Zobrazeno 1 - 10
of 31 366
pro vyhledávání: '"Swamy, A"'
Adversarial Imitation Learning is traditionally framed as a two-player zero-sum game between a learner and an adversarially chosen cost function, and can therefore be thought of as the sequential generalization of a Generative Adversarial Network (GA
Externí odkaz:
http://arxiv.org/abs/2410.13855
Autor:
Gao, Zhaolin, Zhan, Wenhao, Chang, Jonathan D., Swamy, Gokul, Brantley, Kianté, Lee, Jason D., Sun, Wen
Large Language Models (LLMs) have achieved remarkable success at tasks like summarization that involve a single turn of interaction. However, they can still struggle with multi-turn tasks like dialogue that require long-term planning. Previous works
Externí odkaz:
http://arxiv.org/abs/2410.04612
Autor:
Rao, Nikitha, Gilbert, Elizabeth, Ramananandro, Tahina, Swamy, Nikhil, Goues, Claire Le, Fakhoury, Sarah
Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol parsers, and language runtimes. Specifications for such systems are o
Externí odkaz:
http://arxiv.org/abs/2410.04249
Autor:
Swamy, Vinitra, Romano, Davide, Desikan, Bhargav Srinivasa, Camburu, Oana-Maria, Käser, Tanja
Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art AI models are understandable for non-technical users such as educators and students. In response, we introduc
Externí odkaz:
http://arxiv.org/abs/2409.08027
We consider the {\em correlated knapsack orienteering} (CSKO) problem: we are given a travel budget $B$, processing-time budget $W$, finite metric space $(V,d)$ with root $\rho\in V$, where each vertex is associated with a job with possibly correlate
Externí odkaz:
http://arxiv.org/abs/2408.16566
Autor:
Guan, Hong, Wang, Yancheng, Xie, Lulu, Nag, Soham, Goel, Rajeev, Swamy, Niranjan Erappa Narayana, Yang, Yingzhen, Xiao, Chaowei, Prisby, Jonathan, Maciejewski, Ross, Zou, Jia
Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms. The training of accurate
Externí odkaz:
http://arxiv.org/abs/2408.01690
Recent advancements in deep learning (DL) have significantly advanced medical image analysis. In the field of medical image processing, particularly in histopathology image analysis, the variation in staining protocols and differences in scanners pre
Externí odkaz:
http://arxiv.org/abs/2406.15685
Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g. demonstrations collected in simulation but deployment in the real wo
Externí odkaz:
http://arxiv.org/abs/2406.11905
Graph partitioning (GP) and vertex connectivity have traditionally been two distinct fields of study. This paper introduces the highly connected graph partitioning (HCGP) problem, which partitions a graph into compact, size balanced, and $Q$-(vertex)
Externí odkaz:
http://arxiv.org/abs/2406.08329
We study a multi-agent imitation learning (MAIL) problem where we take the perspective of a learner attempting to coordinate a group of agents based on demonstrations of an expert doing so. Most prior work in MAIL essentially reduces the problem to m
Externí odkaz:
http://arxiv.org/abs/2406.04219