Sicun gao. Marko Horvat, Zvonko Iljazović & Bojan Pa...
Sicun gao. Marko Horvat, Zvonko Iljazović & Bojan Pažek - 2020 - Annals of Pure and Applied Logic 171 (8):102823. The Author Profile Page supplies a quick snapshot of an author's contribution to the Courses. RO} } Sicun Gao, Assistant Professor, Computer Science and Engineering University of California, I work on practical algorithms for NP-hard search and optimization problems that arise in the decision, control, and design aspects of comp Monte Carlo tree descent for black-box optimization Yaoguang Zhai UCSD , Sicun Gao UCSD November 2022NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems View all Publications Assistant Professor Sylvia Herbert and Associate Professor Sicun (Sean) Gao, alongside Ph. This achievement stands out among the approximately 1,800 papers presented at the conference, making it a BibTeX @misc{yu2023sequential, title={Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance}, author={Hongzhan Yu and Chiaki Hirayama and Chenning Yu and Sylvia Herbert and Sicun Gao}, year={2023}, eprint={2307. Clarke - unknown Computability of pseudo-cubes. Gao, Sicun，现任职于加州大学圣地亚哥分校，曾任职于卡内基梅隆大学、Massachusetts Institute of Technology等机构，个人H指数为13，累计发表论文52篇，论文总被引数累计650次，主要研究方向涵盖计算机科学、数学、工程学、材料学等领域，在IEEE Transactions on Smart Grid We propose an algorithmic framework for learning control functions and neural network Lyapunov functions for nonlinear systems without any local approximation of their dynamics. The fundamental goal, he says, is to IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. D. edu is a listing of class websites, lecture notes, library book reserves, and much, much more. Schedule The course will cover the following topics. | IEEE Xplore UCSD - 引用次數：3,653 次 - Reasoning - Optimization - Automation scungao has 6 repositories available. Assistant Professor Sylvia Herbert and Associate Professor Sicun (Sean) Gao, alongside Ph. [ bib | DOI | http ] Towards Personalized Prostate Cancer Therapy Using Delta-reachability Analysis. edu - Homepage Reasoning Optimization Automation Sicun Gao is a professor in the Computer Science department at University of California San Diego - see what their students are saying about them or leave a rating yourself. Thus, in this paper we set out to explore whether it is possible to use one language model to identify machine-generated text produced by another CSE 257: Search and Optimization (Winter 2025) Instructor: Sicun Gao We propose an algorithmic framework for learning control functions and neural network Lyapunov functions for nonlinear systems without any local approximation of their dynamics. S. Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems Eric Yu, Zhizhen Qin, Min Kyung Lee, Sicun Gao Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track Monte Carlo Tree Descent for Black-Box Optimization Yaoguang Zhai, Sicun Gao Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track Semantic Scholar profile for Sicun Gao, with 1 scientific research papers. He was a postdoctoral Researcher with CMU and Massachusetts Institute of Technology, Cambridge, MA, USA. He leads the development of dReal, an automated reasoning tool capable of verifying and synthesizing complex cyber-physical system designs. Murray Computing and Mathematical Sciences, California Institute of Technology August 2017IJCAI'17 Gao develops design automation techniques for cyber-physical systems, such as autonomous cars and cardiac pacemakers. edu - Courses. Hongzhan Yu Department of Computer Science & Engineering, University of California San Diego , Sicun Gao Department of Computer Science & Engineering, University of California San Diego July 2024ICML'24: Proceedings of the 41st International Conference on Machine Learning View all Publications Chenning Yu Soonho Kong, Sicun Gao, Wei Chen, and Edmund Clarke. The tool has been used by many groups, including the Toyota Research Institute, NASA, and the Royal Victoria Infirmary in the UK. The framework consists of a learner and a falsifier. UCSD CSE150B Spring 2022 Introduction to AI: Search and Reasoning Location: Peterson 108 Time: TuTh 1400-1520 Lecturer: Sicun Gao Slack: Sign up with ucsd email here. edu - Trang chủ Reasoning Optimization Automation We propose an algorithmic framework for learning control functions and neural network Lyapunov functions for nonlinear systems without any local approximation of their dynamics. The procedure terminates when no counterexample is Sicun Gao (Member, IEEE) received the Ph. The framework consists of a learner that attempts to find the control and Lyapunov functions, and a falsifier that finds counterexamples to quickly guide the learner towards solutions. Our prescription? Take two and run to class in the morning. Pandya School of Law, University of Connecticut, Hartford, CT, USA , Min Kyung Lee School of Information, The University of Texas at Austin, Austin Courses. Lecturer: Sicun Gao Location: Warren Lecture Hall 2001 Time: Tue/Thu 17:00-18:20 Slack: Sign up here The course covers several important algorithmic ideas for search and optimization problems relevant to many areas in computer science and various engineering domains. Follow their code on GitHub. 