Xing, Eric P.

214 publications

ICLR 2025 Causal Representation Learning from Multimodal Biomedical Observations Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang

NeurIPS 2025 Dimensional Collapse in VQVAEs: Evidence and Remedies Jiayou Zhang, Yifan Shen, Guangyi Chen, Le Song, Eric P. Xing

NeurIPS 2025 EvoLM: In Search of Lost Training Dynamics for Language Model Reasoning Zhenting Qi, Fan Nie, Alexandre Alahi, James Zou, Himabindu Lakkaraju, Yilun Du, Eric P. Xing, Sham M. Kakade, Hanlin Zhang

NeurIPS 2025 Faster Video Diffusion with Trainable Sparse Attention Peiyuan Zhang, Yongqi Chen, Haofeng Huang, Will Lin, Zhengzhong Liu, Ion Stoica, Eric P. Xing, Hao Zhang

ICLR 2025 Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju

ICLRW 2025 MobiLlama: Towards Accurate & Lightweight Fully Transparent GPT Omkar Chakradhar Thawakar, Ashmal Vayani, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Michael Felsberg, Timothy Baldwin, Eric P. Xing, Fahad Shahbaz Khan

ICLRW 2025 PRISM: Enhancing Protein Inverse Folding Through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations Sazan Mahbub, Souvik Kundu, Eric P. Xing

NeurIPS 2025 Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Tianjun Yao, Haoxuan Li, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, Zhiqiang Shen

NeurIPS 2025 Revisiting Reinforcement Learning for LLM Reasoning from a Cross-Domain Perspective Zhoujun Cheng, Shibo Hao, Tianyang Liu, Fan Zhou, Yutao Xie, Feng Yao, Yuexin Bian, Nilabjo Dey, Yonghao Zhuang, Yuheng Zha, Yi Gu, Kun Zhou, Yuqi Wang, Yuan Li, Richard Fan, Jianshu She, Chengqian Gao, Abulhair Saparov, Taylor W. Killian, Haonan Li, Mikhail Yurochkin, Eric P. Xing, Zhengzhong Liu, Zhiting Hu

ICLR 2025 Scaling Long Context Training Data by Long-Distance Referrals Yonghao Zhuang, Lanxiang Hu, Longfei Yun, Souvik Kundu, Zhengzhong Liu, Eric P. Xing, Hao Zhang

CVPR 2025 SmartCLIP: Modular Vision-Language Alignment with Identification Guarantees Shaoan Xie, Lingjing Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang

ICLRW 2025 Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs Bowen Tan, Zheng Xu, Eric P. Xing, Zhiting Hu, Shanshan Wu

NeurIPS 2025 What Is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Baker Grosse, Eric P. Xing

NeurIPS 2025 scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery Yiming Gao, Zhen Wang, Jefferson Chen, Mark Antkowiak, Mengzhou Hu, JungHo Kong, Dexter Pratt, Jieyuan Liu, Enze Ma, Zhiting Hu, Eric P. Xing

NeurIPSW 2024 A Large-Scale Foundation Model for RNA Function and Structure Prediction Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb Ellington, Robin Jonathan Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing

NeurIPSW 2024 Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale Caleb Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Eric P. Xing, Le Song

NeurIPSW 2024 CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing Wenhao Zheng, Yixiao Chen, Weitong Zhang, Souvik Kundu, Yun Li, Zhengzhong Liu, Eric P. Xing, Hongyi Wang, Huaxiu Yao

ICML 2024 Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning Jannik Deuschel, Caleb Ellington, Yingtao Luo, Ben Lengerich, Pascal Friederich, Eric P. Xing

ICLRW 2024 Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju

NeurIPSW 2024 From One to Zero: RAG-IM Adapts Language Models for Interpretable Zero-Shot Clinical Predictions Sazan Mahbub, Caleb Ellington, Sina Alinejad, Kevin Wen, Yingtao Luo, Ben Lengerich, Eric P. Xing

NeurIPSW 2024 From One to Zero: RAG-IM Adapts Language Models for Interpretable Zero-Shot Predictions on Clinical Tabular Data Sazan Mahbub, Caleb Ellington, Sina Alinejad, Kevin Wen, Yingtao Luo, Ben Lengerich, Eric P. Xing

NeurIPS 2024 Learning Discrete Concepts in Latent Hierarchical Models Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang

NeurIPSW 2024 Mixture of Experts Enable Efficient and Effective Protein Understanding and Design Ning Sun, Shuxian Zou, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, Eric P. Xing

ICML 2024 Position: TrustLLM: Trustworthiness in Large Language Models Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Yang Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao

