionally Robust Optimization.
result Improves fairness for minority groups over time.
New metrics show current interpretability methods lack robustness.
problem Lack of robustness in interpretability methods.
method Introduce metrics to quantify robustness and propose ways to enforce robustness.
result Current interpretability methods do not perform well according to introduced robustness metrics.
New method uses unlabeled data to improve robust learning models.
problem Current DRL formulations are too broad, leading to unreliable predictions.
method Incorporates unlabeled data to constrain the adversary in DRL.
result New formulation yields tighter performance guarantees and effective classifiers.
Meta-trained optimizers improve model robustness to image corruptions.
problem Robustness of deep learning models to input noise.
method Meta-training a learned optimizer to produce robust models.
result Meta-trained optimizers improve model robustness to Gaussian noise.
Proposes robust ITRs integrating multiple datasets to handle posterior shift.
problem Posterior shift in conditional outcome distributions between source and target populations.
method Distributionally robust approach with closed-form solution and adaptive uncertainty tuning.
result Achieves superior performance compared to existing methods in simulations and real-data applications.
The paper solves investment problems with uncertain factors using game theory.
problem Optimal forward investment in an incomplete market with model uncertainty.
method Combining stochastic differential games and ergodic BSDE approach.
result Representation of robust forward performance processes in factor form.
ART improves transfer learning performance with robust theory and methods.
problem Improving performance of primary tasks using auxiliary data.
method Adaptive Robust Transfer Learning (ART) pipeline with theoretical guarantees.
result ART provides a provable theoretical guarantee for adaptive transfer and robustness.
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.
EEG signals enhance speaker verification system robustness.
problem Improving speaker verification in noisy environments.
method Used end-to-end deep learning model with EEG and speech features.
result EEG signals improve speaker verification robustness, especially in noisy conditions.
Proposes a metric to measure DNN robustness against adversarial attacks.
problem Lack of a robustness metric for DNNs under adversarial attacks.
method Noise Sensitivity Score (NSS) and skewness based dataset robustness metric.
result Effective and generalizable metric for evaluating DNN robustness.
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
New metric measures deep learning robustness to noise.
problem Deep learning models are vulnerable to noise.
method Formal definition of robustness as localized Lipschitz constant.
result New metric evaluated on competitive vision datasets.
Enhances deep networks with manifold exploration for better performance and robustness.
problem Improving deep neural network performance and robustness to random transformations.
method Geometric transformations learned through line-search manifold exploration to misclassify images while maintaining manifold consistency.
result Significantly increased robustness and performance on challenging classification tasks.
Replicated and validated Rank-N-Contrast for robust regression.
problem Deep regression models struggle with continuous sample orders.
method Contrastive learning of continuous representations by ranking samples.
result Improved performance and robustness of RNC framework.
We propose a robust elastic net (REN) model for high-dimensional sparse regression and give its performance guarantees (both the statistical error bound and the optimization bound). A simple idea of trimming the inner product is applied to the elastic net model. Specifically, we robustify the covariance matrix by trimm…
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are …
New approach connects reinforcement learning robustness and regularization.
problem Dealing with external uncertainty in reinforcement learning.
method Introducing Wasserstein distributionally robust MDPs and a new regularizer.
result Established a dual relation between robust MDPs and regularization.