Yash Sharma

Affiliations:

University of Tübingen, Germany
International Max Planck Research School for Intelligent Systems, Germany

According to our database¹, Yash Sharma authored at least 30 papers between 2017 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance.

[BibT_eX]

[DOI]

CoRR, 2024

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies.

[BibT_eX]

[DOI]

CoRR, 2024

2023

nl-causal-representations.

[BibT_eX]

[DOI]

Dataset, April, 2023

Jacobian-based Causal Discovery with Nonlinear ICA.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Provably Learning Object-Centric Representations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

nl-causal-representations.

[BibT_eX]

[DOI]

Dataset, August, 2022

How Adversarial Robustness Transfers from Pre-training to Downstream Tasks.

[BibT_eX]

[DOI]

Laura Fee Nern

Yash Sharma

CoRR, 2022

Pixel-level Correspondence for Self-Supervised Learning from Video.

[BibT_eX]

[DOI]

CoRR, 2022

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA.

[BibT_eX]

[DOI]

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Benchmarking Unsupervised Object Representations for Video Sequences.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unsupervised Learning of Compositional Energy Concepts.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Contrastive Learning Inverts the Data Generating Process.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Spatially Structured Recurrent Modules.

[BibT_eX]

[DOI]

Nasim Rahaman

Anirudh Goyal

Muhammad Waleed Gondal

Proceedings of the 9th International Conference on Learning Representations, 2021

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

S2RMs: Spatially Structured Recurrent Modules.

[BibT_eX]

[DOI]

Nasim Rahaman

Anirudh Goyal

Muhammad Waleed Gondal

CoRR, 2020

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences.

[BibT_eX]

[DOI]

CoRR, 2020

MMA Training: Direct Input Space Margin Maximization through Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

On the Effectiveness of Low Frequency Perturbations.

[BibT_eX]

[DOI]

Yash Sharma

Gavin Weiguang Ding

Marcus A. Brubaker

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Are Generative Classifiers More Robust to Adversarial Attacks?

[BibT_eX]

[DOI]

Yingzhen Li

John Bradshaw

Yash Sharma

Proceedings of the 36th International Conference on Machine Learning, 2019

GenAttack: practical black-box attacks with gradient-free optimization.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

2018

Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2018

CAAD 2018: Generating Transferable Adversarial Examples.

[BibT_eX]

[DOI]

Yash Sharma

Tien-Dung Le

Moustafa Alzantot

CoRR, 2018

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization.

[BibT_eX]

[DOI]

CoRR, 2018

Bypassing Feature Squeezing by Increasing Adversary Strength.

[BibT_eX]

[DOI]

Yash Sharma

Pin-Yu Chen

CoRR, 2018

Attacking the Madry Defense Model with $L_1$-based Adversarial Examples.

[BibT_eX]

[DOI]

Yash Sharma

Pin-Yu Chen

Proceedings of the 6th International Conference on Learning Representations, 2018

Generating Natural Language Adversarial Examples.

[BibT_eX]

[DOI]

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Attacking the Madry Defense Model with L<sub>1</sub>-based Adversarial Examples.

[BibT_eX]

[DOI]

Yash Sharma

Pin-Yu Chen

CoRR, 2017

ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models.

[BibT_eX]

[DOI]

Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

Yash Sharma

Timeline

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