Adversarial Learning

Adversarial learning is a machine learning technique that improves model robustness and fairness by pitting two neural networks against each other—a generator and a discriminator—in a competitive training process. Current research focuses on applications across diverse fields, including improving fairness in predictive analytics, enhancing robustness in reinforcement learning and domain adaptation, and mitigating the effects of adversarial attacks on various models. This approach is significant because it addresses critical limitations of standard machine learning methods, leading to more reliable and equitable outcomes in various applications, from financial risk assessment to medical image analysis and autonomous systems.

Papers

March 20, 2022

Mitigating Gender Bias in Machine Translation through Adversarial Learning
Eve Fleisig, Christiane Fellbaum
Machine Translation Adversarial Learning Gender Bias

March 17, 2022

On the Properties of Adversarially-Trained CNNs
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Adversarial Attack Native Robustness Adversarial Example Adversarial Training Adversarial Learning Many Property

March 16, 2022

March 12, 2022

Towards Equal Opportunity Fairness through Adversarial Learning
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial Training Adversarial Learning Bias Mitigation Adversarial Debiasing Standard Adversarial Training

March 4, 2022

Adversarial Patterns: Building Robust Android Malware Classifiers
Dipkamal Bhusal, Nidhi Rastogi
Adversarial Attack Adversarial Learning Malware Detection Android Malware Adversarial Pattern Malware Classifier

March 1, 2022

Global-Local Regularization Via Distributional Robustness
Hoang Phan, Trung Le, Trung Phung, Tuan Anh Bui, Nhat Ho, Dinh Phung
Loss Function Adversarial Learning Distributional Robustness

February 24, 2022

A Fair Pricing Model via Adversarial Learning
Vincent Grari, Arthur Charpentier, Marcin Detyniecki
Adversarial Learning Dynamic Pricing Fairness Assessment Actuarial Model

February 22, 2022

Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Ngoc Dung Huynh, Mohamed Reda Bouadjenek, Imran Razzak, Kevin Lee, Chetan Arora, Ali Hassani, Arkady Zaslavsky
Timely Survey Adversarial Attack Speech Recognition Adversarial Learning Speech Recognition System Safety Critical Domain

February 21, 2022

February 17, 2022

Gradient Based Activations for Accurate Bias-Free Learning
Vinod K Kurmi, Rishabh Sharma, Yash Vardhan Sharma, Vinay P. Namboodiri
Adversarial Learning Gradient Based Unbiased Learning Adversarial Method BIAS Detection Bias Reduction

February 16, 2022

Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars
Aakash Kumar
Convolutional Neural Network Generative Adversarial Network Adversarial Learning Self Driving Car Adversarial Policy Data Efficiency

February 15, 2022

StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection
Aqib Rashid, Jose Such
Adversarial Attack Adversarial Robustness Adversarial Learning Domain Adversarial Adversarial Threat Based Malware Detection

January 30, 2022

Co-Regularized Adversarial Learning for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Adversarial Learning Domain Alignment Domain Text Classification Domain Divergence Virtual Adversarial Training

January 29, 2022

Maximum Batch Frobenius Norm for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Adversarial Learning Domain Invariant Feature Batch Learning Domain Text Classification

January 27, 2022

Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks
Ramnath Kumar, Tristan Deleu, Yoshua Bengio
Reinforcement Learning Adversarial Training Adversarial Learning Multi Task Reinforcement Learning Exploration Performance

January 24, 2022

State-Conditioned Adversarial Subgoal Generation
Vivienne Huiling Wang, Joni Pajarinen, Tinghuai Wang, Joni-Kristian Kämäräinen
Adversarial Learning Hierarchical Reinforcement Learning Prior Policy Non Stationary Reinforcement Learning Subgoal Search Policy Update

January 23, 2022

Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Anish Lahiri, Marc Klasky, Jeffrey A. Fessler, Saiprasad Ravishankar
Training Data Adversarial Learning Image Reconstruction Learning Based Reconstruction Incremental Reconstruction Consistency Based Semi Supervised Learning Model Based Iterative Reconstruction

January 19, 2022

Privacy-Aware Human Mobility Prediction via Adversarial Networks
Yuting Zhan, Alex Kyllo, Afra Mashhadi, Hamed Haddadi
Adversarial Learning Mobility Data Private Training Utility Privacy Trade Human Mobility Prediction Trajectory Recovery Mobility Datasets