ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. - Pin-Yu Chen; Huan Zhang 001; Yash Sharma 001; Jinfeng Yi; Cho-Jui Hsieh (AISec@CCS, 2017)

Attacking the Madry Defense Model with L1-based Adversarial Examples. - Yash Sharma 001; Pin-Yu Chen (CoRR, 2017)

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. - Pin-Yu Chen; Yash Sharma 001; Huan Zhang 001; Jinfeng Yi; Cho-Jui Hsieh (AAAI, 2018)

Generating Natural Language Adversarial Examples. - Moustafa Alzantot; Yash Sharma 001; Ahmed Elgohary; Bo-Jhang Ho; Mani B. Srivastava; Kai-Wei Chang (EMNLP, 2018)

Attacking the Madry Defense Model with $L_1$-based Adversarial Examples. - Yash Sharma 001; Pin-Yu Chen (ICLR (Workshop), 2018)

Bypassing Feature Squeezing by Increasing Adversary Strength. - Yash Sharma 001; Pin-Yu Chen (CoRR, 2018)

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization. - Moustafa Alzantot; Yash Sharma 001; Supriyo Chakraborty; Mani B. Srivastava (CoRR, 2018)

CAAD 2018: Generating Transferable Adversarial Examples. - Yash Sharma 001; Tien-Dung Le; Moustafa Alzantot (CoRR, 2018)

Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training. - Gavin Weiguang Ding; Yash Sharma 001; Kry Yik Chau Lui; Ruitong Huang (CoRR, 2018)

GenAttack: practical black-box attacks with gradient-free optimization. - Moustafa Alzantot; Yash Sharma 001; Supriyo Chakraborty; Huan Zhang 001; Cho-Jui Hsieh; Mani B. Srivastava (GECCO, 2019)

Are Generative Classifiers More Robust to Adversarial Attacks? - Yingzhen Li; John Bradshaw; Yash Sharma 001 (ICML, 2019)

On the Effectiveness of Low Frequency Perturbations. - Yash Sharma 001; Gavin Weiguang Ding; Marcus A. Brubaker (IJCAI, 2019)

MMA Training: Direct Input Space Margin Maximization through Adversarial Training. - Gavin Weiguang Ding; Yash Sharma 001; Kry Yik Chau Lui; Ruitong Huang (ICLR, 2020)

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences. - Marissa A. Weis; Kashyap Chitta; Yash Sharma 001; Wieland Brendel; Matthias Bethge; Andreas Geiger 001; Alexander S. Ecker (CoRR, 2020)

S2RMs: Spatially Structured Recurrent Modules. - Nasim Rahaman; Anirudh Goyal; Muhammad Waleed Gondal; Manuel Wuthrich; Stefan Bauer; Yash Sharma 001; Yoshua Bengio; Bernhard Schölkopf (CoRR, 2020)

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding. - David A. Klindt; Lukas Schott; Yash Sharma 001; Ivan Ustyuzhaninov; Wieland Brendel; Matthias Bethge; Dylan M. Paiton (ICLR, 2021)

Spatially Structured Recurrent Modules. - Nasim Rahaman; Anirudh Goyal; Muhammad Waleed Gondal; Manuel Wuthrich; Stefan Bauer; Yash Sharma 001; Yoshua Bengio; Bernhard Schölkopf (ICLR, 2021)

Contrastive Learning Inverts the Data Generating Process. - Roland S. Zimmermann; Yash Sharma 001; Steffen Schneider 001; Matthias Bethge; Wieland Brendel (ICML, 2021)

Unsupervised Learning of Compositional Energy Concepts. - Yilun Du; Shuang Li 013; Yash Sharma 001; Josh Tenenbaum 001; Igor Mordatch (NeurIPS, 2021)

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. - Julius von Kügelgen; Yash Sharma 001; Luigi Gresele; Wieland Brendel; Bernhard Schölkopf; Michel Besserve; Francesco Locatello (NeurIPS, 2021)

Benchmarking Unsupervised Object Representations for Video Sequences. - Marissa A. Weis; Kashyap Chitta; Yash Sharma 001; Wieland Brendel; Matthias Bethge; Andreas Geiger 001; Alexander S. Ecker (J. Mach. Learn. Res., 2021)

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. - Sébastien Lachapelle; Pau Rodríguez; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CLeaR, 2022)

Pixel-level Correspondence for Self-Supervised Learning from Video. - Yash Sharma 001; Yi Zhu; Chris Russell 001; Thomas Brox (CoRR, 2022)

How Adversarial Robustness Transfers from Pre-training to Downstream Tasks. - Laura Fee Nern; Yash Sharma 001 (CoRR, 2022)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2022)

Provably Learning Object-Centric Representations. - Jack Brady; Roland S. Zimmermann; Yash Sharma 001; Bernhard Schölkopf; Julius von Kügelgen; Wieland Brendel (ICML, 2023)

Jacobian-based Causal Discovery with Nonlinear ICA. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Trans. Mach. Learn. Res., 2023)

nl-causal-representations. - Patrik Reizinger; Yash Sharma 001; Matthias Bethge; Bernhard Schölkopf; Ferenc Huszár; Wieland Brendel (Zenodo, 2023)

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance. - Vishaal Udandarao; Ameya Prabhu; Adhiraj Ghosh; Yash Sharma 001; Philip Torr 001; Adel Bibi; Samuel Albanie; Matthias Bethge (NeurIPS, 2024)

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. - Sébastien Lachapelle; Pau Rodríguez López; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CoRR, 2024)

Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks. - Thaddäus Wiedemer; Yash Sharma 001; Ameya Prabhu; Matthias Bethge; Wieland Brendel (CoRR, 2025)