Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Dillig, Isil"'
Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by leveraging a backup policy to ensure safety when the learned policy att
Externí odkaz:
http://arxiv.org/abs/2405.13863
Publikováno v:
Proc. ACM Program. Lang. 8, PLDI, Article 188 (June 2024), 32 pages
Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online stre
Externí odkaz:
http://arxiv.org/abs/2404.04743
This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e.g., "good parking spot", "convenient drop-off location") from visual input. Despite its similarity to learning factual concepts (e.g., "red cube
Externí odkaz:
http://arxiv.org/abs/2403.16689
Due to the availability of increasingly large amounts of visual data, there is a growing need for tools that can help users find relevant images. While existing tools can perform image retrieval based on similarity or metadata, they fall short in sce
Externí odkaz:
http://arxiv.org/abs/2401.10464
Autor:
Saxena, Divyanshu, Sharma, Nihal, Kim, Donghyun, Dwivedula, Rohit, Chen, Jiayi, Yang, Chenxi, Ravula, Sriram, Hu, Zichao, Akella, Aditya, Angel, Sebastian, Biswas, Joydeep, Chaudhuri, Swarat, Dillig, Isil, Dimakis, Alex, Godfrey, P. Brighten, Kim, Daehyeok, Rossbach, Chris, Wang, Gang
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are
Externí odkaz:
http://arxiv.org/abs/2312.07813
Developers often dedicate significant time to maintaining and refactoring existing code. However, most prior work on generative models for code focuses solely on creating new code, overlooking the distinctive needs of editing existing code. In this w
Externí odkaz:
http://arxiv.org/abs/2305.18584
Many data extraction tasks of practical relevance require not only syntactic pattern matching but also semantic reasoning about the content of the underlying text. While regular expressions are very well suited for tasks that require only syntactic p
Externí odkaz:
http://arxiv.org/abs/2305.10401
Prior work has combined chain-of-thought prompting in large language models (LLMs) with programmatic representations to perform effective and transparent reasoning. While such an approach works well for tasks that only require forward reasoning (e.g.
Externí odkaz:
http://arxiv.org/abs/2305.09656
The goal of programmatic Learning from Demonstration (LfD) is to learn a policy in a programming language that can be used to control a robot's behavior from a set of user demonstrations. This paper presents a new programmatic LfD algorithm that targ
Externí odkaz:
http://arxiv.org/abs/2305.03129