Customer-obsessed science
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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July 9, 202610 min read
Featured news
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IJCAI 2022 AI Safety Workshop2022Contextual bandits are widely used across the industry in many applications such as search engines, dialogue systems, recommendation systems, etc. In such applications, it is often necessary to update the policy regularly as the data distribution changes and new features are being on-boarded frequently. As any new policy deployment directly impacts the user experience, safety in model updates is an important
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KDD 2022 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2022Online advertising opportunities are sold through auctions, billions of times every day across the web. Advertisers who participate in those auctions need to decide on a bidding strategy: how much they are willing to bid for a given impression opportunity. Deciding on such a strategy is not a straightforward task, because of the interactive and reactive nature of the repeated auction mechanism. Indeed,
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ICPR 20222022Detecting audio-video (A/V) synchronization error is important to measure end user experience. Today, researchers in this domain are mainly focused on contents such as movies or sports. The state of art algorithms usually first detect a specific type of events and then correlate the A/V data within during these events, e.g., find the human chatting events and then correlate the vocals with the lip shapes
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IROS 20222022Monocular depth estimation (MDE) has attracted intense study due to its low cost and critical functions for robotic tasks such as localization, mapping and obstacle detection. Supervised approaches have led to great success with the advance of deep learning, but they rely on large quantities of ground-truth depth annotations that are expensive to acquire. Unsupervised domain adaptation (UDA) transfers knowledge
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ICML 2022 Workshop on Continuous Time Methods for Machine Learning2022Time series forecasting is a fundamental problem in machine learning with relevance to many applications including supply chain management, finance, healthcare, etc. As an example, consider a large e-commerce retailer with a system to produce forecasts of the demand distribution for a set of products at a target time T. Using these forecasts as an input, the retailer can then optimize buying and placement
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