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
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 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.
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.
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.
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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)
Mixed Membership Stochastic Blockmodels
Edoardo M. Airoldi;David M. Blei;Stephen E. Fienberg;Eric P. Xing.
Journal of Machine Learning Research (2008)
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)
Feature selection for high-dimensional genomic microarray data
Eric P. Xing;Michael I. Jordan;Richard M. Karp.
international conference on machine learning (2001)
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)
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)
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Qirong Ho;James Cipar;Henggang Cui;Seunghak Lee.
neural information processing systems (2013)
MedLDA: maximum margin supervised topic models
Jun Zhu;Amr Ahmed;Eric P. Xing.
Journal of Machine Learning Research (2012)
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)
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
Seyoung Kim;Eric P. Xing.
international conference on machine learning (2010)
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