adversarially robust generalization requires more data
play

Adversarially Robust Generalization Requires More Data Ludwig - PowerPoint PPT Presentation

Dec 06, 2023 •2.36k likes •2.52k views

Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Poster #31 Kunal Talwar Aleksander M dry Adversarial Examples [Szegedy, Zaremba, Sutskever, Bruna, Erhan, Goodfellow,

  1. Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Poster #31 Kunal Talwar Aleksander M ą dry

  2. Adversarial Examples [Szegedy, Zaremba, Sutskever, Bruna, Erhan, Goodfellow, Fergus, 2013] [Biggio, Corona, Maiorca, Nelson, Srndic, Laskov, Giacinto, Roli, 2013]

  3. Adversarial Examples [Szegedy, Zaremba, Sutskever, Bruna, Erhan, Goodfellow, Fergus, 2013] [Biggio, Corona, Maiorca, Nelson, Srndic, Laskov, Giacinto, Roli, 2013] What makes adversarial examples a hard problem? This paper: perspective on sample complexity

  4. <latexit sha1_base64="TCezhzHnvwDoFMFoQ4EK/L9JVgU=">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</latexit> <latexit sha1_base64="TCezhzHnvwDoFMFoQ4EK/L9JVgU=">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</latexit> <latexit sha1_base64="TCezhzHnvwDoFMFoQ4EK/L9JVgU=">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</latexit> <latexit sha1_base64="TCezhzHnvwDoFMFoQ4EK/L9JVgU=">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</latexit> Standard vs Robust Generalization x,y ∼ D [ loss ( f ( x ) , y ) ] “Standard” Generalization: E

Recommend

A Spectral View of Adversarially Robust Features Shivam Garg Vatsal Sharan * Brian Zhang *

A Spectral View of Adversarially Robust Features Shivam Garg Vatsal Sharan * Brian Zhang *

A Spectral View of Adversarially Robust Features Shivam Garg Vatsal Sharan * Brian Zhang * Gregory Valiant Stanford University What are adversarial examples? Adding small amount of well-crafted noise to the test data fools the classifier More

574 views • 14 slides
Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett,

Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett,

Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, Volkan Cevher Conference on Neural Information Processing Systems (Dec 2018) Gaussian Process Optimization Optimum Non-robust

393 views • 12 slides
Better generalization with less data using robust gradient descent Matthew J. Holland 1 Kazushi

Better generalization with less data using robust gradient descent Matthew J. Holland 1 Kazushi

Better generalization with less data using robust gradient descent Matthew J. Holland 1 Kazushi Ikeda 2 1 Osaka University 2 Nara Institute of Science and Technology Distribution robustness In practice, the learner does not know what kind of data

893 views • 16 slides
Calibrated Surrogate Losses for Adversarially Robust Classification 1 The University of Tokyo

Calibrated Surrogate Losses for Adversarially Robust Classification 1 The University of Tokyo

Calibrated Surrogate Losses for Adversarially Robust Classification 1 The University of Tokyo 2 RIKEN AIP 3 University of Michigan Jul. 9 th - 12 th @ COLT 2020 Han Bao 1,2 Clayton Scott 3 Masashi Sugiyama 2,1 Adversarial Attacks 2

514 views • 16 slides
Optimal Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan, Yuting

Optimal Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan, Yuting

Optimal Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan, Yuting Wei, Pradeep Ravikumar ICML 2020 Computer Science Department, Statistics Department, Machine Learning Department Carnegie Mellon University

403 views • 16 slides
Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio

Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio

Setting - 2 Task Setting - 1 Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations Method Ju-chieh Chou , Cheng-chieh Yeh, Hung-yi Lee, Lin-shan Lee Best student paper award nominated

532 views • 25 slides
Data Anonymization - Generalization Algorithms Li Xiong, Slawek Goryczka CS573 Data Privacy and

Data Anonymization - Generalization Algorithms Li Xiong, Slawek Goryczka CS573 Data Privacy and