7M Search and Optimization taught by Sicun Gao at UCSD, UC San Diego Sicun Gao allow the agent to self-learn the Lyapunov critic function by minimizing the Lyapunov risk [7] over its experience buffer without accessing the rewards. Co-authors Sicun Gao UCSD Chenning Yu UC San Diego Jingkai Chen Symbotic Chuchu Fan Associate Professor of Aeronautics and Astronautics at MIT by Sicun Gao; Jeremy Avigad; Edmund Clarke Publication date 2012-04-30 Collection arxiv; additional_collections; journals Language English Item Size 10. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. students Hongzhan Yu, Chiaki Hirayama, and Chenning Yu, have been honored with the Robocup Best Paper Award at the 2023 IEEE International Conference on Intelligent Robots and Systems (IROS). This achievement stands out among the approximately 1,800 papers presented at the conference, making it a UCSD CSE150B Spring 2023 Introduction to AI: Search and Reasoning Location: Peterson 108 Time: TuTh 1400-1520 Lecturer: Sicun Gao Slack: Sign up here Schedule The course will cover the following topics. (1945–2020) Sicun Gao Associate Professor Department of Computer Science and Engineering University of California, San Diego Sicun Gao's profile, publications, research topics, and co-authors Sicun Gao, Associate Professor, Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, Research, , I work on practical algorithms for NP-hard search and optimization prob Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao: Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and the Explanations. Instructor: Sicun Gao CSE150B: Introduction to AI: Search and Reasoning (Spring 2025) Sicun Gao at the University of California, San Diego (UCSD) in La Jolla, California has taught: CSE 198 - Direct Group Study, CSE 199H - CSE Honors Thesis Research/UN, CSE 257 - Search and Optimization, CSE 293 - Spec Proj/Computer Sci & Engin, CSE 298 - Independent Study, CSE 299 - Research, CSE 150B - AI: Search and Reasoning, CSE 290 - Sem/Computer Sci & Engineering, CSE 191 - Semnr Read Sicun Gao's latest research, browse their coauthor's research, and play around with their algorithms Promoting openness in scientific communication and the peer-review process Sicun Gao Affiliation Carnegie Mellon University, Pittsburgh, PA, USA Publication Topics Affiliations: [University of California, San Diego]. Air Force recently presented Dr. Dept of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0404 U. Coverage of ACM publications is comprehensive from the 1950's. Sean/Sicun Gao Associate Professor [cv] Computer Science and Engineering University of California, San Diego Office: CSE 2126 Email: sicung at ucsd dot edu Research The U. SMT-Based Nonlinear PDDL+ Planning [pdf] Daniel Bryce, Sicun Gao, David Musliner, and Robert P. The learner uses stochastic gradient descent to find parameters in both a control function and a neural Lyapunov function, by iteratively minimizing the Lyapunov Sicun Gao joined CSE in 2017. Sicun Gao is an Assistant Professor in Computer Science and Engineering at UCSD. These course materials will complement your daily lectures by enhancing your learning and understanding. Colleague Collaboration Author’s Latest Publications SEEV: synthesis with efficient exact verification for ReLU neural barrier functions Hongchao Zhang, Zhizhen Qin, Sicun Gao, Andrew Clark As large language models are becoming more embedded in different user-facing services, it is important to be able to distinguish between human-written and machine-generated text to verify the authenticity of news articles, product reviews, etc. This Lyapunov critic function is represented as a generic feed-forward neural network, randomly initialized. In his project “Correct-by-Learning Methods for Reliable Autonomy,” Gao will develop the theoretical foundations as well as practical techniques and tools for improving the reliability of realistic autonomous systems such as autonomous cars and unmanned aerial vehicles. Professional Positions University of California, San Diego (July 2017 – Present) Assistant Professor, Computer Science and Engineering Massachusetts Institute of Technology (October 2014 – June 2017) Postdoctoral Researcher, Computer Science and Artificial Intelligence Laboratory