1 code implementation • 17 Nov 2022 • Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du, William Hewlett
CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario.
no code implementations • 12 Oct 2021 • Nandini Ramanan, Rasool Tahmasbi, Marjorie Sayer, Deokwoo Jung, Shalini Hemachandran, Claudionor Nunes Coelho Jr
Practical machine learning applications involving time series data, such as firewall log analysis to proactively detect anomalous behavior, are concerned with real time analysis of streaming data.
no code implementations • 11 Jun 2021 • Deokwoo Jung, Nandini Ramanan, Mehrnaz Amjadi, Sankeerth Rao Karingula, Jake Taylor, Claudionor Nunes Coelho Jr
Our algorithm is easily parallelizable, more robust for ill-conditioned and seasonal data, and highly scalable for a large number of anomaly models.
no code implementations • 29 May 2021 • Charanraj Thimmisetty, Praveen Tiwari, Didac Gil de la Iglesia, Nandini Ramanan, Marjorie Sayer, Viswesh Ananthakrishnan, Claudionor Nunes Coelho Jr
By combining the strengths of deep learning and symbolic methods, Log2NS provides a very powerful reasoning and debugging tool for log-based data.
no code implementations • 10 Apr 2021 • Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr
Many advanced autoregressive methods such as ARIMA were used to develop forecasting models.
no code implementations • 15 Dec 2019 • Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan
First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization.
no code implementations • 6 Aug 2018 • Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
We consider the problem of learning Relational Logistic Regression (RLR).