Data Anonymization - Generalization Algorithms Li Xiong, Slawek Goryczka CS573 Data Privacy and Anonymity Generalization and Suppression Generalization Suppression Replace the value with a less Do not release a Z2 = {410**}

1.27k views • 52 slides
Making Generalization Robust Katrina Ligett HUJI &amp; Caltech joint with Rachel Cummings, Kobbi

Making Generalization Robust Katrina Ligett HUJI &amp; Caltech joint with Rachel Cummings, Kobbi

Making Generalization Robust Katrina Ligett HUJI &amp; Caltech joint with Rachel Cummings, Kobbi Nissim, Aaron Roth, and Steven Wu A model for science A model for science Hypothesis Learning Alg domain: contains all possible

904 views • 56 slides
Data Anonymization - Generalization Algorithms Li Xiong CS573 Data Privacy and Anonymity

Data Anonymization - Generalization Algorithms Li Xiong CS573 Data Privacy and Anonymity

Data Anonymization - Generalization Algorithms Li Xiong CS573 Data Privacy and Anonymity Generalization and Suppression Generalization Suppression Replace the value with a less Do not release a Z2 = {410**} value at all

790 views • 45 slides
Learning From Data Lecture 7 Approximation Versus Generalization The VC Dimension Approximation

Learning From Data Lecture 7 Approximation Versus Generalization The VC Dimension Approximation

Learning From Data Lecture 7 Approximation Versus Generalization The VC Dimension Approximation Versus Generalization Bias and Variance The Learning Curve M. Magdon-Ismail CSCI 4100/6100 recap: The Vapnik-Chervonenkis Bound (VC Bound) P [ |

353 views • 22 slides
The Common Core World A Generalization about Where Were Headed Phyllis Hinton, Adult Education

The Common Core World A Generalization about Where Were Headed Phyllis Hinton, Adult Education

The Common Core World A Generalization about Where Were Headed Phyllis Hinton, Adult Education and Literacy Consultant July 2013 PD Standard 8: Requires leadership at the state and local levels that promotes effective professional

600 views • 32 slides
Learning From Data Lecture 22 Neural Networks and Overfitting Approximation vs. Generalization

Learning From Data Lecture 22 Neural Networks and Overfitting Approximation vs. Generalization

Learning From Data Lecture 22 Neural Networks and Overfitting Approximation vs. Generalization Regularization and Early Stopping Minimizing E in More Efficienty M. Magdon-Ismail CSCI 4100/6100 recap: Neural Networks and Fitting the Data

464 views • 15 slides
HALI: Hierarchical Adversarially Learned Inference Negar Rostamzadeh ACM Webinar January 18,

HALI: Hierarchical Adversarially Learned Inference Negar Rostamzadeh ACM Webinar January 18,

HALI: Hierarchical Adversarially Learned Inference Negar Rostamzadeh ACM Webinar January 18, 2018 Negar Rostamzadeh HALI January 18, 2018 1 / 43 Hierarchical Adversarially Learned Inference Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier

468 views • 43 slides
Adversarially Learned Representations for Information Obfuscation and Inference Martin Bertran 1

Adversarially Learned Representations for Information Obfuscation and Inference Martin Bertran 1

Adversarially Learned Representations for Information Obfuscation and Inference Martin Bertran 1 , Natalia Martinez 1 , Afroditi Papadaki 2 Qiang Qiu 1 , Miguel Rodrigues 2 , Galen Reeves 1 , Guillermo Sapiro 1 1. Duke University 2. University

1.03k views • 41 slides
On the Generalization of Subspace Detection in Unordered Multidimensional Data Leandro Augusto

On the Generalization of Subspace Detection in Unordered Multidimensional Data Leandro Augusto

On the Generalization of Subspace Detection in Unordered Multidimensional Data Leandro Augusto Frata Fernandes laffernandes@inf.ufrgs.br Manuel Menezes de Oliveira Neto oliveira@inf.ufrgs.br Advisor CG UFRGS Data Alignments 2 Data

