Customer-obsessed science
Research areas
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
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September 2, 20253 min read
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Featured news
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AI-ML Systems 20242024Internet is one of the largest scale distributed system made up of multiple networks that is used to digitally connect billions of users. Traffic Engineering (TE) is a core problem in networking, which is responsible for routing packets across networks to provide the best user experience while ensuring a secure, stable, well-utilized and cost-efficient network. The time-varying graph nature of the network
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2024Customer behavioral data significantly impacts e-commerce search systems. However, in the case of less common queries, the associated behavioral data tends to be sparse and noisy, offering inadequate support to the search mechanism. To address this challenge, the concept of query reformulation has been introduced. It suggests that less common queries could utilize the behavior patterns of their popular
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CVPR 2024 Workshop on the Evaluation of Generative Foundation Models2024In the rapidly evolving field of Generative AI, this work takes initial steps towards establishing a systematic approach for comparing image editing methods. Currently, there is a lack of quantitative metrics for evaluating image editing tasks, with new methods being evaluated mostly qualitatively. Our methodology involves three key components: 1) The creation of a large synthetic dataset using GAN-Control
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JAMA Network Open2024Question: What is the association between enrollment in a subscription program that offers Amazon Prime members access to 60 common generic prescription drugs for a $5 monthly fee with medication refills, days’ supply and out-of-pocket costs? Findings: In this cohort study comparing 5,003 enrollees to 5,137 controls, before and after enrollment, subscription program enrollment was associated with statistically
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2024Many critical machine learning applications in cybersecurity, healthcare and finance, encounter challenges like data privacy, distribution shifts and class imbalance. Often, minority class labels are scarce and may only be present for specific types of samples, which can pose challenges for developing effective models that handle new and unforeseen minority examples at inference time. Additionally, feeding
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