Conveners
New Directions in AD
- David Shih
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Vinny Mikuni17/06/2025, 09:00
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Anna Hallin17/06/2025, 09:18
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Chi Lung Cheng Cheng (University of Wisconsin-Madison)17/06/2025, 09:36Plenary Talk - New Directions in AD
Weakly-supervised anomaly detection methods offer a powerful approach for discovering new physics by comparing data to a background-only reference. However, the sensitivity of existing strategies can be significantly limited by rare signals or high-dimensional, noisy feature spaces. We present Prior-Assisted Weak Supervision (PAWS), a novel machine-learning technique that significantly boosts...
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Runze Li (Yale University)17/06/2025, 09:54Plenary Talk - New Directions in AD
Modern simulation-based inference (SBI) is a suite of tools to scaffold physics-based simulations with neural networks and other tools to perform inference using as much information as possible. We extend this toolkit to the case where some or all of the simulations are actually surrogate models learned directly from the data. This Surrogate Simulation-based Inference (S2BI) concept is...
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Gaia Grosso (IAIFI/MIT/Harvard)17/06/2025, 10:12Plenary Talk - New Directions in AD
Anomaly detection in high energy physics (HEP) and many other scientific fields, is challenged by rare signals found in high-dimensional data. Two main strategies have emerged to mitigate the curse of dimensionality: scaling detection methods to handle high dimensions, or reducing the dimensionality before statistical analysis.
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This talk focuses on the latter, introducing a supervised... -
Gaia Grosso