NeurIPSW 2024 Scaling Dense Representations for Single Cell Gene Expression with Transcriptome-Scale Context Nicholas Ho, Caleb Ellington, Jinyu Hou, Sohan Addagudi, Shentong Mo, Tianhua Tao, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, Eric P. Xing

NeurIPS 2024 Towards Understanding Extrapolation: A Causal Lens Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang

NeurIPS 2024 Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu

ICML 2024 Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian Mcauley, Zichao Yang, Eric P. Xing, Zhiting Hu

NeurIPS 2024 Web2Code: A Large-Scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, Eric P. Xing, Xiaodan Liang, Zhiqiang Shen

CVPR 2023 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El Saddik, Shijian Lu, Eric P. Xing

NeurIPSW 2023 A Study on the Calibration of In-Context Learning Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade

NeurIPS 2023 Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer Bowen Tan, Yun Zhu, Lijuan Liu, Eric P. Xing, Zhiting Hu, Jindong Chen

NeurIPS 2023 Counterfactual Generation with Identifiability Guarantees Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang

NeurIPS 2023 FedNAR: Federated Optimization with Normalized Annealing Regularization Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang

NeurIPSW 2023 Fusing Models with Complementary Expertise Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin

NeurIPS 2023 Identification of Nonlinear Latent Hierarchical Models Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang

NeurIPS 2023 Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E Gonzalez, Ion Stoica

CVPR 2023 KD-DLGAN: Data Limited Image Generation via Knowledge Distillation Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric P. Xing

NeurIPSW 2023 LightSeq: : Sequence Level Parallelism for Distributed Training of Long Context Transformers Dacheng Li, Rulin Shao, Anze Xie, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Xuezhe Ma, Hao Zhang

NeurIPS 2023 Making Scalable Meta Learning Practical Sang Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing

NeurIPS 2023 Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale from a New Perspective Zeyuan Yin, Eric P. Xing, Zhiqiang Shen

CVPR 2023 StyleRF: Zero-Shot 3D Style Transfer of Neural Radiance Fields Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El Saddik, Shijian Lu, Eric P. Xing

NeurIPS 2023 Temporally Disentangled Representation Learning Under Unknown Nonstationarity Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang

CVPR 2023 Understanding Masked Autoencoders via Hierarchical Latent Variable Models Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang

NeurIPS 2023 Weakly Supervised 3D Open-Vocabulary Segmentation Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu, Yingchen Yu, Abdulmotaleb El Saddik, Christian Theobalt, Eric P. Xing, Shijian Lu

NeurIPS 2022 AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang

AAAI 2022 Learning from Mistakes - A Framework for Neural Architecture Search Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie

NeurIPS 2022 Masked Generative Adversarial Networks Are Data-Efficient Generation Learners Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric P. Xing

CVPR 2022 Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P. Xing, Zhiqiang Shen

NeurIPS 2022 Rare Gems: Finding Lottery Tickets at Initialization Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris Papailiopoulos

CVPR 2022 The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing

UAI 2022 Toward Learning Human-Aligned Cross-Domain Robust Models by Countering Misaligned Features Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing

CVPR 2022 Towards Principled Disentanglement for Domain Generalization Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

CVPR 2022 Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric P. Xing

AAAI 2021 Explaining a Black-Box by Using a Deep Variational Information Bottleneck Approach Seo-Jin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric P. Xing

NeurIPS 2021 Multi-Task Learning of Order-Consistent Causal Graphs Xinshi Chen, Haoran Sun, Caleb Ellington, Eric P. Xing, Le Song

NeurIPS 2020 AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing

IJCAI 2020 Generalized Zero-Shot Text Classification for ICD Coding Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing

NeurIPS 2020 Improving GAN Training with Probability Ratio Clipping and Sample Reweighting Yue Wu, Pan Zhou, Andrew G Wilson, Eric P. Xing, Zhiting Hu

NeurIPS 2020 Regularizing Black-Box Models for Improved Interpretability Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar

ECCV 2020 Self-Challenging Improves Cross-Domain Generalization Zeyi Huang, Haohan Wang, Eric P. Xing, Dong Huang

ICLR 2020 Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients Haohan Wang, Xindi Wu, Songwei Ge, Zachary C. Lipton, Eric P. Xing

JMLR 2020 Tuning Hyperparameters Without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

ICLRW 2019 Connecting the Dots Between MLE and RL for Sequence Generation Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

AAAI 2019 Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing

NeurIPS 2019 Learning Data Manipulation for Augmentation and Weighting Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, Eric P Xing

