H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 95 Citations 36,995 449 World Ranking 194 National Ranking 117

Research.com Recognitions

Awards & Achievements

2016 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to statistical machine learning, its theoretical analysis, new algorithms for learning probabilistic models, and applications of these to important problems in biology, social network analysis, natural language processing and beyond; and to the development of new architecture, system platform, and theory for distributed machine learning programs on large scale applications.

2008 - Fellow of Alfred P. Sloan Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Eric P. Xing mostly deals with Artificial intelligence, Machine learning, Inference, Theoretical computer science and Pattern recognition. His biological study spans a wide range of topics, including Data mining and Natural language processing. His Machine learning study incorporates themes from Contextual image classification, Data parallelism, Regression and Set.

His Inference study combines topics in areas such as Topic model, Latent Dirichlet allocation, Latent variable, Support vector machine and Probabilistic logic. The Theoretical computer science study combines topics in areas such as Scalability, Dependency grammar, Parsing, Graphical model and Network topology. His Pattern recognition research includes elements of Feature, Set, Tree, Image and Lasso.

His most cited work include:

  • Distance Metric Learning with Application to Clustering with Side-Information (2389 citations)
  • Mixed Membership Stochastic Blockmodels (1587 citations)
  • Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification (823 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial intelligence, Machine learning, Inference, Pattern recognition and Theoretical computer science. His Artificial intelligence research focuses on Natural language processing and how it relates to Semantics. His Machine learning research incorporates themes from Domain, Generalization and Bayesian probability.

His work carried out in the field of Inference brings together such families of science as Algorithm, Data mining, Cluster analysis and Markov chain Monte Carlo. Eric P. Xing is interested in Segmentation, which is a branch of Pattern recognition. Topic model is a subfield of Information retrieval that Eric P. Xing explores.

He most often published in these fields:

  • Artificial intelligence (53.53%)
  • Machine learning (31.22%)
  • Inference (16.83%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (53.53%)
  • Machine learning (31.22%)
  • Deep learning (5.90%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Machine learning, Deep learning, Robustness and Artificial neural network. His research in Artificial intelligence is mostly focused on Convolutional neural network. His research in Machine learning intersects with topics in Sample, Black box and Benchmark.

His studies deal with areas such as Segmentation and Distributed computing as well as Deep learning. The various areas that Eric P. Xing examines in his Robustness study include Algorithm and Regularization. His Artificial neural network research includes themes of Python, Scalability and Hyperparameter.

Between 2018 and 2021, his most popular works were:

  • Theoretically Principled Trade-off between Robustness and Accuracy (348 citations)
  • Theoretically Principled Trade-off between Robustness and Accuracy (204 citations)
  • Rethinking Knowledge Graph Propagation for Zero-Shot Learning (112 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of study are Artificial intelligence, Machine learning, Convolutional neural network, Robustness and Differentiable function. His research on Artificial intelligence frequently connects to adjacent areas such as Domain. He combines subjects such as Black box, Benchmark, Set and Bayesian inference with his study of Machine learning.

His Convolutional neural network study combines topics from a wide range of disciplines, such as Contextual image classification, Salient and Generalization. Eric P. Xing interconnects Graphical model, Probability distribution and Algorithm in the investigation of issues within Robustness. His Pattern recognition research integrates issues from Pixel, Feature and Representation.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Distance Metric Learning with Application to Clustering with Side-Information

Eric P. Xing;Michael I. Jordan;Stuart J Russell;Andrew Y. Ng.
neural information processing systems (2002)

3303 Citations

Mixed Membership Stochastic Blockmodels

Edoardo M. Airoldi;David M. Blei;Stephen E. Fienberg;Eric P. Xing.
Journal of Machine Learning Research (2008)

1893 Citations

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

Li-jia Li;Hao Su;Li Fei-fei;Eric P. Xing.
neural information processing systems (2010)

1081 Citations

Feature selection for high-dimensional genomic microarray data

Eric P. Xing;Michael I. Jordan;Richard M. Karp.
international conference on machine learning (2001)

836 Citations

A Latent Variable Model for Geographic Lexical Variation

Jacob Eisenstein;Brendan O'Connor;Noah A. Smith;Eric P. Xing.
empirical methods in natural language processing (2010)

751 Citations

Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping

Seyoung Kim;Eric P. Xing.
The Annals of Applied Statistics (2012)

529 Citations

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

Qirong Ho;James Cipar;Henggang Cui;Seunghak Lee.
neural information processing systems (2013)

499 Citations

MedLDA: maximum margin supervised topic models

Jun Zhu;Amr Ahmed;Eric P. Xing.
Journal of Machine Learning Research (2012)

474 Citations

Joint latent topic models for text and citations

Ramesh M. Nallapati;Amr Ahmed;Eric P. Xing;William W. Cohen.
knowledge discovery and data mining (2008)

466 Citations

Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity

Seyoung Kim;Eric P. Xing.
international conference on machine learning (2010)

442 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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