Robust Learning

Robust learning aims to develop machine learning models that are resilient to various forms of noise and uncertainty in data, including label noise, adversarial attacks, and distribution shifts. Current research focuses on developing algorithms and model architectures (e.g., multiview SVMs, graph neural networks, and diffusion models) that incorporate techniques like adversarial training, data augmentation, and loss function modifications to enhance robustness. These advancements are crucial for improving the reliability and generalizability of machine learning models in real-world applications, particularly in safety-critical domains like healthcare and autonomous systems, where data imperfections are common.

Papers

May 10, 2022

Robust Learning of Parsimonious Deep Neural Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
Variational Inference Robust Learning Structured Pruning Variational Posterior Parsimonious Machine Learning Gaussian Scale Mixture

April 12, 2022

A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks
Thomas F. Tiotto, Jelmer P. Borst, Niels A. Taatgen
Neural Network Supervised Learning Robust Learning Memristive Device Brain Inspired Computing Memristive Synapsis

April 7, 2022

Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
Carl Qi, Pieter Abbeel, Aditya Grover
Reinforcement Learning Imitation Learning Inverse Reinforcement Learning Robust Learning Noisy Demonstration Imitation Policy Decision Time Planning

March 2, 2022

Adversarially Robust Learning with Tolerance
Hassan Ashtiani, Vinayak Pathak, Ruth Urner
Adversarial Robustness Robust Learning Robust PAC

February 22, 2022

Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statistics
Kaan Gokcesu, Hakan Gokcesu
Robust Learning Convex Loss General Nonconvex Robust Loss Function Huber Loss Exponential Convergence Pseudo Stochastic

February 12, 2022

Robust Learning from Observation with Model Misspecification
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban, Craig Innes, Subramanian Ramamoorthy, Adrian Weller
Imitation Learning Robust Learning Robust Reinforcement Learning Model Misspecification Observation Centric Adversarial Imitation Generative Adversarial Imitation Learning

February 11, 2022

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang
Native Robustness Adversarial Robustness Robust Learning New Perspective

February 8, 2022

Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Jörn-Henrik Jacobsen
Robust Learning Expert Model Hybrid Modeling Expert Augmentation

January 31, 2022

Robust supervised learning with coordinate gradient descent
Stéphane Gaïffas, Ibrahim Merad
LeArning Abstract Robust Version Robust Learning Robust Estimation Coordinate Descent Robust Learning Method

January 10, 2022

Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang
Native Robustness Robust Learning Robust Estimation Non Asymptotic Unbounded Variance Truncated M Estimator

December 27, 2021

Learning Robust and Lightweight Model through Separable Structured Transformations
Xian Wei, Yanhui Huang, Yangyu Xu, Mingsong Chen, Hai Lan, Yuanxiang Li, Zhongfeng Wang, Xuan Tang
Convolutional Neural Network Deep Learning Model Deep Model Robust Learning Lightweight Model Fully Connected Layer Structural Transformation

December 23, 2021

Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
Matej Grcić, Petra Bevandić, Zoran Kalafatić, Siniša Šegvić
Distribution Detection Robust Learning Dense Prediction

December 22, 2021

Robust learning of data anomalies with analytically-solvable entropic outlier sparsification
Illia Horenko
Robust Learning Non Gaussian Anomalous Event Unsupervised Anomaly Error Minimizing Noise

December 12, 2021

Fairness for Robust Learning to Rank
Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian Ziebart
Procedural Fairness Group Fairness Robust Learning Stable Rank Fairness Constraint Distributional Robustness Proportional Ranking Minimax Game

December 6, 2021

Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
Mingcai Chen, Hao Cheng, Yuntao Du, Ming Xu, Wenyu Jiang, Chongjun Wang
LeArning Abstract Pseudo Label Noisy Label Label Noise Robust Learning Human Answer Mistake Confirmation Bias

November 12, 2021

Adversarially Robust Learning for Security-Constrained Optimal Power Flow
Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry Pileggi, J. Zico Kolter
Robust Learning Robust Training Optimal Power Flow Minimax Optimization Problem

November 10, 2021

Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool, Nidhal C. Bouaynaya, Lyudmila Mihaylova
Deep Neural Network Variational Inference Robust Learning Adversarial Environment Variational Distribution Deep Bayesian

November 9, 2021

Constrained Instance and Class Reweighting for Robust Learning under Label Noise
Abhishek Kumar, Ehsan Amid
Deep Neural Network Pseudo Label Supervised Learning Label Noise Robust Learning Importance Weight

November 8, 2021