23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
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- Georgia Institute of TechnologyUS17 papers
104 papers · 1 filter
Online false discovery rate control for anomaly detection in time series
Quentin Rebjock, Barış Kurt, Tim Januschowski +1
This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties…
Multiple Randomization Designs: Estimation and Inference with Interference
Lorenzo Masoero, Suhas Vijaykumar, Thomas Richardson +5
Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs…
Differentially Private -means Clustering Applied to Meter Data Analysis and Synthesis
Nikhil Ravi, Anna Scaglione, Sachin Kadam +5
The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholde…
Living-Off-The-Land Command Detection Using Active Learning
Talha Ongun, Jack W. Stokes, Jonathan Bar Or +6
In recent years, enterprises have been targeted by advanced adversaries who leverage creative ways to infiltrate their systems and move laterally to gain access to critical data. O…
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Trung Phung, Trung Le, Long Vuong +4
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations a…
Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters
Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman +1
Many organizations employ compute clusters equipped with accelerators such as GPUs and TPUs for training deep learning models in a distributed fashion. Training is resource-intensi…