NeurIPS 2019 Learning Robust Global Representations by Penalizing Local Predictive Power Haohan Wang, Songwei Ge, Zachary Lipton, Eric P Xing

ICLR 2019 Learning Robust Representations by Projecting Superficial Statistics Out Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing

NeurIPS 2019 Learning Sample-Specific Models with Low-Rank Personalized Regression Ben Lengerich, Bryon Aragam, Eric P Xing

MLHC 2019 Multimodal Machine Learning for Automated ICD Coding Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing

NeurIPS 2019 Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P Xing, Clark Glymour

AAAI 2019 What if We Simply Swap the Two Text Fragments? a Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks Haohan Wang, Da Sun, Eric P. Xing

NeurIPS 2018 DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing

NeurIPS 2018 Deep Generative Models with Learnable Knowledge Constraints Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Lianhui Qin, Xiaodan Liang, Haoye Dong, Eric P Xing

JMLR 2018 Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing

ECML-PKDD 2018 Domain Adaption in One-Shot Learning Nanqing Dong, Eric P. Xing

MLHC 2018 Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition Devendra Singh Sachan, Pengtao Xie, Mrinmaya Sachan, Eric P. Xing

NeurIPS 2018 Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P Xing

NeurIPS 2018 Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P Xing

NeurIPS 2018 Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing

ICLR 2018 On Unifying Deep Generative Models Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

NeurIPS 2018 Symbolic Graph Reasoning Meets Convolutions Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P Xing

NeurIPS 2018 The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K Ravikumar, Eric P Xing

NeurIPS 2018 Unsupervised Text Style Transfer Using Language Models as Discriminators Zichao Yang, Zhiting Hu, Chris Dyer, Eric P Xing, Taylor Berg-Kirkpatrick

ICCV 2017 Deep Determinantal Point Process for Large-Scale Multi-Label Classification Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, Eric P. Xing

CVPR 2017 Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection Xiaodan Liang, Lisa Lee, Eric P. Xing

ICCV 2017 Dual Motion GAN for Future-Flow Embedded Video Prediction Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing

CVPR 2017 Efficient Multiple Instance Metric Learning Using Weakly Supervised Data Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing

CVPR 2017 Interpretable Structure-Evolving LSTM Xiaodan Liang, Liang Lin, Xiaohui Shen, Jiashi Feng, Shuicheng Yan, Eric P. Xing

ICML 2017 Learning Latent Space Models with Angular Constraints Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing

JMLR 2017 Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing

UAI 2017 Near-Orthogonality Regularization in Kernel Methods Pengtao Xie, Barnabás Póczos, Eric P. Xing

ICCV 2017 Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing

ICCV 2017 Recurrent Topic-Transition GAN for Visual Paragraph Generation Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing

NeurIPS 2017 Structured Generative Adversarial Networks Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P Xing

ICML 2017 Toward Controlled Generation of Text Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing

ICML 2017 Uncorrelation and Evenness: A New Diversity-Promoting Regularizer Pengtao Xie, Aarti Singh, Eric P. Xing

AISTATS 2016 Bayesian Nonparametric Kernel-Learning Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

CVPR 2016 Closed-Form Training of Mahalanobis Distance for Supervised Clustering Marc T. Law, YaoLiang Yu, Matthieu Cord, Eric P. Xing

AISTATS 2016 Deep Kernel Learning Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

IJCAI 2016 Grounding Topic Models with Knowledge Bases Zhiting Hu, Gang Luo, Mrinmaya Sachan, Eric P. Xing, Zaiqing Nie

JMLR 2016 Latent Space Inference of Internet-Scale Networks Qirong Ho, Junming Yin, Eric P. Xing

NeurIPS 2016 Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P Xing

UAI 2016 Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing

AISTATS 2016 On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing

AISTATS 2016 Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing

AISTATS 2016 Scalable and Sound Low-Rank Tensor Learning Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans

NeurIPS 2016 Stochastic Variational Deep Kernel Learning Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P Xing

CVPR 2016 They Are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers Xiaojun Chang, Yao-Liang Yu, Yi Yang, Eric P. Xing

NeurIPS 2016 Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, Eric P Xing

JMLR 2015 AD3: Alternating Directions Dual Decomposition for MAP Inference in Graphical Models André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing

IJCAI 2015 An Active Learning Approach to Coreference Resolution Mrinmaya Sachan, Eduard H. Hovy, Eric P. Xing

AISTATS 2015 Fast Function to Function Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

AAAI 2015 High-Performance Distributed ML at Scale Through Parameter Server Consistency Models Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth A. Gibson, Eric P. Xing

