Shobha Venkataraman — accepted AI-conference papers

2022

Theseus: A Library for Differentiable Nonlinear Optimization

Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky T. Q. Chen +7

NeurIPS 2022accept

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision. Existing DNLS implementations are application specific an…

Cited by 107 Source ↗PDF ↗Scholar ↗

2021

CrypTen: Secure Multi-Party Computation Meets Machine Learning

Brian Knott, Shobha Venkataraman, Awni Hannun, Shubhabrata Sengupta, Mark Ibrahim, Laurens van der Maaten

NeurIPS 2021poster

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation o…

Cited by 456 Source ↗PDF ↗Scholar ↗