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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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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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NeurIPS 2020 Workshop on Meta-learning2020Bayesian optimization (BO) is among the most effective and widely used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are derived from the posterior predictive of a probabilistic surrogate model. Prevalent among these is the expected improvement (EI). Naturally, the need to ensure analytical tractability
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NeurIPS 2020 Workshop on Meta-learning2020In many real-world applications, the performance of machine learning models is evaluated not along a single objective, but across multiple, potentially competing ones. For instance, for a model deciding whether to grant or deny loans, it is critical to make sure decisions are fair and not only accurate. As it is often infeasible to find a single model performing best across all objectives, practitioners
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NeurIPS 2020 Workshop on Meta-learning2020Bayesian optimization (BO) is a popular method to optimize expensive blackbox functions. It efficiently tunes machine learning algorithms under the implicit assumption that hyperparameter evaluations cost approximately the same. In reality, the cost of evaluating different hyperparameters, be it in terms of time, dollars or energy, can span several orders of magnitude of difference. While a number of heuristics
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NeurIPS 2020 Workshop on Offline Reinforcement Learning2020We study offline policy evaluation in a setting where the target policy can take actions that were not available when the data was logged. We analyze the bias of two popular regression-based estimators in this setting, and upper-bound their biases by a quantity we refer to as the reward regression risk. We show that the estimators can be asymptotically unbiased and uniformly convergent if the reward regression
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NeurIPS 20202020We tackle the problem of predicting the number of optimization steps that a pretrained deep network needs to converge to a given value of the loss function. To do so, we leverage the fact that the training dynamics of a deep network during fine-tuning are well approximated by those of a linearized model. This allows us to approximate the training loss and accuracy at any point during training by solving
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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
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Courtesy of Pai-Ling Yin
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