65 citations · 108 across the 12 of their papers we have counts for
12 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…
Streaming Quantiles Algorithms with Small Space and Update Time
Nikita Ivkin, Edo Liberty, Kevin Lang +2
Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically op…
Communication-efficient distributed SGD with Sketching
Nikita Ivkin, Daniel Rothchild, Enayat Ullah +3
Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time. Motivated by the su…
DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results
Sanghyun Choi, Nikita Ivkin, Vladimir Braverman +1
Extracting information from electronic health records (EHR) is a challenging task since it requires prior knowledge of the reports and some natural language processing algorithm (N…
Scalable Streaming Tools for Analyzing -body Simulations: Finding Halos and Investigating Excursion Sets in One Pass
Nikita Ivkin, Zaoxing Liu, Lin F. Yang +6
Cosmological -body simulations play a vital role in studying models for the evolution of the Universe. To compare to observations and make a scientific inference, statistic anal…
BPTree: an heavy hitters algorithm using constant memory
Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin +3
The task of finding heavy hitters is one of the best known and well studied problems in the area of data streams. One is given a list and the goal is to…
Beating CountSketch for Heavy Hitters in Insertion Streams
Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin +1
Given a stream of items from a universe , which, without loss of generality we identify with the set of integers , we consider…