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for her contributions to uncovering the underlying connections among robustness, privacy, and generalization in AI, showing how different models are vulnerable to malicious attacks, and how to eliminate these vulnerabilities using mathematical tools that provide robustness guarantees for learning
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for his contributions to both the approach of semantic parsing for natural language understanding and better methods for learning latent-variable models, sometimes with weak supervision, in machine learning.
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for her contributions to integrating machine learning with game theory and the use of these novel techniques to tackle societal challenges such as more effective deployment of security resources, enhancing
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for his contributions to consolidating properties of trust, robustness and safety into rigorous algorithmic procedures and computable metrics for improving AI systems.
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for his contributions to AI and society, in particular foundational work on the theory of algorithmic fairness using principles from social choice theory.
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of the topic and provide significant coverage of it beyond a mere trivial mention. If notability cannot be shown, the article is likely to be
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for his contributions to statistical and relational artificial intelligence, and the study of tractability in learning and reasoning.
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Please help to demonstrate the notability of the topic by citing
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International Joint
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International Joint
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and Julian
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200:(IJCAI), recognizing outstanding young scientists in
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565:"Awards – IJCAI 2024"
545:"Awards – IJCAI 2022"
525:"Awards – IJCAI 2021"
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