Berivan Isik
Berivan Isik is a research scientist at Google, focusing on developing efficient and trustworthy large models. She earned her PhD from Stanford University in 2024. See her personal page for more up-to-date information.
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Google
Leveraging Per-Example Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Karolina Dziugaite
International Conference on Machine Learning (ICML) (2025)
Scaling Laws for Downstream Task Performance in Machine Translation
Natalia Ponomareva
Hussein Hazimeh
Sanmi Koyejo
International Conference on Learning Representations (ICLR) (2025) (to appear)
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Onur Guleryuz
Phil A. Chou
Hugues Hoppe
Danhang Tang
Jonathan Taylor
Philip Davidson
arXiv:2402.05887 (2024)
Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen
Albert No
Sewoong Oh
International Conference on Machine Learning (ICML) (2024)
Sketching for Distributed Deep Learning: A Sharper Analysis
Mayank Shrivastava
Qiaobo Li
Sanmi Koyejo
Arindam Banerjee
Conference on Neural Information Processing Systems (NeurIPS) (2024)
Sandwiched Video Compression: An Efficient Learned Video Compression Approach
Onur Guleryuz
Danhang "Danny" Tang
Jonathan Taylor
Phil Chou
IEEE International Conference on Image Processing (2023)
LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks
Phil Chou
Sung Jin Hwang
Journal of Frontiers in Signal Processing (2022)
