Machine Learning & AI
At the forefront of technological innovation, Machine Learning involves crafting algorithms for autonomous learning, while AI simulates human intelligence. Integrating multi-agent systems and game theory enhances decision-making in complex environments, finding applications in finance, cybersecurity, and resource management.
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71 result(s)
Online Pricing Incentive to Sample Fresh Information
H. Li and L. Duan, 2023, IEEE Transactions on Network Science and Engineering, 10(1), 514 – 526, https://arxiv.org/abs/2209.08711
Parallelizing Maximal Clique Enumeration on GPUs
M Almasri, YH Chang, I El Hajj, R Nagi, J Xiong, W Hwu, 2023, 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT), 162-175, https://arxiv.org/pdf/2212.01473.pdf
Passenger-Centric Slot Allocation at Schedule-Coordinated Airports
S Birolini, A Jacquillat, P Schmedeman, N Ribeiro, 2023, Transportation Science, vol. 57 (1), 4-26, https://pubsonline.informs.org/doi/pdf/10.1287/trsc.2022.1165
Probability Bounds for N Random Events Under (nā 1)-wise Independence
K Natarajan, A Ramachandra, C Tan, 2023, Operations Research Letters, 116-122, https://www.sciencedirect.com/science/article/pii/S0167637723000044
To Save Mobile Crowdsourcing from Cheap-talk: A Game Theoretic Learning Approach
S. Hao and L. Duan, 2023, IEEE Transactions on Mobile Computing, https://arxiv.org/abs/2306.06791
When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure
H. Li and L. Duan, 2023, The 37th AAAI Conference on Artificial Intelligence, https://arxiv.org/abs/2211.14029
The Random QUBO
K Natarajan, 2022, The Quadratic Unconstrained Binary Optimization Problem: Theory, Algorithms, and Applications, 187-206, https://sutd.primo.exlibrisgroup.com/openurl/65SUTD_INST/65SUTD_INST:65SUTD?sid=google&auinit=K&aulast=Natarajan&atitle=The+Random+QUBO&id=doi:10.1007/978-3-031-04520-2_7
Submodularity and Pairwise Independence
A Ramachandra, K Natarajan, 2022, SSRN Electronic Journal, 1-38, https://arxiv.org/pdf/2209.08563.pdf
The Limit of the Marginal Distribution Model in Consumer Choice
Y Ruan, X Li, K Murthy, K Natarajan, 2022, 1-65
A Representative Consumer Model in Data-driven Multiproduct Pricing Optimization
Z Yan, K Natarajan, CP Teo, C Cheng, 2022, Management Science, Volume 68, Issue 8, Pages 5798-5827, https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2021.4182