RobBoost optimizes a deep model ensemble for certified robustness.
problem Improving the robustness of deep models.
method Optimizes a neural network's robustness certificate through model selection and weighting.
result RobBoost forms a more robust ensemble with better certified robustness and clean accuracy.
Voting ensemble of robust models improves robustness.
problem Improving robustness of defensive models against adversarial attacks.
method Hard-label voting ensemble of pretrained robust models.
result Voting ensemble can boost robust error over individual models.
New method certifies joint adversarial robustness of model ensembles.
problem Ensuring robustness of model ensembles against adversarial attacks.
method Proposes a novel technique to certify joint robustness, building on prior work on single-model robustness certification.
result Demonstrates the effectiveness of certifying joint robustness of ensembles, improving understanding of ensemble defenses.
This paper explores how diverse neural network ensembles improve prediction accuracy and robustness against deception.
problem Improving prediction accuracy and robustness of neural networks against adversarial attacks.
method Examines and measures ensemble diversity, develops algorithms for creating and combining diverse ensembles.
result Greater diversity in neural network ensembles leads to higher accuracy and robustness against deception.
This study evaluates the robustness of transformation-based ensemble defense against evasion attacks.
problem Understanding the reasons behind the robustness improvement in transformation-based ensemble defense.
method Designing two adaptive attacks to evaluate transformation-based ensemble defense, conducting experiments to analyze robustness.
result The robustness improvement is mainly from irreversible transformations rather than the ensemble of models.
DVERGE diversifies adversarial vulnerabilities to enhance robust ensemble models.
problem Diverse adversarial vulnerabilities for robust ensemble models.
method Isolates and diversifies adversarial vulnerabilities through distillation and training.
result Achieves higher robustness against transfer attacks compared to previous methods.
LOTOS improves ensemble robustness by promoting orthogonal transformations.
problem Transferability of adversarial examples threatens robustness of classification models.
method LOTOS promotes orthogonality among sub-spaces of transformations in ensemble models.
result LOTOS increases robust accuracy of ensembles by 6 percentage points against black-box attacks.
This paper improves ensemble robustness by promoting diversity among individual models.
problem Existing ensemble models are vulnerable to adversarial attacks.
method Introduces a new diversity promoting regularizer to enhance robustness.
result The method improves adversarial robustness while maintaining normal accuracy.
Proposes an ensemble loss function for robust regression.
problem Improving robustness of simple regression models in noisy environments.
method Ensemble techniques applied to a simple regressor with a half-quadratic learning algorithm.
result Significantly improves performance of simple regressors in noisy environments.
This paper tackles robustness of ensemble stumps and trees under general ℓ_p norm perturbations.
problem The vulnerability of ensemble stumps and trees to small input perturbations under the ℓ_∞ norm.
method Developed dynamic programming algorithms for robustness verification and certified defense under general ℓ_p norm perturbations.
result First certified defense method for ensemble stumps and trees under ℓ_p norm perturbations.
The paper verifies the robustness of classifier ensembles against randomized attacks.
problem Ensuring classifier ensembles are robust against arbitrary randomized attacks.
method Formal verification procedure using SMT and MILP encodings to assess robustness.
result Proves the NP-hardness of the robustness-checking problem and provides upper bounds on attack sets.
New approach verifies tree ensembles efficiently, making them more robust.
problem Verifying robustness of decision tree ensembles against evasion attacks is NP-hard.
method Identified large-spread ensembles, proposed verifiable learning, designed training algorithm.
result Large-spread ensembles can be verified in polynomial time and are more robust.
This work improves adversarial robustness by boosting model ensembles with margin maximization.
problem Single models are insufficient for defending against adversarial attacks.
method Margin-boosting approach to learn ensembles with maximum margin.
result Our algorithm outperforms existing ensembling techniques and large models trained end-to-end.
New method creates diverse neural ensembles for better uncertainty estimation and robustness.
problem Creating more robust neural networks for uncertainty estimation and dataset shift.
method Automatically constructing ensembles with varying architectures.
result Ensembles with varying architectures outperform deep ensembles in accuracy, uncertainty calibration, and robustness.
Stochastic deep learning algorithms can generalize well if their ensemble robustness is bounded.
problem Why do stochastic deep learning algorithms generalize well?
method Revisit and introduce ensemble robustness as a measure of robustness for stochastic algorithms.
result Stochastic deep learning algorithms can generalize well if their ensemble robustness is bounded in average over training examples.
Enhances robustness for time series classification using self-ensemble method.
problem Limited adversarial robustness in time series classification.
method Proposes a self-ensemble method to improve Randomized Smoothing's robustness certification.
result Demonstrates superior robustness compared to baseline approaches.
