In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researc…
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48 results for “robustness benchmark”
In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performan…
This work benchmarks and theorizes robust NAS under adversarial training.
problem Lack of benchmark evaluations and theoretical guarantees for robust NAS architectures under adversarial training.
method Released a comprehensive data set and established a generalization theory using the neural tangent kernel.
result Established a generalization theory for robust NAS architectures under adversarial training.
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.
BREEDS benchmarks assess model robustness to subpopulation shifts.
problem Measuring model robustness to novel subpopulation shifts.
method Controlled synthesis of realistic distribution shifts using class structure.
result Validated model sensitivity and effectiveness of robustness interventions.
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets,…
RobustBench aims to standardize adversarial robustness evaluation in image classification.
problem Lack of systematic understanding and error-prone robustness evaluations.
method Standardized benchmark with restricted models and adaptive attacks.
result Reflects current state of the art in adversarial robustness.
Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithmstat.ML
Natural experiment dataset reveals inconsistent treatment effect estimators.
problem Inconsistent results from over 20 estimators on a new dataset.
method Created a benchmark to evaluate estimator accuracy, derived variance formula, introduced new estimator.
result Doubly robust estimators outperform others by orders of magnitude.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
problem Instance-dependent label noise in graph data.
method BeGIN introduces a benchmark with various noise types and evaluates noise-handling strategies across GNN architectures.
result Challenges of instance-dependent noise, especially LLM-based corruption, and the importance of node-specific parameterization.
NES improves robust optimization with noisy inputs.
problem Finding robust optima in problems with input and measurement noise.
method Noisy-Input Entropy Search (NES) acquisition function based on Gaussian process modeling.
result NES reliably finds robust optima, outperforming existing methods.
TimeGraph creates synthetic datasets for robust time-series causal discovery.
problem Lack of reliable synthetic benchmark datasets for robust time-series causal discovery.
method Developed comprehensive synthetic datasets with temporal properties, including trends, seasonality, and noise.
result Demonstrated significant variations in algorithm performance under realistic temporal conditions.
New method compares classifiers using GSD-front, addressing statistical uncertainty and robustness.
problem Comparing classifiers with multiple quality metrics and statistical uncertainty.
method Proposes GSD-front and statistical tests for robust comparisons.
result Reliable method for comparing classifiers with statistical uncertainty and robustness.
URSABench benchmarks Bayesian methods for deep learning models.
problem Scalability issues in Bayesian inference for deep learning.
method Open-source benchmark suite for assessing approximate Bayesian inference methods.
result Initial results show promise for addressing uncertainty and robustness in deep learning.
Current OOD benchmarks overestimate model robustness to spurious correlations.
problem Spurious correlations degrade OOD performance, but benchmarks show the opposite.
method Analyze OOD datasets for spurious correlations and derive conditions for robustness.
result Current OOD benchmarks are misspecified and overestimate model robustness.
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
New benchmark evaluates BDL methods in medical retinopathy diagnosis.
problem Evaluate robustness and scalability of BDL methods in medical applications.
method Developed a new benchmark with real-world diabetic retinopathy tasks.
result Some BDL techniques overfit uncertainty to datasets, underperforming on new benchmark.
New framework improves adversarial robustness in one-stage L2D.
problem Adversarial robustness in one-stage Learning-to-Defer (L2D).
method Formalizes attacks, proposes cost-sensitive adversarial surrogate losses, establishes theoretical guarantees.
result Improves robustness against untargeted and targeted attacks while preserving clean performance.
We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unpre…
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarioscs.LG
CausalCompass evaluates TSCD robustness under violations of modeling assumptions.
problem Widespread adoption of TSCD is hindered by untestable causal assumptions and lack of robustness evaluation.
method CausalCompass is a flexible benchmark framework for assessing TSCD robustness under violations of modeling assumptions.
result No single method consistently attains optimal performance across all settings, but deep learning-based methods perform well.
Robust RL improves controller robustness to dynamics variations using adversarial populations.
problem Robustness issues in RL when dynamics are perturbed.
method Adversarial population augmentation to the Robust RL formulation.
result Population-based adversarial approach yields more robust and generalizable policies.
This paper provides a comprehensive benchmark and taxonomy for certifiably robust DNN defenses.
problem Certifiably robust defenses against adversarial attacks for deep neural networks.
method Taxonomy and benchmark of certifiably robust approaches.
result First comprehensive benchmark of certifiably robust approaches on different datasets.
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. …
Enhances GNN robustness during inference using Conditional Random Fields.
problem Vulnerability of GNNs to adversarial attacks.
method Post-hoc approach using Conditional Random Fields (CRF).
result Improves robustness of GNNs across various models.
New algorithms achieve better regret bounds for online classification with relaxed benchmarks.
problem Competing with worst-case optimal binary loss in online classification.
method Comparing against predictors robust to small input perturbations, performing well under Gaussian smoothing, or maintaining a prescribed output margin.
result Regret guarantees depend only on VC dimension and instance space complexity, with an dependence on the generalized margin.