AAAI 2015 Integrating Image Clustering and Codebook Learning Pengtao Xie, Eric P. Xing

AISTATS 2015 Minimizing Nonconvex Non-Separable Functions Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing

AAAI 2015 Mining User Interests from Personal Photos Pengtao Xie, Yulong Pei, Yuan Xie, Eric P. Xing

IJCAI 2015 Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu

NeurIPS 2015 The Human Kernel Andrew G Wilson, Christoph Dann, Chris Lucas, Eric P Xing

UAI 2014 Asymptotically Exact, Embarrassingly Parallel MCMC Willie Neiswanger, Chong Wang, Eric P. Xing

JMLR 2014 Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs Jun Zhu, Ning Chen, Eric P. Xing

NeurIPS 2014 Dependent Nonparametric Trees for Dynamic Hierarchical Clustering Kumar Avinava Dubey, Qirong Ho, Sinead A Williamson, Eric P Xing

AISTATS 2014 Fast Distribution to Real Regression Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

AISTATS 2014 Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing

JMLR 2014 Graph Estimation from Multi-Attribute Data Mladen Kolar, Han Liu, Eric P. Xing

CVPR 2014 Hierarchical Feature Hashing for Fast Dimensionality Reduction Bin Zhao, Eric P. Xing

CVPR 2014 Joint Summarization of Large-Scale Collections of Web Images and Videos for Storyline Reconstruction Gunhee Kim, Leonid Sigal, Eric P. Xing

UAI 2014 Modeling Citation Networks Using Latent Random Offsets Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing

NeurIPS 2014 On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A Gibson, Eric P Xing

UAI 2014 Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing

CVPR 2014 Quasi Real-Time Summarization for Consumer Videos Bin Zhao, Eric P. Xing

CVPR 2014 Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos Gunhee Kim, Eric P. Xing

AISTATS 2014 The Dependent Dirichlet Process Mixture of Objects for Detection-Free Tracking and Object Modeling Willie Neiswanger, Frank D. Wood, Eric P. Xing

NeurIPS 2013 A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P Xing

AISTATS 2013 Block Regularized Lasso for Multivariate Multi-Response Linear Regression Weiguang Wang, Yingbin Liang, Eric P. Xing

ICCVW 2013 Discovering Pictorial Brand Associations from Large-Scale Online Image Data Gunhee Kim, Eric P. Xing

UAI 2013 Integrating Document Clustering and Topic Modeling Pengtao Xie, Eric P. Xing

CVPR 2013 Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines Gunhee Kim, Eric P. Xing

NeurIPS 2013 More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A Gibson, Greg Ganger, Eric P Xing

IJCAI 2013 Multi-Modal Distance Metric Learning Pengtao Xie, Eric P. Xing

NeurIPS 2013 Restricting Exchangeable Nonparametric Distributions Sinead A Williamson, Steve N MacEachern, Eric P Xing

IJCAI 2013 Scalable Dynamic Nonparametric Bayesian Models of Content and Users Amr Ahmed, Eric P. Xing

CVPR 2013 Sparse Output Coding for Large-Scale Visual Recognition Bin Zhao, Eric P. Xing

NeurIPS 2013 Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J Smola, Eric P Xing

UAI 2012 A Spectral Algorithm for Latent Junction Trees Ankur P. Parikh, Le Song, Mariya Ishteva, Gabi Teodoru, Eric P. Xing

ICML 2012 Consistent Covariance Selection from Data with Missing Values Mladen Kolar, Eric P. Xing

ICML 2012 Group Sparse Additive Models Junming Yin, Xi Chen, Eric P. Xing

ECCV 2012 Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models Kriti Puniyani, Eric P. Xing

JMLR 2012 MedLDA: Maximum Margin Supervised Topic Models Jun Zhu, Amr Ahmed, Eric P. Xing

NeurIPS 2012 Monte Carlo Methods for Maximum Margin Supervised Topic Models Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing

CVPR 2012 On Multiple Foreground Cosegmentation Gunhee Kim, Eric P. Xing

NeurIPS 2012 On Triangular Versus Edge Representations --- Towards Scalable Modeling of Networks Qirong Ho, Junming Yin, Eric P. Xing

AAAI 2012 Supervised Probabilistic Robust Embedding with Sparse Noise Yu Zhang, Dit-Yan Yeung, Eric P. Xing

NeurIPS 2012 Symmetric Correspondence Topic Models for Multilingual Text Analysis Kosuke Fukumasu, Koji Eguchi, Eric P. Xing

ICML 2011 A Spectral Algorithm for Latent Tree Graphical Models Ankur P. Parikh, Le Song, Eric P. Xing

