• Home
  • Papers
  • Courses
  • Readings
  • Notes
  • Classics
  • Calendar
  • Courses
    • Introduction to Machine Learning
  • Publications
    • Last-iterate Convergence in Regularized Graphon Mean Field Game
    • Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
    • Disguised Copyright Infringement of Latent Diffusion Models
    • Faster Approximation of Probabilistic and Distributional Values via Least Squares
    • Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors
    • Noise-Aware Aggregation for Heterogeneous Differentially Private Federated Learning
    • One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently
    • $f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning
    • A Unifying Framework for Federated Learning
    • Batchnorm Allows Unsupervised Radial Attacks
    • CM-GAN: Stabilizing GAN Training with Consistency Models
    • Distilling the Knowledge in Diffusion Models
    • Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks
    • Functional Rényi Differential Privacy for Generative Modeling
    • MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenome-assembled genomes
    • Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality
    • Operator Selection and Ordering in a Pipeline Approach to Efficiency Optimizations for Transformers
    • Proportional Fairness in Federated Learning
    • Robust Data Valuation with Weighted Banzhaf Values
    • Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
    • Conditional Generative Quantile Networks via Optimal Transport
    • FedMGDA+: Federated Learning meets Multi-objective Optimization
    • Indiscriminate Data Poisoning Attacks on Neural Networks
    • Network Comparison with Interpretable Contrastive Network Representation Learning
    • Optimality and Stability in Non-Convex Smooth Games
    • Revisiting flow generative models for Out-of-distribution detection
    • Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training
    • BERxiT: Better-fine-tuned and Wider-applicable Early Exit for *BERT
    • Demystifying and Generalizing BinaryConnect
    • Newton-type Methods for Minimax Optimization
    • Posterior Differential Regularization with $f$-divergence for Improving Model Robustness
    • Quantifying and Improving Transferability in Domain Generalization
    • S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
    • The Art of Abstention: Selective Prediction and Error Regularization for Natural Language Processing
    • A novel neoantigen discovery approach based on chromatin high order conformation
    • Convergence of Gradient Methods on Bilinear Zero-Sum Games
    • Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
    • DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
    • DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning
    • Density Deconvolution with Normalizing Flows
    • Early Exiting BERT for Efficient Document Ranking
    • On Minimax Optimality of GANs for Robust Mean Estimation
    • Problems and Opportunities in Training Deep-Learning Software Systems: An Analysis of Variance
    • Showing Your Work Doesn't Always Work
    • Stronger and Faster Wasserstein Adversarial Attacks
    • Tails of Lipschitz Triangular Flows
    • Unsupervised Multilingual Alignment using Wasserstein Barycenters
    • A Penalized Regression Model for the Joint Estimation of eQTL Associations and Gene Network Structure
    • Least-Squares Estimation of Weakly Convex Functions
    • Multivariate Triangular Quantile Maps for Novelty Detection
    • Sum-of-squares Polynomial Flow
    • Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin
    • What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons
    • Deep Homogeneous Mixture Models: Representation, Separation and Approximation
    • Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
    • Inductive Two-Layer Modeling with Parametric Bregman Transfer
    • Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design
    • Analyzable Diversity-Promoting Latent Space Models
    • Bregman Divergence for Stochastic Variance Reduction Methods: Adversarial Prediction and Saddle-Point Problems
    • Convex-constrained Sparse Additive Modeling and Its Extensions
    • Dropout with Expectation-Linear Regularization
    • Efficient Multiple Instance Metric Learning using Weakly Supervised Data
    • Generalized Conditional Gradient for Sparse Estimation
    • Inference of Multiple-wave Population Admixture by Modeling Decay of Linkage Disequilibrium With Polynomial Functions
    • Robust Top-$k$ Multiclass SVM for Visual Category Recognition
    • Semantic Pooling for Complex Event Analysis in Untrimmed Videos
    • Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
    • Closed-Form Training of Mahalanobis Distance for Supervised Clustering
    • Convex Two-Layer Modeling with Latent Structure
    • Exact Algorithms for Isotonic Regression and Related
    • Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting
    • On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System
    • Scalable and Sound Low-Rank Tensor Learning
    • They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers
    • Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM
    • Linear Time Samplers for Supervised Topic Models using Compositional Proposals
    • Minimizing Nonconvex Non-Separable Functions
    • Online Learning and Optimization
    • Petuum: A New Platform for Distributed Machine Learning on Big Data
    • Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision
    • Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection
    • Efficient Structured Matrix Rank Minimization
    • Better Approximation and Faster Algorithm Using the Proximal Average
    • Characterizing the Representer Theorem
    • On Decomposing the Proximal Map
    • Polar Operators for Structured Sparse Estimation
    • A Polynomial-time Form of Robust Regression
    • Accelerated Training for Matrix-Norm Regularization: A Boosting Approach
    • Analysis of Kernel Mean Matching under Covariate Shift
    • Convex Multi-view Subspace Learning
    • Regularizers versus Losses for Nonlinear Dimensionality Reduction
    • Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions
    • Distance Metric Learning by Minimal Distance Maximization
    • Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering
    • Relaxed Clipping: A Global Training Method for Robust Regression and Classification
    • A Conditional Value-at-Risk Approach for Uncertain Markov Decision Processes
    • A General Projection Property for Distribution Families
    • Online TD(1) Meets Offline Monte Carlo
  • Recorded Readings
    • The Random Walk Guide to Investing
    • In A Flight of Starlings
    • Eye of the Hurricane
  • Some Classic Papers
  • Some Notes

Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin

Jan 1, 2019·
K. Wu
,
Y. Yu
· 0 min read
Cite URL
Type
Book section
Publication
NeurIPS Workshop on Maching Learning with Guarantees
Last updated on Jan 1, 2019
Workshop
← Sum-of-squares Polynomial Flow Jan 1, 2019
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons Jan 1, 2019 →

© 2024-2025 Yaoliang Yu. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.