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Our research focuses on understanding the practical limits of using existing ML methods in the real-world. Essential, we seek answers to the following question: How to make ML models simpler & reliable to use in constrained settings? Simplicity refers to the ability to (i) build or implement the method easily, (ii) execute the deployed model efficiently, and (iii) evolve the deployed model with less effort. Reliability relates to (i) whether we can rely on the model to solve the intended task well, (ii) whether this performance is preserved under frequently perturbed environments in practice such as data corruptions or distributional changes, and (iii) whether the model is resilient to (i.e., its performance is not significantly affected by) various forms of security attacks such as adversarial examples and causal attacks. In this sense, we believe that many existing ML methods, including those with complex deep neural networks, are reliable but not yet easy-to-use because they do not satisfy various constraints seen in real-world applications. We also strongly believe the effort to answer this question will help us truly realize the potential of AI/ML methodology in practice.
Our research focuses on understanding the practical limits of using existing ML methods in the real-world. Essential, we seek answers to the following question: How to make ML models simpler & reliable to use in constrained settings? Simplicity refers to the ability to (i) build or implement the method easily, (ii) execute the deployed model efficiently, and (iii) evolve the deployed model with less effort. Reliability relates to (i) whether we can rely on the model to solve the intended task well, (ii) whether this performance is preserved under frequently perturbed environments in practice such as data corruptions or distributional changes, and (iii) whether the model is resilient to (i.e., its performance is not significantly affected by) various forms of security attacks such as adversarial examples and causal attacks. In this sense, we believe that many existing ML methods, including those with complex deep neural networks, are reliable but not yet easy-to-use because they do not satisfy various constraints seen in real-world applications. We also strongly believe the effort to answer this question will help us truly realize the potential of AI/ML methodology in practice.
研究兴趣
论文共 38 篇作者统计合作学者相似作者
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCEno. Part A (2024): 107166-107166
PROCEEDINGS OF THE 46TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, SIGIR 2023 (2023): 697-707
CoRR (2023)
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arxiv(2023)
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arxiv(2023)
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NeurIPS (2023)
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