Mike Rabbat
Publications by Year
Research Areas
Stochastic Gradient Optimization Techniques, Privacy-Preserving Technologies in Data, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Cryptography and Data Security
Most-Cited Works
- → Sustainable AI: Environmental Implications, Challenges and Opportunities(2021)346 cited
- → Masked Siamese Networks for Label-Efficient Learning(2022)188 cited
- → MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions(2019)33 cited
- → Papaya: Practical, Private, and Scalable Federated Learning(2021)29 cited
- → Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity(2022)27 cited
- Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery(2021)
- → Benchmarking Neural Network Training Algorithms(2023)6 cited
- → CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery(2020)4 cited
- → Learning with Gradient Descent and Weakly Convex Losses(2021)3 cited
- → Privacy-Aware Compression for Federated Data Analysis(2022)2 cited