author
Chief Scientist
,
Snorkel AI
Assistant Professor @ University of Wisconsin-Madison
Frederic Sala is Chief Scientist at Snorkel AI and an assistant professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research studies the fundamentals of data-driven systems and machine learning, with a focus on data-centric AI, foundation models, and automated machine learning. He and his group received the 2024 DARPA Young Faculty Award, a best student paper runner-up award at UAI ’22, the outstanding Ph.D. dissertation award from the UCLA Department of Electrical Engineering, the NSF Graduate Research Fellowship.
The latest from Fred
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
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: do state-of-the-art NAS methods perform well on diverse tasks? To construct the benchmark, we curate ten tasks spanning a diverse array of application domains, dataset sizes, problem dimensionalities, and learning objectives. Each new task is carefully chosen to interoperate with...
Research Paper
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
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:…
Blog
4 new papers show foundation models can build on themselves
The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.
Blog
Getting better performance from foundation models (with less data)
Getting better performance from foundation models (with less data)
Blog
The future of large language models is faster and more robust
Snorkel and affiliated academic labs have been hard at work reducing how computationally expensive large language models are.
Anomaly Detection with Multiple Reference Datasets
This paper proposes generalizations of CWOLA and SALAD, which exploit multiple reference datasets to improve performance in resonant anomaly detection, and provides finite-sample guarantees to go beyond existing asymptotic analyses.
Research Paper
Anomaly Detection with Multiple Reference Datasets
This paper proposes generalizations of CWOLA and SALAD, which exploit multiple reference datasets to improve performance in resonant anomaly detection, and provides finite-sample guarantees to go beyond existing asymptotic analyses.
Ask Me Anything: A simple strategy for prompting language models.
This paper proposes "Ask Me Anything" (AMA), a prompting method that uses weak supervision to combine noisy predictions from multiple prompts generated from an LLM, resulting in an average 10.2% performance lift over the few-shot baseline across a variety of different open-source models.
Research Paper
Ask Me Anything: A simple strategy for prompting language models.
This paper proposes “Ask Me Anything” (AMA), a prompting method that uses weak supervision to combine noisy predictions from multiple prompts generated from an LLM, resulting in an average 10.2% performance lift over the few-shot baseline across a variety of different open-source models.
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
AutoWS-Bench-101 is a framework for evaluating automated weak supervision techniques compared to other baseline methods such as zero-shot foundation models and supervised learning, in order to help practitioners choose the best method to generate additional labels.
Research Paper
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
AutoWS-Bench-101 is a framework for evaluating automated weak supervision techniques compared to other baseline methods such as zero-shot foundation models and supervised learning, in order to help practitioners choose the best method to generate additional labels.
Lifting Weak Supervision To Structured Prediction
This paper finds that weak supervision can be used beyond classification applications, including rankings, graphs, and manifolds, and can provide generalization guarantees nearly identical to models trained on clean data.
Research Paper
Lifting Weak Supervision To Structured Prediction
This paper finds that weak supervision can be used beyond classification applications, including rankings, graphs, and manifolds, and can provide generalization guarantees nearly identical to models trained on clean data.
Generative Modeling Helps Weak Supervision (and Vice Versa)
This work proposes and theoretically justifies a model that fuses weak supervision and generative adversarial networks to improve the estimate of unobserved labels and data augmentation, outperforming baseline weak supervision models on multiclass image classification datasets.
Research Paper
Generative Modeling Helps Weak Supervision (and Vice Versa)
This work proposes and theoretically justifies a model that fuses weak supervision and generative adversarial networks to improve the estimate of unobserved labels and data augmentation, outperforming baseline weak supervision models on multiclass image classification datasets.
