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ORCIDYash Sharma 0001
Person information
- affiliation: University of Tübingen, Germany
- affiliation: International Max Planck Research School for Intelligent Systems, Germany
Other persons with the same name
- Yash Sharma — disambiguation page
- Yash Sharma 0002 — University of Virginia, Charlottesville, VA, USA
- Yash Sharma 0003 — St. Petersburg Electrotechnical University, Russia
- Yash Sharma 0004 — Indian Institute of Technology Bombay, Mumbai, India
- Yash Sharma 0005 — Jaypee Institute of Information Technology, Noida, India
- Yash Sharma 0006 — Shizuoka University, Japan
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2020 – today
- 2024
[i20]Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien:
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. CoRR abs/2401.04890 (2024)- [i19]Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip H. S. Torr, Adel Bibi, Samuel Albanie, Matthias Bethge:
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance. CoRR abs/2404.04125 (2024) - 2023
- [j2]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. 2023 (2023) - [c15]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. ICML 2023: 3038-3062 - [d2]Patrik Reizinger
, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version v1.0.1. Zenodo, 2023 [all versions]
- [i18]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. CoRR abs/2305.14229 (2023) - 2022
- [c14]Sébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien:
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. CLeaR 2022: 428-484 - [d1]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version 1.0.0. Zenodo, 2022 [all versions] - [i17]Yash Sharma, Yi Zhu, Chris Russell, Thomas Brox:
Pixel-level Correspondence for Self-Supervised Learning from Video. CoRR abs/2207.03866 (2022) - [i16]Laura Fee Nern, Yash Sharma:
How Adversarial Robustness Transfers from Pre-training to Downstream Tasks. CoRR abs/2208.03835 (2022) - 2021
- [j1]Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker:
Benchmarking Unsupervised Object Representations for Video Sequences. J. Mach. Learn. Res. 22: 183:1-183:61 (2021) - [c13]David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton:
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. ICLR 2021 - [c12]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
Spatially Structured Recurrent Modules. ICLR 2021 - [c11]Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel:
Contrastive Learning Inverts the Data Generating Process. ICML 2021: 12979-12990 - [c10]Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, Igor Mordatch:
Unsupervised Learning of Compositional Energy Concepts. NeurIPS 2021: 15608-15620 - [c9]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS 2021: 16451-16467 - [i15]Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel:
Contrastive Learning Inverts the Data Generating Process. CoRR abs/2102.08850 (2021) - [i14]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR abs/2106.04619 (2021) - [i13]Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch:
Unsupervised Learning of Compositional Energy Concepts. CoRR abs/2111.03042 (2021) - 2020
- [c8]Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang:
MMA Training: Direct Input Space Margin Maximization through Adversarial Training. ICLR 2020 - [i12]Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker:
Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences. CoRR abs/2006.07034 (2020) - [i11]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
S2RMs: Spatially Structured Recurrent Modules. CoRR abs/2007.06533 (2020) - [i10]David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan M. Paiton:
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. CoRR abs/2007.10930 (2020)
2010 – 2019
- 2019
- [c7]Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Huan Zhang, Cho-Jui Hsieh, Mani B. Srivastava:
GenAttack: practical black-box attacks with gradient-free optimization. GECCO 2019: 1111-1119 - [c6]Yingzhen Li, John Bradshaw, Yash Sharma:
Are Generative Classifiers More Robust to Adversarial Attacks? ICML 2019: 3804-3814 - [c5]Yash Sharma, Gavin Weiguang Ding, Marcus A. Brubaker:
On the Effectiveness of Low Frequency Perturbations. IJCAI 2019: 3389-3396 - [i9]Yash Sharma, Gavin Weiguang Ding, Marcus A. Brubaker:
On the Effectiveness of Low Frequency Perturbations. CoRR abs/1903.00073 (2019) - 2018
- [c4]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. AAAI 2018: 10-17 - [c3]Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani B. Srivastava, Kai-Wei Chang:
Generating Natural Language Adversarial Examples. EMNLP 2018: 2890-2896 - [c2]Yash Sharma, Pin-Yu Chen:
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples. ICLR (Workshop) 2018 - [i8]Yash Sharma, Pin-Yu Chen:
Bypassing Feature Squeezing by Increasing Adversary Strength. CoRR abs/1803.09868 (2018) - [i7]Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani B. Srivastava, Kai-Wei Chang:
Generating Natural Language Adversarial Examples. CoRR abs/1804.07998 (2018) - [i6]Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, Mani B. Srivastava:
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization. CoRR abs/1805.11090 (2018) - [i5]Yash Sharma, Tien-Dung Le, Moustafa Alzantot:
CAAD 2018: Generating Transferable Adversarial Examples. CoRR abs/1810.01268 (2018) - [i4]Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, Ruitong Huang:
Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training. CoRR abs/1812.02637 (2018) - 2017
- [c1]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. AISec@CCS 2017: 15-26 - [i3]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models. CoRR abs/1708.03999 (2017) - [i2]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. CoRR abs/1709.04114 (2017) - [i1]Yash Sharma, Pin-Yu Chen:
Attacking the Madry Defense Model with L1-based Adversarial Examples. CoRR abs/1710.10733 (2017)
Coauthor Index
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