Core Stability

Core stability, broadly defined as the robustness and reliability of a system or model's performance under various conditions, is a central theme across diverse scientific fields. Current research focuses on improving stability in machine learning models (e.g., through regularization techniques, modified optimization algorithms like AdamG, and analyses of Jacobian alignment), in inverse problems (using methods like Wasserstein gradient flows and data-dependent regularization), and in dynamical systems (by mitigating data-induced instability). Understanding and enhancing core stability is crucial for building reliable and trustworthy systems across applications ranging from medical imaging and AI to robotics and control systems.

Papers

July 7, 2023

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
Strong Generalization Core Stability Compositional Optimization Uniform Stability

July 5, 2023

Stability of Q-Learning Through Design and Optimism
Sean Meyn
Product Design Q Learning Core Stability Stochastic Approximation Linear Function Approximation Reinforcement Learning Library

June 29, 2023

Improving Online Continual Learning Performance and Stability with Temporal Ensembles
Albin Soutif--Cormerais, Antonio Carta, Joost Van de Weijer
Continual LEArning Catastrophic Forgetting Core Stability Non Stationary Online Continual Learning Continual Learning Performance

June 18, 2023

Towards Stability of Autoregressive Neural Operators
Michael McCabe, Peter Harrington, Shashank Subramanian, Jed Brown
Neural Operator Core Stability Autoregressive Neural Network Spatiotemporal Dynamical System

June 6, 2023

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters
Zhenyu Sun, Xiaochun Niu, Ermin Wei
Strong Generalization Stochastic Gradient Descent Generalization Performance Core Stability Unobserved Heterogeneity Centralized Machine Learning

May 31, 2023

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
Rie Johnson, Tong Zhang
Deep Neural Network Training Data Core Stability Generalization Gap Training Deep Hard to Easy Inconsistency

May 29, 2023

On the Variance, Admissibility, and Stability of Empirical Risk Minimization
Gil Kur, Eli Putterman, Alexander Rakhlin
Core Stability Empirical Risk Minimization Variance Information Square Loss Bias Variance Decomposition

May 26, 2023

May 24, 2023

May 23, 2023

Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency
Dongcheng Zhao, Guobin Shen, Yiting Dong, Yang Li, Yi Zeng
Neural Network System Performance Spiking Neural Network Back Propagation Core Stability Neuromorphic Datasets Temporal Consistency Optimal Neural Network

May 21, 2023

Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack
Christopher Burger, Lingwei Chen, Thai Le
Adversarial Attack Explainable AI Line by Line Explanation Core Stability XAI Method Text Classifier Text Perturbation Text Semantics Dseg Lime Explanation Selection

May 20, 2023

Stability and Generalization of lp-Regularized Stochastic Learning for GCN
Shiyu Liu, Linsen Wei, Shaogao Lv, Ming Li
Strong Generalization Core Stability Sparse Graph Proximal Gradient Descent Smoothness Constraint Graph Smoothing

May 19, 2023

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Gradient Descent Extreme Edge Core Stability Implicit Bias Logistic Regression Logistics System Unbounded Loss

May 11, 2023

Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Early Stage Convergence Core Stability Stochastic Approximation Stochastic Process Recursive Algorithm Borkar Meyn Stability Theorem Arbitrary Delay Cheeger Inequality

May 6, 2023

Maintaining Stability and Plasticity for Predictive Churn Reduction
George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Machine Learning Model Core Stability Plasticity Rule Stability Plasticity Churn Prediction Model Combination

May 5, 2023

Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT
Yu Gao, Xiaochuan Pan, Chong Chen
Core Stability Computational Reconstruction \Bf{T}$ Image Known Solution Point

May 3, 2023

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer
Lichang Chen, Heng Huang, Minhao Cheng
Large Language Model System Performance Prompt Tuning Core Stability Style PROMPT Minimax McTs Variational Regularization

May 2, 2023

Class based Influence Functions for Error Detection
Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Ngoc Nguyen, Anh T. V. Dau, Nghi D. Q. Bui
Deep Network Core Stability True Class Error Detection Influence Function Large Scale Datasets

Core Stability

Papers

Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

Stability of Q-Learning Through Design and Optimism

Improving Online Continual Learning Performance and Stability with Temporal Ensembles

Towards Stability of Autoregressive Neural Operators

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training

On the Variance, Admissibility, and Stability of Empirical Risk Minimization

Improving Stability in Decision Tree Models

Stability of implicit neural networks for long-term forecasting in dynamical systems

Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness

Reconstruction, forecasting, and stability of chaotic dynamics from partial data

Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency

Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack

Stability and Generalization of lp-Regularized Stochastic Learning for GCN

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability

Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays

Maintaining Stability and Plasticity for Predictive Churn Reduction

Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer

Class based Influence Functions for Error Detection