Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images. - Tribhuvanesh Orekondy; Bernt Schiele; Mario Fritz (ICCV, 2017)

Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images. - Tribhuvanesh Orekondy; Mario Fritz; Bernt Schiele (CVPR, 2018)

Understanding and Controlling User Linkability in Decentralized Learning. - Tribhuvanesh Orekondy; Seong Joon Oh; Bernt Schiele; Mario Fritz (CoRR, 2018)

Knockoff Nets: Stealing Functionality of Black-Box Models. - Tribhuvanesh Orekondy; Bernt Schiele; Mario Fritz (CVPR, 2019)

Prediction Poisoning: Utility-Constrained Defenses Against Model Stealing Attacks. - Tribhuvanesh Orekondy; Bernt Schiele; Mario Fritz (CoRR, 2019)

Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks. - Tribhuvanesh Orekondy; Bernt Schiele; Mario Fritz (ICLR, 2020)

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. - Dingfan Chen; Tribhuvanesh Orekondy; Mario Fritz (NeurIPS, 2020)

Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries. - Shadi Rahimian; Tribhuvanesh Orekondy; Mario Fritz (CoRR, 2020)

Understanding and controlling leakage in machine learning. - Tribhuvanesh Orekondy (2020)

Differential Privacy Defenses and Sampling Attacks for Membership Inference. - Shadi Rahimian; Tribhuvanesh Orekondy; Mario Fritz (AISec@CCS, 2021)

InfoScrub: Towards Attribute Privacy by Targeted Obfuscation. - Hui-Po Wang; Tribhuvanesh Orekondy; Mario Fritz (CVPR Workshops, 2021)

MIMO-GAN: Generative MIMO Channel Modeling. - Tribhuvanesh Orekondy; Arash Behboodi; Joseph B. Soriaga (ICC, 2022)

Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps. - Thomas M. Hehn; Tribhuvanesh Orekondy; Ori Shental; Arash Behboodi; Juan Bucheli; Akash S. Doshi; June Namgoong; Taesang Yoo; Ashwin Sampath; Joseph B. Soriaga (GLOBECOM, 2023)

WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations. - Tribhuvanesh Orekondy; Kumar Pratik; Shreya Kadambi; Hao Ye; Joseph Soriaga; Arash Behboodi (ICLR, 2023)

Simulating, Fast and Slow: Learning Policies for Black-Box Optimization. - Fabio Valerio Massoli; Tim Bakker; Thomas Hehn 001; Tribhuvanesh Orekondy; Arash Behboodi (CoRR, 2024)

Probabilistic and Differentiable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (CoRR, 2024)

Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems. - Gianluigi Silvestri; Fabio Valerio Massoli; Tribhuvanesh Orekondy; Afshin Abdi; Arash Behboodi (CoRR, 2024)

Differentiable and Learnable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (ICLR, 2025)