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Learning Recourse on Instance Environment to Enhance Prediction Accuracy Lokesh Nagalapatti Guntakanti Sai Koushik Abir De Sunita Sarawagi Department of Computer Science and Engineering IIT Bombay
The performance of any supervised learning model depends strongly on the quality of input instances.
NeuralPayoffMachines: PredictingFairandStable PayoffAllocationsAmongTeamMembers
In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities.
the performance of various LLMs on code synthesis. However, these test-cases can be limited in both quantity and quality for fully assessing the functional
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code.
