Zobrazeno 1 - 10
of 55 706
pro vyhledávání: '"A. Krishnamurthy"'
Autor:
Yin, George, Krishnamurthy, Vikram
We analyze the finite sample regret of a decreasing step size stochastic gradient algorithm. We assume correlated noise and use a perturbed Lyapunov function as a systematic approach for the analysis. Finally we analyze the escape time of the iterate
Externí odkaz:
http://arxiv.org/abs/2410.08449
Autor:
Krishnamurthy, Vikram, Rojas, Cristian
We consider word-of-mouth social learning involving $m$ Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of
Externí odkaz:
http://arxiv.org/abs/2410.08447
Autor:
Devarakonda, Venkata Naren, Goswami, Raktim Gautam, Kaypak, Ali Umut, Patel, Naman, Khorrambakht, Rooholla, Krishnamurthy, Prashanth, Khorrami, Farshad
Enabling robots to autonomously navigate unknown, complex, dynamic environments and perform diverse tasks remains a fundamental challenge in developing robust autonomous physical agents. They must effectively perceive their surroundings while leverag
Externí odkaz:
http://arxiv.org/abs/2410.06239
LLMs are increasingly being used in workflows involving generating content to be consumed by humans (e.g., marketing) and also in directly interacting with humans (e.g., through chatbots). The development of such systems that are capable of generatin
Externí odkaz:
http://arxiv.org/abs/2410.02653
Autor:
Ghazanfari, Sara, Araujo, Alexandre, Krishnamurthy, Prashanth, Garg, Siddharth, Khorrami, Farshad
Multi-modal Large Language Models (MLLMs) have recently exhibited impressive general-purpose capabilities by leveraging vision foundation models to encode the core concepts of images into representations. These are then combined with instructions and
Externí odkaz:
http://arxiv.org/abs/2410.02080
Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hi
Externí odkaz:
http://arxiv.org/abs/2410.00702
Autor:
Tali, Ronak, Rabeh, Ali, Yang, Cheng-Hau, Shadkhah, Mehdi, Karki, Samundra, Upadhyaya, Abhisek, Dhakshinamoorthy, Suriya, Saadati, Marjan, Sarkar, Soumik, Krishnamurthy, Adarsh, Hegde, Chinmay, Balu, Aditya, Ganapathysubramanian, Baskar
Simulating fluid flow around arbitrary shapes is key to solving various engineering problems. However, simulating flow physics across complex geometries remains numerically challenging and computationally resource-intensive, particularly when using c
Externí odkaz:
http://arxiv.org/abs/2409.18032
Autor:
Devarakonda, Venkata Naren, Kaypak, Ali Umut, Yuan, Shuaihang, Krishnamurthy, Prashanth, Fang, Yi, Khorrami, Farshad
LLMs have shown promising results in task planning due to their strong natural language understanding and reasoning capabilities. However, issues such as hallucinations, ambiguities in human instructions, environmental constraints, and limitations in
Externí odkaz:
http://arxiv.org/abs/2409.16455
Autor:
Abramovich, Talor, Udeshi, Meet, Shao, Minghao, Lieret, Kilian, Xi, Haoran, Milner, Kimberly, Jancheska, Sofija, Yang, John, Jimenez, Carlos E., Khorrami, Farshad, Krishnamurthy, Prashanth, Dolan-Gavitt, Brendan, Shafique, Muhammad, Narasimhan, Karthik, Karri, Ramesh, Press, Ofir
Although language model (LM) agents are demonstrating growing potential in many domains, their success in cybersecurity has been limited due to simplistic design and the lack of fundamental features for this domain. We present EnIGMA, an LM agent for
Externí odkaz:
http://arxiv.org/abs/2409.16165
Autor:
Snow, Luke, Krishnamurthy, Vikram
We derive a minimax distributionally robust inverse reinforcement learning (IRL) algorithm to reconstruct the utility functions of a multi-agent sensing system. Specifically, we construct utility estimators which minimize the worst-case prediction er
Externí odkaz:
http://arxiv.org/abs/2409.14542