Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift S Rabanser, S Günnemann, Z Lipton Advances in Neural Information Processing Systems (NeurIPS), 2019 | 377 | 2019 |
Introduction to Tensor Decompositions and their Applications in Machine Learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017 | 281 | 2017 |
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models S Rabanser, T Januschowski, V Flunkert, D Salinas, J Gasthaus KDD Workshop on Mining and Learning from Time Series (MiLeTS) 2020, 2020 | 27 | 2020 |
Selective Prediction via Training Dynamics S Rabanser, A Thudi, K Hamidieh, A Dziedzic, I Bahceci, AB Sediq, ... | 19* | |
Training Private Models That Know What They Don't Know S Rabanser, A Thudi, A Thakurta, K Dvijotham, N Papernot Advances in Neural Information Processing Systems (NeurIPS), 2023 | 3 | 2023 |
Intrinsic Anomaly Detection for Multi-Variate Time Series S Rabanser*, T Januschowski*, K Rasul, O Borchert, R Kurle, J Gasthaus, ... arXiv preprint arXiv:2206.14342, 2022 | 3 | 2022 |
p-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations A Dziedzic*, S Rabanser*, M Yaghini*, A Ale, MA Erdogdu, N Papernot arXiv preprint arXiv:2207.12545, 2022 | 2 | 2022 |
Robust and Actively Secure Serverless Collaborative Learning O Franzese*, A Dziedzic*, CA Choquette-Choo, MR Thomas, MA Kaleem, ... Advances in Neural Information Processing Systems (NeurIPS), 2023 | | 2023 |