Daniela Rus

Scalable Secure Computation of Statistical Functions with Applications to k-Nearest Neighbors.

2018 · arXiv (Cornell University)

Given a set $S$ of $n$ $d$-dimensional points, the $k$-nearest neighbors (KNN) is the problem of quickly finding $k$ points in $S$ that are nearest to a query point $q$. The $k$-nearest neighbors problem has …

Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars.

2021 · arXiv (Cornell University)

Despite the rich theoretical foundation of model-based deep reinforcement learning (RL) agents, their effectiveness in real-world robotics-applications is less studied and understood. In this paper, we, therefore, investigate how such agents generalize to real-world autonomous-vehicle …

BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control

2023 · IEEE Transactions on Robotics

Many safety-critical applications of neural networks, such as robotic control, require safety guarantees. This article introduces a method for ensuring the safety of learned models for control using differentiable control barrier functions (dCBFs). dCBFs are …

Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly …

TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding

2025

Zhaoxuan Wu, Zijian Zhou, Arun Verma, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.