Native Robustness

Native robustness in machine learning focuses on developing models inherently resistant to various forms of input perturbations, including adversarial attacks and noisy data, without relying solely on post-hoc defenses. Current research emphasizes techniques like ensemble methods, reprogramming existing models, and modifying training procedures (e.g., using different learning rates for specific model layers or incorporating regularization methods) to improve robustness across diverse model architectures, including convolutional neural networks, vision transformers, and large language models. This field is crucial for deploying reliable AI systems in safety-critical applications, such as healthcare and autonomous driving, where model resilience to unexpected inputs is paramount.

Papers

August 19, 2024

A Biologically Inspired Design Principle for Building Robust Robotic Systems
Xing Li, Oussama Zenkri, Adrian Pfisterer, Oliver Brock
Native Robustness Robotic System Long Horizon Manipulation Task Intrinsic Robustness Inspired Design Robust Robot Adaptive Robotics

August 16, 2024

August 15, 2024

Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability
Haniyeh Ehsani Oskouie, Lionel Levine, Majid Sarrafzadeh
Native Robustness Context Information Artificial Intelligence Model Stronger Generalizability Model Performance Responsible AI

August 11, 2024

On the Robustness of Kernel Goodness-of-Fit Tests
Xing Liu, François-Xavier Briol
Native Robustness Goodness of Fit Kernel Stein Discrepancy

August 9, 2024

August 8, 2024

August 6, 2024

RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
Luis Roque, Carlos Soares, Luís Torgo
Native Robustness New Framework Hierarchical Time Series Forecasting Algorithm Hierarchical Time Series Forecasting

August 5, 2024

August 4, 2024

A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios
Samuel Ackerman, Ella Rabinovich, Eitan Farchi, Ateret Anaby-Tavor
Native Robustness Benchmark Datasets Novel Metric Adversarial Information

August 2, 2024

July 29, 2024

Revisiting the robustness of post-hoc interpretability methods
Jiawen Wei, Hugues Turbé, Gianmarco Mengaldo
Native Robustness Post Hoc Interpretability Hoc Interpretability

July 26, 2024

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White
Reinforcement Learning Native Robustness New Benchmark Continuous Environment Hyperparameter Configuration

July 25, 2024

Adversarially Robust Decision Transformer
Xiaohang Tang, Afonso Marques, Parameswaran Kamalaruban, Ilija Bogunovic
Native Robustness Decision Transformer Minimax Optimality Adversarial Environment Adversarial Reinforcement Learning Transformer Based Generative Adversarial

Native Robustness

Papers

A Biologically Inspired Design Principle for Building Robust Robotic Systems

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving

Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability

On the Robustness of Kernel Goodness-of-Fit Tests

Modeling Electromagnetic Signal Injection Attacks on Camera-based Smart Systems: Applications and Mitigation

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change

SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation

AExGym: Benchmarks and Environments for Adaptive Experimentation

Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning

RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms

RCDM: Enabling Robustness for Conditional Diffusion Model

On the Robustness of Malware Detectors to Adversarial Samples

A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios

Coalitions of Large Language Models Increase the Robustness of AI Agents

Assessing Robustness of Machine Learning Models using Covariate Perturbations

Certifiably Robust Encoding Schemes

Revisiting the robustness of post-hoc interpretability methods

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning

Adversarially Robust Decision Transformer