基本信息
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职业迁徙
个人简介
Research
I have tried to push certain fronts in ML research to make models truly useful and deployable in real life. They can be grouped into a few keywords.
Robustness. Changes in the input distribution shall not disrupt the model's predictive power. Ideally, a model should be robust against the shifts in input domain (e.g. natural and adversarial perturbations) and confounders (e.g. fairness).
Uncertainty. A model should know when it is going to get it wrong. This allows the users and downstream systems to make sensible and safe decisions based on the estimated confidence levels.
Weak Supervision. Human supervision is often a bottleneck for training a model on a new task. I have sought cost-effective surrogates for the annotations and corresponding training methodologies.
Privacy & Security. There are different privacy and security angles with which ML can be analyzed. One may question the "stealability" of a black-box model as an IP; one may also question the privacy guarantees for user data in the federated learning setup. Still others may wonder whether certain level privacy is achievable at all on internet, with the increasing volume of user data online and more widespread use of machine learning algorithms to process such data.
Evaluation. Correct evaluation is undoubtably important in research and industrial applications, yet it is surprisingly difficult. I have cleaned up benchmarks and evaluation protocols in a few domains.
Large-Scale ML. Some of the methodologies I have been involved in are designed for large-scale ML. They typically require minimal changes to the original ML system but bring consistent gains across the board.
I have tried to push certain fronts in ML research to make models truly useful and deployable in real life. They can be grouped into a few keywords.
Robustness. Changes in the input distribution shall not disrupt the model's predictive power. Ideally, a model should be robust against the shifts in input domain (e.g. natural and adversarial perturbations) and confounders (e.g. fairness).
Uncertainty. A model should know when it is going to get it wrong. This allows the users and downstream systems to make sensible and safe decisions based on the estimated confidence levels.
Weak Supervision. Human supervision is often a bottleneck for training a model on a new task. I have sought cost-effective surrogates for the annotations and corresponding training methodologies.
Privacy & Security. There are different privacy and security angles with which ML can be analyzed. One may question the "stealability" of a black-box model as an IP; one may also question the privacy guarantees for user data in the federated learning setup. Still others may wonder whether certain level privacy is achievable at all on internet, with the increasing volume of user data online and more widespread use of machine learning algorithms to process such data.
Evaluation. Correct evaluation is undoubtably important in research and industrial applications, yet it is surprisingly difficult. I have cleaned up benchmarks and evaluation protocols in a few domains.
Large-Scale ML. Some of the methodologies I have been involved in are designed for large-scale ML. They typically require minimal changes to the original ML system but bring consistent gains across the board.
研究兴趣
论文共 59 篇作者统计合作学者相似作者
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CoRR (2024)
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arxiv(2024)
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crossref(2024)
arxiv(2024)
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CoRR (2024)
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CoRR (2023)
引用2浏览0EI引用
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Luca Scimeca, Alexander Rubinstein,Damien Teney,Seong Joon Oh, Armand Mihai Nicolicioiu,Yoshua Bengio
arxiv(2023)
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