7 citations · 13 across the 6 of their papers we have counts for
10 papers
Federated Learning Clients Clustering with Adaptation to Data Drifts
Minghao Li, Dmitrii Avdiukhin, Rana Shahout +3
Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limi…
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Piali Das, Valerio Perrone, Nikita Ivkin +22
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of…
Sketch and Scale: Geo-distributed tSNE and UMAP
Viska Wei, Nikita Ivkin, Vladimir Braverman +1
Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security…
Practical and sample efficient zero-shot HPO
Fela Winkelmolen, Nikita Ivkin, H. Furkan Bozkurt +1
Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement e…
FetchSGD: Communication-Efficient Federated Learning with Sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah +5
Existing approaches to federated learning suffer from a communication bottleneck as well as convergence issues due to sparse client participation. In this paper we introduce a nove…
Six Dimensional Streaming Algorithm for Cluster Finding in N-Body Simulations
Aidan Reilly, Nikita Ivkin, Gerard Lemson +2
Cosmological N-body simulations are crucial for understanding how the Universe evolves. Studying large-scale distributions of matter in these simulations and comparing them to obse…