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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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NeurIPS 2022 Workshop on SyntheticData4ML2022Recent advancements in Natural Language Processing (NLP) algorithms have resulted in state-of-the-art performance on Named Entity Recognition (NER) tasks. These algorithms typically require high-quality labeled datasets for training models. However, training NLP models effectively can suffer from issues such as scarcity of labeled data, data bias and under-representation, and privacy concerns with using
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NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML)2022Gradient boosting takes linear combinations of weak base learners. Therefore, absent privacy constraints (when we can exactly optimize over the base models) it is not effective when run over base learner classes that are closed under linear combinations (e.g. linear models). As a result, gradient boosting is typically implemented with tree base learners (e.g., XGBoost), and this has become the state of
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EMNLP 2022 Workshop on Massively Multilingual NLU2022Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available data. They also often vary widely in performance. We launch a three-phase approach to address the limitations in NLU and help propel NLU technology to new heights. We release a 52 language
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CoRL 2022 Workshop on Language and Robot Learning2022Household environments are visually diverse. Embodied agents performing Vision-and-Language Navigation (VLN) in the wild must be able to handle this diversity, while also following arbitrary language instructions. Recently, VisionLanguage models like CLIP have shown great performance on the task of zeroshot object recognition. In this work, we ask if these models are also capable of zero-shot language grounding
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NeurIPS 2022 Workshop on a Causal View on Dynamical Systems2022Seeking causal explanations in panel (or longitudinal/multivariate time-series) data is a difficult problem of both academic and industrial importance. Although there exists a large amount of literature on forward causal inference, where the treatment/outcome/covariates variables are well-defined, it is unclear how to answer the reverse question: which covariates have effects on the outcome? In this paper
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Gretchen Ertl
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A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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