CEB enhances model resilience through simple entropy bottleneck.
problem Improving model robustness against adversarial attacks.
method Conditional Entropy Bottleneck (CEB) combined with data augmentation.
result CEB significantly boosts adversarial robustness on various benchmarks.
Adversarial training effectiveness varies widely due to inconsistent training settings.
problem Variability in adversarial training effectiveness due to inconsistent training settings.
method Comprehensive evaluation of 10+ adversarial training methods and their hyperparameters.
result Basic training settings like weight decay can significantly impact adversarial robustness.
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
PortBench benchmarks LLMs for PM, revealing their weaknesses in diversification and robustness.
problem Lack of benchmarks for LLM-driven portfolio management, especially in diversification and robustness.
method Developed a comprehensive benchmark with a static QA dataset and a dynamic allocation pipeline, introducing metrics to evaluate correlation and robustness.
result 90% of LLMs fail to outperform a basic equal-weight allocation, highlighting their limitations in diversification and robustness.
TS-Fault benchmarks TSF models against structural faults.
problem Evaluating the robustness of time series forecasting models against structured events.
method TS-Fault uses parameterized fault scenarios with controllable difficulty.
result Three findings contradict common leaderboard intuition.
Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robu…
TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantificationcs.LG
TailedTS dataset benchmarks heavy-tailed time series forecasting and periodicity quantification.
problem Benchmarking robustness of time series models under heavy-tailed distributions.
method Derived from Wikipedia page views, introduces periodicity quantification and robust loss functions.
result Standard Gaussian models degrade on high-volume page categories, while robust alternatives perform consistently.
DA-GNN improves robustness of GNNs by modeling noise dependencies.
problem Real-world graph node features often contain noise, leading to performance degradation in GNNs.
method DA-GNN captures noise dependencies using variational inference and new benchmark datasets.
result DA-GNN consistently outperforms existing baselines across various noise scenarios.
We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…
Improved model robustness against corruptions using online adaptation.
problem Machine vision models' vulnerability to image corruptions like blurring or compression artefacts.
method Using corrupted images' statistics for unsupervised online adaptation to improve robustness.
result ResNet-50 achieves 62.2% mCE on ImageNet-C with adaptation, improving from 76.7% without.
Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performanceq-fin.TR
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
Optimal asset allocation strategy outperforms stochastic benchmark.
problem Achieving higher terminal wealth than a stochastic benchmark.
method Data-driven Neural Network optimization framework for dynamic asset allocation.
result Optimal adaptive strategy outperforms benchmark with higher median and right-skewed terminal wealth.
Paper develops a robust Bayesian optimization method for noisy zeroth-order settings.
problem Achieving robustness to distributional shift in machine learning.
method Distributionally robust Bayesian optimization (DRBO) algorithm for noisy zeroth-order optimization.
result DRBO algorithm provably obtains sub-linear robust regret in various settings.
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSscs.LG
Study evaluates adversarial training for deep learning IDSs against various attacks.
problem Evasion attacks against deep learning-based IDSs.
method Investigated adversarial training using min-max approach on CNN and RNN.
result Adversarial training improves robustness against five attack methods.
Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This paper studies how adversarial robustness transfers from teacher to student during knowledge distillation. We find that a large amount of robu…
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systemati…
Paper tackles robust imitation learning from noisy demonstrations.
problem Learning from noisy demonstrations is challenging.
method Optimizes a classification risk with a symmetric loss, combining pseudo-labeling and co-training.
result Our method is more robust than state-of-the-art methods.
Novel Bayesian neural network method for robustness.
problem Adversarial robustness without online training.
method Distributes uncertainty across all inputs.
result Demonstrates robustness on benchmark datasets.
New metrics fail adversarial tests, with some more robust than others.
problem Evaluation metrics for time-series anomaly detection were improved but not fully robust.
method Adversarial stress-testing of 12 adopted metrics on real benchmarks.
result Some metrics are more robust than others, with ROC-based metrics being gamed more often.
Unified Python package N benchmarks NN-based matrix completion methods.
problem Matrix completion with robust NN methods across diverse applications.
method Unified Python package N consolidates NN-based methods.
result NN-based techniques outperform classical methods in real-world settings.
New algorithm ATENT improves adversarial robustness in neural networks.
problem Improving neural network robustness against adversarial attacks.
method Proposes a new loss function with entropic regularization for training robust neural networks.
result ATENT achieves competitive robust classification accuracy on benchmark datasets.
Enhances neural network robustness with polyhedral envelope regularization.
problem Improving neural network robustness against adversarial attacks.
method Introduces polyhedral envelope regularization to bound the robustness region.
result Demonstrates improved robustness guarantees with minimal computational overhead.