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methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as well as programming to test out
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theory to prove mathematical guarantees about the performance of such new explanation methods, as well as programming to test out the methods and mathematical theory in experiments. (See example references
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, fundamentally limiting their ability for spatial reasoning, temporal logic, and operating in low-resource scenarios, which leads to shortcut learning and hallucination at test-time. This PhD project focuses on a
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are critically hindered by the language-focussed optimization of current foundation models, fundamentally limiting their ability for spatial reasoning, temporal logic, and operating in low-resource scenarios
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leveraged a culture of open, collaborative innovation to scale the reach of our ideas and pace of iteration, without having to scale our headcount. We operate in Chicago, New York, and Amsterdam. In our