Calibrated ensembles improve both ID and OOD accuracy in distribution shift.
problem Desired balance between in-distribution and out-of-distribution accuracy.
method Ensemble standard and robust models, calibrating on ID data only.
result ID-calibrated ensembles outperform state-of-the-art methods on multiple datasets.
Ensemble of models with uncorrelated loss functions improves adversarial robustness.
problem Adversarial attacks on deep neural networks.
method Diversity Training: training an ensemble of models with uncorrelated loss functions.
result Our method significantly improves adversarial robustness of ensembles.
Proposes a new method for ensembling neural subnetworks.
problem Computational expense and limited flexibility of traditional deep ensembles.
method Sequential Bayesian neural subnetwork ensembling.
result Outperforms traditional ensembles in various metrics.
Single neural networks can match deep ensembles' benefits without the complexity.
problem The effectiveness and necessity of deep ensembles in neural network models.
method Demonstrated limitations of ensemble diversity and OOD performance in deep ensembles compared to a single larger model.
result A single neural network can replicate deep ensembles' benefits in uncertainty quantification and robustness.
Efficiently builds diverse sub-model ensembles for robust self-supervised learning.
problem Challenges in diversity and efficiency of deep ensembles for self-supervised representation learning.
method Ensemble of independent sub-networks with a new loss function for diversity.
result Significantly improves prediction reliability and model calibration.
Improves natural accuracy of deep learning models by combining robust predictions and features.
problem Maintaining natural accuracy while resisting adversarial attacks.
method Ensemble methods combining robust and standard models.
result Optimized natural accuracy through ensemble of robust models.
SWEEN improves certified robustness via weighted ensembling of smoothed classifiers.
problem Finding optimal base classifiers for randomized smoothing.
method Smoothed WEighted ENsembling (SWEEN) scheme.
result SWEEN achieves optimal certified robustness under mild assumptions.
The paper analyzes and minimizes transferability of adversarial attacks between models in an ensemble.
problem Adversarial attacks can transfer between models, posing security risks.
method Introduces a gradient-based measure to assess and reduce transferability, and uses it during training to increase robustness.
result Demonstrates that the gradient-based measure can be used to increase an ensemble's robustness to adversarial attacks.
DASH improves ensemble generalizability by encouraging diverse, flat loss landscapes.
problem Improving generalization and robustness of deep ensembles.
method DASH promotes diversity and flatness in deep ensembles by encouraging base learners to move towards low-loss regions of minimal sharpness.
result DASH improves ensemble generalizability, as demonstrated by extensive empirical evidence.
This work proposes a robust ensemble method for decision trees that resists adversarial attacks.
problem Adversarial attacks on machine learning models, especially decision trees.
method Feature partitioning to train robust ensembles and approximate certification methods.
result The proposed ensemble method can resist evasion attacks by a majority of its models.
Proposes a meta-learning method for robust portfolio optimization.
problem Optimizing a robust portfolio ensemble with diverse sub-portfolios.
method Uses a deep generative model with convolutional, LSTM, and dense layers to generate diverse sub-portfolios.
result The ensemble portfolio is robust and generalizes well, balancing performance and diversity.
New method tests tree models without causing computational pressure.
problem Incompatibility of adversarial robustness testing with tree ensembles.
method Smooths tree ensembles with sigmoid functions and uses gradient descent.
result Successfully reveals adversarial vulnerability of tree ensemble models.
New adversarial training enhances malware detectors against various attacks.
problem Vulnerability of malware detectors to evasion attacks.
method Proposes a mixture of attacks and adversarial training to improve deep neural networks.
result Significantly enhances robustness of deep neural networks against a wide range of attacks.
Ensemble of diverse CNNs detects and mitigates adversarial attacks.
problem Detecting and defending against adversarial attacks.
method An ensemble of specialized CNNs with a voting mechanism.
result Significant reduction in adversarial attack risk rate.
EDRBO optimizes Bayesian optimization with continuous contexts using ensemble models and robust methods.
problem Bayesian optimization with unknown and continuous contextual distributions leads to suboptimal results.
method EDRBO uses ensemble surrogate models and Wasserstein ball ambiguity sets to handle uncertainty and maintain computational tractability.
result EDRBO achieves sublinear cumulative regret guarantees of order O ( γ T T ) \mathcal{O}(γ_T \sqrt{T}) O ( γ T T ) . This study extends verifiable learning to boosted tree ensembles, enabling efficient security verification.
problem Efficiently verifying the robustness of boosted tree ensembles against norm-based attackers.
method Formal verification of robustness for large-spread boosted tree ensembles, considering L ∞ L_\infty L ∞ -norm and pseudo-polynomial time for L p L_p L p -norm verification. result Polynomial time verification for L ∞ L_\infty L ∞ -norm attackers, NP-hard for other norms, and pseudo-polynomial time for L p L_p L p -norm verification. Proposes SWA for adversarial training to improve model robustness.
problem Overfitting in adversarial training due to insufficient sample complexity.
method Adversarial training with Stochastic Weight Average (SWA).
result Improves model robustness on CIFAR-10, CIFAR-100, and SVHN datasets.
