We define and advance data and environments to push the AI frontier

Weak supervision overcomes the label bottleneck, enabling efficient development of training sets. Millions of models trained on such datasets have been deployed in the real world and interact with users on a daily basis. However, the techniques that make weak supervision attractive—such as integrating any source of signal to estimate unknown labels—also ensure that the pseudolabels it produces are highly…

Graph-based machine learning is experiencing explosive growth, driven by impressive recent developments and wide applicability. Typical approaches for graph representation learning predominantly focus on pairwise interactions, while neglecting the patterns of higher-order interactions common to complex systems. This paper explores many-body interaction models, centering on simplicial complexes. From a theoretical point of view, we offer a pair of insights illustrating…

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Auto-labeling systems are a promising way to reduce reliance on manual labeling for dataset construction. Threshold-based auto-labeling, where validation data obtained from humans is used to find a threshold for confidence above which the data is machine-labeled, is emerging as a popular solution used widely in…

The ability to generalize to unseen domains is crucial for machine learning systems, especially when we only have data from limited training domains and must deploy the resulting models in the real world. In this paper, we study domain generalization via the classic empirical risk minimization (ERM) approach with a simple regularizer based on the nuclear norm of the learned…

The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate change AI (CCAI) community works on a diverse, challenging set of problems which often involve physics-constrained ML or heterogeneous spatiotemporal data. It would be desirable to use automated machine learning (AutoML) techniques to automatically find…

The vision of Automated Machine Learning (AutoML) is to produce high performing ML pipelines that require very little human involvement or domain expertise to use. Competitions and benchmarks have been critical tools for accelerating progress in AutoML. However, much of the prior work on AutoML competitions has focused on well-studiedd omains in machine learning such as vision and language—these are…

Spurious correlations are one of the biggest pain points for users of modern machine learning. To handle this issue, many approaches attempt to learn features that are causally linked to the prediction variable. Such techniques, however, suffer from various flaws—they are often prohibitively complex or based on heuristics and strong assumptions that may fail in practice. There is no onesize-fits-all…

Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet. Thismakes the performance of NAS approaches in more diverse areas poorly understood. In this paper, we present NAS-Bench-360, a benchmark suite to evaluate methods on domains beyond those traditionally studied in architecture search, and use it to address the following question:…

Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool’s usage. Unfortunately, demonstrations are hard to acquire, and can result in undesirable biased usage if the wrong demonstration is chosen. Even in the rare scenario that demonstrations are readily available, there is no principled selection protocol to determine how many and…