1.1k views • 43 slides
Chapter 2 SPATIAL DATA INPUT SPATIAL DATA INPUT Analysis and modeling in a GIS requires input of

Chapter 2 SPATIAL DATA INPUT SPATIAL DATA INPUT Analysis and modeling in a GIS requires input of

Chapter 2 SPATIAL DATA INPUT SPATIAL DATA INPUT Analysis and modeling in a GIS requires input of relevant data. The data consist of two types: spatial data representing geographic features (points, lines and areas) and attribute data (descriptive

550 views • 26 slides
Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2

Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2

Adversarially Regularized Autoencoders Jake Zhao* 1 , 3 Yoon Kim* 2 Kelly Zhang 1 Alexander Rush 2 Yann LeCun 1 , 3 1 NYU CILVR Lab 2 Harvard NLP 3 Facebook AI Research Training Deep Latent Variable Models Two dominant approaches Variational

400 views • 27 slides
Robust Location and Scatter Estimators Outline for Multivariate Data Analysis Background

Robust Location and Scatter Estimators Outline for Multivariate Data Analysis Background

Robust Location and Scatter Estimation Robust Location and Scatter Estimation Robust Location and Scatter Estimators Outline for Multivariate Data Analysis Background and Motivation {robustbase}, {rrcov} Computing the Robust

512 views • 16 slides
What? Discrimination is a generalization of sorting and partitioning that can be defined

What? Discrimination is a generalization of sorting and partitioning that can be defined

Discrimination is Wrong And What We Can Do About It Edward Kmett What? Discrimination is a generalization of sorting and partitioning that can be defined generically by structural recursion. Radix / American Flag Sort for algebraic data

1.18k views • 74 slides
Deep learning: Challenges in learning and generalization Tomas Mikolov, Facebook AI What is

Deep learning: Challenges in learning and generalization Tomas Mikolov, Facebook AI What is

Deep learning: Challenges in learning and generalization Tomas Mikolov, Facebook AI What is generalization? Memorization to remember all training examples (Universal Approximator) 2 + 3 = 5, 3 + 2 = 5, ... Generalization to

281 views • 14 slides
Adversarially Regularized Autoencoders Junbo (Jake) Zhao, Yoon Kim, Kelly Zhang, Alexander M.

Adversarially Regularized Autoencoders Junbo (Jake) Zhao, Yoon Kim, Kelly Zhang, Alexander M.

Adversarially Regularized Autoencoders Junbo (Jake) Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun Wei Zhen Teoh and Mathieu Ravaut 1 Refresh: Adversarial Autoencoder [From Adversarial Autoencoders by Makhzani et al 2015] 2 Some

629 views • 30 slides
Short Course in Supervised Learning Robust Optimization and Machine Learning Robust Supervised

Short Course in Supervised Learning Robust Optimization and Machine Learning Robust Supervised

Robust Optimization &amp; Machine Learning 6. Robust Optimization Short Course in Supervised Learning Robust Optimization and Machine Learning Robust Supervised Learning Motivations Examples Thresholding and robustness Boolean data

724 views • 48 slides
Improving Generalization by Data Categorization Ling Li, Amrit Pratap, Hsuan-Tien Lin, and Yaser

Improving Generalization by Data Categorization Ling Li, Amrit Pratap, Hsuan-Tien Lin, and Yaser

Introduction Concepts Methods Experiments Conclusion Improving Generalization by Data Categorization Ling Li, Amrit Pratap, Hsuan-Tien Lin, and Yaser Abu-Mostafa Learning Systems Group, Caltech ECML/PKDD, October 4, 2005 Introduction

445 views • 18 slides
Generalization of Cycle-Covering Heuristics Clemens B uchner Department of Mathematics and

Generalization of Cycle-Covering Heuristics Clemens B uchner Department of Mathematics and

Generalization of Cycle-Covering Heuristics Masters Thesis Presentation Generalization of Cycle-Covering Heuristics Clemens B uchner Department of Mathematics and Computer Science University of Basel May 14, 2020 Generalization of

877 views • 54 slides

More recommend