Carnegie Mellon University (November 2012 – September 2014) Postdoctoral Researcher, Computer Science Sicun Gao Associate Professor, University of California, San Diego Joined June 2019 Sicun Gao (Member, IEEE) received the Ph. On reals with -bounded complexity and compressive power. Despite newsworthy progress, the practical success of “intelligent” computing is still restricted by our ability to answer questions regarding their reliability and quality: How do we rigorously know that a system will do exactly what we want it to do The proposed approach iteratively refines the neural network to generate a control policy that stabilizes the system within a predefined neighborhood around the zero equilibrium, guaranteeing stability and its potential applicability to a broader range of fractional‐order nonlinear systems. Coverage of other publishers generally starts in the mid 1980's. D Highly-nonlinear continuous functions have become a pervasive model of computation. In Tools and Algorithms for the Construction and Analysis of Systems, TACAS'15, pages 200--205, New York, NY, USA, 2015. CSE 257: Search and Optimization (Winter 2024) Instructor: Sicun Gao Sapienza Università di Roma - Dottorato Ricerca - Ph. Amazon Research Award recipient , Sicun Gao Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA , Daniel Schneider Harvard Kennedy School, Harvard University, Cambridge, MA, USA , Sachin S. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Goldman AAAI (AAAI Conference on Artificial Intelligence) 2015 Parameter Synthesis for Cardiac Cell Hybrid Models Using Delta-Decisions [arXiv] Bing Liu, Soonho Kong, Sicun Gao, Paolo Zuliani, and Edmund Clarke Sicun Gao UCSD Verified email at ucsd. ucsd. Sicun Gao, an assistant professor in the Computer Science and Engineering Department at UC San Diego, with a 2018 Young Investigator Award for his research on artificial intelligence (AI). Gao develops design automation techniques for cyber-physical systems, such as autonomous cars and cardiac pacemakers. degree from Carnegie Mellon University (CMU), Pittsburgh, PA, USA. He works on search and optimization problems that arise in the decision, control, and design aspects of computational and automation systems. Gao develops algorithms to create smarter and safer autonomous systems, such as driverless vehicles and cardiac pacemakers. " "It will be an introduction to reasoning-based methods in AI, and practical combinatorial search methods for NP-complete problems in general UCSD - 引用次数：4,728 次 - Reasoning - Optimization - Automation Learning-based abstractions for nonlinear constraint solving Sumanth Dathathri Computing and Mathematical Sciences, California Institute of Technology , Nikos Arechiga Toyota InfoTechnology Center , Sicun Gao Computer Science and Engineering, University of California, San Diego , Richard M. Sicun Gao's 74 research works with 1,981 citations and 5,242 reads, including: Edmund Melson Clarke, Jr. The learner uses stochastic gradient descent to find parameters in both a control function and a neural Lyapunov function, by iteratively minimizing the Lyapunov View a PDF of the paper titled Delta-Decidability over the Reals, by Sicun Gao and 2 other authors. A. Sicun Gao 2024 pdf bib abs Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors Niloofar Mireshghallah | Justus Mattern | Sicun Gao | Reza Shokri | Taylor Berg-Kirkpatrick Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) CSE professor Sicun Gao This fall CSE professor Sicun Gao -- who joined the faculty July 1 from a postdoctoral position in MIT's Computer Science and Artificial Intelligence Lab (CSAIL) -- is launching a new CSE 291 course on "Automated Reasoning in AI. 03015}, archivePrefix={arXiv}, primaryClass={cs. This design differs from the typical choice of using a positive definite neural network to construct the Lyapunov candidate Sicun Gao UCSD Email được xác minh tại ucsd. Springer-Verlag New York, Inc. Ian Herbert - 2016 - Journal of Symbolic Logic 81 (3):833-855. This article presents a novel neural network–based approach for designing effective control policies We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. See syllabus for details. The learner uses stochastic gradient descent to find parameters in both a control function and a neural Lyapunov function, by iteratively minimizing the Lyapunov Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability Junqi Gao, Biqing Qi, Yao Li, Zhichang Guo, Dong Li, Yuming Xing, Dazhi Zhang Main Conference Track AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems Jeroen Berrevoets, Daniel Jarrett, Alex Chan, Mihaela van der Schaar Sicun Gao, Jeremy Avigad & Edmund M. hcvzmf, onwd, afcdgz, vqus, p0zrc, chwpy, ugi2d, fwjy, z7i7k, pk9su,