Matches in SemOpenAlex for { <https://semopenalex.org/work/W3021678865> ?p ?o ?g. }
Showing items 1 to 73 of
73
with 100 items per page.
- W3021678865 abstract "Author(s): Raman, Parameswaran | Advisor(s): S.V.N., Vishwanathan | Abstract: Distributed algorithms in machine learning follow two main flavors: horizontal partitioning, where the data is distributed across multiple slaves and vertical partitioning, where the model parameters are partitioned across multiple machines. The main drawback of the former strategy is that the model parameters need to be replicated on every machine. This is problematic when the number of parameters is very large, and hence cannot fit in a single machine. This drawback of the latter strategy is that the data needs to be replicated on each machine, thus failing to scale to massive datasets.The goal of this thesis is to achieve the best of both worlds by partitioning both - the data as well as the model parameters, thus enabling the training of more sophisticated models on massive datasets. In order to do so, we exploit a structure that is observed in several machine learning models, which we term as textit{Double-Separability}. Double-Separability basically means that the objective function of the model can be decomposed into independent sub-functions which can be computed independently. For distributed machine learning, this implies that both data and model parameters can partitioned across machines and stochastic updates for parameters can be carried out independently and without any locking. Furthermore, double-separability naturally lends itself to developing efficient asynchronous algorithms which enable computation and communication to happen in parallel, offering further speedup. Some machine learning models such as Matrix Factorization directly exhibit double-separability in their objective function, however the majority of models do not. My work explores techniques to reformulate the objective function of such models to cast them into double-separable form. Often this involves introducing additional auxiliary variables that have nice interpretations. In this direction, I have developed Hybrid Parallel algorithms for machine learning tasks that include {it Latent Collaborative Retrieval}, {it Multinomial Logistic Regression}, {it Variational Inference for Mixture of Exponential Families} and {it Factorization Machines}. The software resulting from this work are available for public use under an open-source license." @default.
- W3021678865 created "2020-05-13" @default.
- W3021678865 creator A5010360658 @default.
- W3021678865 date "2020-01-01" @default.
- W3021678865 modified "2023-09-27" @default.
- W3021678865 title "Hybrid-Parallel Parameter Estimation for Frequentist and Bayesian Models" @default.
- W3021678865 hasPublicationYear "2020" @default.
- W3021678865 type Work @default.
- W3021678865 sameAs 3021678865 @default.
- W3021678865 citedByCount "0" @default.
- W3021678865 crossrefType "journal-article" @default.
- W3021678865 hasAuthorship W3021678865A5010360658 @default.
- W3021678865 hasConcept C107673813 @default.
- W3021678865 hasConcept C11413529 @default.
- W3021678865 hasConcept C119857082 @default.
- W3021678865 hasConcept C14036430 @default.
- W3021678865 hasConcept C151319957 @default.
- W3021678865 hasConcept C154945302 @default.
- W3021678865 hasConcept C160234255 @default.
- W3021678865 hasConcept C162376815 @default.
- W3021678865 hasConcept C165696696 @default.
- W3021678865 hasConcept C173608175 @default.
- W3021678865 hasConcept C31258907 @default.
- W3021678865 hasConcept C38652104 @default.
- W3021678865 hasConcept C41008148 @default.
- W3021678865 hasConcept C45374587 @default.
- W3021678865 hasConcept C68339613 @default.
- W3021678865 hasConcept C78458016 @default.
- W3021678865 hasConcept C86803240 @default.
- W3021678865 hasConceptScore W3021678865C107673813 @default.
- W3021678865 hasConceptScore W3021678865C11413529 @default.
- W3021678865 hasConceptScore W3021678865C119857082 @default.
- W3021678865 hasConceptScore W3021678865C14036430 @default.
- W3021678865 hasConceptScore W3021678865C151319957 @default.
- W3021678865 hasConceptScore W3021678865C154945302 @default.
- W3021678865 hasConceptScore W3021678865C160234255 @default.
- W3021678865 hasConceptScore W3021678865C162376815 @default.
- W3021678865 hasConceptScore W3021678865C165696696 @default.
- W3021678865 hasConceptScore W3021678865C173608175 @default.
- W3021678865 hasConceptScore W3021678865C31258907 @default.
- W3021678865 hasConceptScore W3021678865C38652104 @default.
- W3021678865 hasConceptScore W3021678865C41008148 @default.
- W3021678865 hasConceptScore W3021678865C45374587 @default.
- W3021678865 hasConceptScore W3021678865C68339613 @default.
- W3021678865 hasConceptScore W3021678865C78458016 @default.
- W3021678865 hasConceptScore W3021678865C86803240 @default.
- W3021678865 hasLocation W30216788651 @default.
- W3021678865 hasOpenAccess W3021678865 @default.
- W3021678865 hasPrimaryLocation W30216788651 @default.
- W3021678865 hasRelatedWork W1554288356 @default.
- W3021678865 hasRelatedWork W1960700909 @default.
- W3021678865 hasRelatedWork W2210864573 @default.
- W3021678865 hasRelatedWork W2247380138 @default.
- W3021678865 hasRelatedWork W2287866814 @default.
- W3021678865 hasRelatedWork W2313049652 @default.
- W3021678865 hasRelatedWork W2405778879 @default.
- W3021678865 hasRelatedWork W2599969863 @default.
- W3021678865 hasRelatedWork W2726321600 @default.
- W3021678865 hasRelatedWork W2741243237 @default.
- W3021678865 hasRelatedWork W2752585447 @default.
- W3021678865 hasRelatedWork W2805880533 @default.
- W3021678865 hasRelatedWork W2904613663 @default.
- W3021678865 hasRelatedWork W2963183931 @default.
- W3021678865 hasRelatedWork W2972096277 @default.
- W3021678865 hasRelatedWork W2979915069 @default.
- W3021678865 hasRelatedWork W2985663642 @default.
- W3021678865 hasRelatedWork W3014266848 @default.
- W3021678865 hasRelatedWork W3156336862 @default.
- W3021678865 hasRelatedWork W3195699808 @default.
- W3021678865 isParatext "false" @default.
- W3021678865 isRetracted "false" @default.
- W3021678865 magId "3021678865" @default.
- W3021678865 workType "article" @default.