activity
20232025
most citedSense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing

4 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models

Sina Tayebati, Divake Kumar, Nastaran Darabi +3

Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliab…

cs.LG2025

SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs

Dinithi Jayasuriya, Sina Tayebati, Davide Ettori +2

We propose SPARC, a lightweight continual learning framework for large language models (LLMs) that enables efficient task adaptation through prompt tuning in a lower-dimensional sp…

cs.RO20251 cited

Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges

Amit Ranjan Trivedi, Sina Tayebati, Hemant Kumawat +9

Autonomous edge computing in robotics, smart cities, and autonomous vehicles relies on the seamless integration of sensing, processing, and actuation for real-time decision-making…

cs.CV20244 cited

Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing

Sina Tayebati, Theja Tulabandhula, Amit R. Trivedi

In this work, we propose a disruptively frugal LiDAR perception dataflow that generates rather than senses parts of the environment that are either predictable based on the extensi…

cs.RO20232 cited

STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy

Nastaran Darabi, Sina Tayebati, Sureshkumar S. +3

Complex sensors such as LiDAR, RADAR, and event cameras have proliferated in autonomous robotics to enhance perception and understanding of the environment. Meanwhile, these sensor…