NIPS Proceedingsβ

Eric P. Xing

23 Papers

  • Learning Data Manipulation for Augmentation and Weighting (2019)
  • Learning Robust Global Representations by Penalizing Local Predictive Power (2019)
  • Learning Sample-Specific Models with Low-Rank Personalized Regression (2019)
  • Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019)
  • DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018)
  • Deep Generative Models with Learnable Knowledge Constraints (2018)
  • Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation (2018)
  • Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems (2018)
  • Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018)
  • Symbolic Graph Reasoning Meets Convolutions (2018)
  • The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models (2018)
  • Unsupervised Text Style Transfer using Language Models as Discriminators (2018)
  • Structured Generative Adversarial Networks (2017)
  • Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices (2016)
  • Stochastic Variational Deep Kernel Learning (2016)
  • Variance Reduction in Stochastic Gradient Langevin Dynamics (2016)
  • The Human Kernel (2015)
  • Dependent nonparametric trees for dynamic hierarchical clustering (2014)
  • On Model Parallelization and Scheduling Strategies for Distributed Machine Learning (2014)
  • A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks (2013)
  • More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server (2013)
  • Restricting exchangeable nonparametric distributions (2013)
  • Variance Reduction for Stochastic Gradient Optimization (2013)