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 30, 20268 min read
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July 9, 202610 min read
Featured news
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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ICML 20202020We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not inherently encoded, i.e., the models are permutation equivalent; this problem
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ICML 20202020Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes.
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ICML 2020 Workshop on AutoML2020Bayesian optimization (BO) is a class of global optimization algorithms ubiquitous in hyperparameter optimization (HPO). BO budgets are typically given in iterations, which implicitly measures convergence in terms of the number of hyperparameter evaluations. In practice, evaluation costs may vary in different regions of the search space. For example, the cost of neural network training increases quadratically
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ICML 2020 Workshop on AutoML2020Given the increasing importance of machine learning (ML) in our lives, algorithmic fairness techniques have been proposed to mitigate biases that can be amplified by ML. Commonly, these specialized techniques apply to a single family of ML models and a specific definition of fairness, limiting their effectiveness in practice. We introduce a general constrained Bayesian optimization (BO) framework to optimize
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KDD 2020 Workshop on Industrial Recommendation2020Identifying similar products is a common pain-point in the world of E-commerce search and discovery. The key challenges lie in two aspects: 1) The definition of similarity varies across different applications, such as near identical products sold by different vendors, products that are substitutable to each other for customers with common interests, personalized products visually similar in terms of design
Collaborations
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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.