This work studies adversarial transferability and proposes ensemble methods to improve robustness.
problem Adversarial transferability in neural networks and its implications for robustness.
method Investigates the effect of various factors on adversarial transferability and proposes ensemble attack methods.
result Transferability is significantly hampered by input quantization and architectural mismatch, but not by initialization.
FoRDE uses input gradients to improve neural network ensembles.
problem Improving neural network ensembles for robustness and accuracy.
method Proposes FoRDE, an ensemble learning method based on ParVI, which repels function space by input gradients.
result FoRDE significantly outperforms DEs and other ensemble methods in accuracy and calibration.
Paper proposes an ensemble of attacks to evaluate adversarial robustness more reliably.
problem Insufficient evaluation of adversarial defenses leads to incorrect robustness assessments.
method Developed two extensions of PGD-attack and combined them with two complementary attacks.
result Identified several broken defenses with lower robust test accuracy than reported.
Proposes optimizing neural network ensemble diversity in feature space.
problem Improving diversity in neural network ensembles while maintaining performance.
method Optimizes particles in the feature space of a specific intermediate layer.
result Significantly outperforms Deep Ensembles on various metrics.
New hyperparameter ensembles boost neural network performance and uncertainty.
problem Improving neural network robustness and uncertainty quantification.
method Designing ensembles over both weights and hyperparameters, stratified across random initializations.
result Hyper-deep and hyper-batch ensembles outperform deep and batch ensembles on various architectures.
Spatial smoothing improves BNNs' accuracy, uncertainty, and robustness without increasing computational cost.
problem Large ensembles in BNNs increase computational cost and reduce performance.
method Spatial smoothing adds blur layers to convolutional neural networks to ensemble neighboring feature map points.
result Spatial smoothing improves BNNs' performance with fewer ensembles and enhances robustness.
A new method speeds up uncertainty estimation in image classification.
problem Fast and accurate uncertainty estimation for robust robotics applications.
method Deep sub-ensembles, where only layers close to the output are ensembled.
result Significant speedup in uncertainty estimation with minimal error and NLL increase.
Ensemble methods improve neural networks' accuracy and robustness against adversarial perturbations.
problem Adversarial perturbations can cause deep learning models to misclassify.
method Used ensemble methods to defend against adversarial perturbations.
result Ensemble methods improve accuracy and robustness of neural networks against adversarial attacks.
Ensemble method ranks homologous proteins robustly across various similarity metrics.
problem Ranking homologous proteins in a candidate set with high accuracy and robustness.
method Ensemble of models and assessment metrics, phalanxes, and aggregation of diverse metrics.
result Ensemble of phalanxes identifies strong and diverse subsets of feature variables for robust ranking.
Research explores how to make models more robust to adversarial attacks.
problem Vulnerability of convolutional neural networks to adversarial examples and their transferability.
method Investigates the impact of l p l_p l p regularization, SVM top layer, and ensemble models on robustness and transferability. result Models trained with different regularizers and ensemble models exhibit barriers to transferability.
Ensemble Adversarial Training improves model robustness to black-box attacks.
problem Vulnerability of adversarial training to black-box attacks and novel attacks.
method Augmenting training data with perturbations from other models.
result Ensemble Adversarial Training yields models with strong robustness to black-box attacks.
REBEC improves robustness of wind wave models using evolutionary techniques.
problem Improving the calibration of numerical wind wave models to local conditions.
method Robust evolutionary-based calibration approach (REBEC) for building stochastic ensemble of models.
result REBEC outperforms baseline SPEA2 in achieving a balance between model quality and robustness.
BAE uses boosting to improve autoencoder ensembles for robust outlier detection.
problem Overfitting in autoencoders limits their effectiveness in unsupervised outlier detection.
method Boosting-based Autoencoder Ensemble (BAE) trains autoencoders sequentially with weighted sampling to reduce outliers and inject diversity.
result BAE outperforms state-of-the-art approaches in various outlier detection conditions.
Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
SharpBalance improves deep ensemble performance by balancing sharpness and diversity.
problem Improving deep ensemble performance in both in-distribution and out-of-distribution scenarios.
method Introducing SharpBalance, a novel training approach that balances sharpness and diversity within ensembles.
result SharpBalance effectively improves the sharpness-diversity trade-off and ensemble performance in ID and OOD scenarios.