Customer-obsessed science
Research areas
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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
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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CIKM 20182018The success of applications that process data critically depends on the quality of the ingested data. Completeness of a data source is essential in many cases. Yet, most missing value imputation approaches suffer from severe limitations. They are almost exclusively restricted to numerical data, and they either offer only simple imputation methods or are difficult to scale and maintain in production. Here
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NeurIPS 20182018Modern machine learning (ML) systems are comprised of complex ML pipelines which typically have many implicit assumptions about the data they consume (e.g., about the scales of variables, the presence of missing values or the dictionary of categorical values). Violations of these assumptions can result in crashes or wrong predictions. We therefore present Deequ, a library that allows users to explicitly
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ECCV 20182018We propose a framework that harnesses visual cues in an unsupervised manner to learn the co-occurrence distribution of items in real-world images for complementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train
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ICDM 20182018Identifying sets of items that are equivalent to one another is a problem common to many fields. Systems addressing this generally have at their core a function s(di, dj ) for computing the similarity between pairs of records di, dj . The output of s() can be interpreted as a weighted graph where edges indicate the likelihood of two records matching. Partitioning this graph into equivalence classes is non-trivial
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Games & Economic Behavior2018We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
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
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