ICML 2011 An Augmented Lagrangian Approach to Constrained MAP Inference André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing

ICML 2011 Approximating Correlated Equilibria Using Relaxations on the Marginal Polytope Hetunandan Kamisetty, Eric P. Xing, Christopher James Langmead

ICCV 2011 Distributed Cosegmentation via Submodular Optimization on Anisotropic Diffusion Gunhee Kim, Eric P. Xing, Li Fei-Fei, Takeo Kanade

NeurIPS 2011 Infinite Latent SVM for Classification and Multi-Task Learning Jun Zhu, Ning Chen, Eric P. Xing

ICML 2011 Infinite SVM: A Dirichlet Process Mixture of Large-Margin Kernel Machines Jun Zhu, Ning Chen, Eric P. Xing

NeurIPS 2011 Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric P. Xing, Ankur P. Parikh

NeurIPS 2011 Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric P. Xing

AISTATS 2011 On Time Varying Undirected Graphs Mladen Kolar, Eric P. Xing

CVPR 2011 Online Detection of Unusual Events in Videos via Dynamic Sparse Coding Bin Zhao, Li Fei-Fei, Eric P. Xing

UAI 2011 Smoothing Proximal Gradient Method for General Structured Sparse Learning Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing

ICML 2011 Sparse Additive Generative Models of Text Jacob Eisenstein, Amr Ahmed, Eric P. Xing

UAI 2011 Sparse Topical Coding Jun Zhu, Eric P. Xing

NeurIPS 2010 Adaptive Multi-Task Lasso: With Application to eQTL Detection Seunghak Lee, Jun Zhu, Eric P. Xing

ICML 2010 Conditional Topic Random Fields Jun Zhu, Eric P. Xing

ECCV 2010 Image Segmentation with Topic Random Field Bin Zhao, Li Fei-Fei, Eric P. Xing

NeurIPS 2010 Large Margin Learning of Upstream Scene Understanding Models Jun Zhu, Li-jia Li, Li Fei-fei, Eric P. Xing

ECCV 2010 Modeling and Analysis of Dynamic Behaviors of Web Image Collections Gunhee Kim, Eric P. Xing, Antonio Torralba

NeurIPS 2010 Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification Li-jia Li, Hao Su, Li Fei-fei, Eric P. Xing

ICML 2010 On Sparse Nonparametric Conditional Covariance Selection Mladen Kolar, Ankur P. Parikh, Eric P. Xing

NeurIPS 2010 Predictive Subspace Learning for Multi-View Data: A Large Margin Approach Ning Chen, Jun Zhu, Eric P. Xing

UAI 2010 Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream Amr Ahmed, Eric P. Xing

ICML 2010 Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity Seyoung Kim, Eric P. Xing

ICML 2009 Dynamic Mixed Membership Blockmodel for Evolving Networks Wenjie Fu, Le Song, Eric P. Xing

NeurIPS 2009 Heterogeneous Multitask Learning with Joint Sparsity Constraints Xiaolin Yang, Seyoung Kim, Eric P. Xing

JMLR 2009 Maximum Entropy Discrimination Markov Networks Jun Zhu, Eric P. Xing

ICML 2009 MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification Jun Zhu, Amr Ahmed, Eric P. Xing

AISTATS 2009 Network Completion and Survey Sampling Steve Hanneke, Eric P. Xing

JMLR 2009 Nonextensive Information Theoretic Kernels on Measures André F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mário A. T. Figueiredo

ICML 2009 On Primal and Dual Sparsity of Markov Networks Jun Zhu, Eric P. Xing

ICML 2009 Polyhedral Outer Approximations with Application to Natural Language Parsing André F. T. Martins, Noah A. Smith, Eric P. Xing

NeurIPS 2009 Sparsistent Learning of Varying-Coefficient Models with Structural Changes Mladen Kolar, Le Song, Eric P. Xing

NeurIPS 2009 Time-Varying Dynamic Bayesian Networks Le Song, Mladen Kolar, Eric P. Xing

ECML-PKDD 2008 A Joint Topic and Perspective Model for Ideological Discourse Wei-Hao Lin, Eric P. Xing, Alexander G. Hauptmann

UAI 2008 Feature Selection via Block-Regularized Regression Seyoung Kim, Eric P. Xing

ICML 2008 Laplace Maximum Margin Markov Networks Jun Zhu, Eric P. Xing, Bo Zhang

JMLR 2008 Mixed Membership Stochastic Blockmodels Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing

NeurIPS 2008 Mixed Membership Stochastic Blockmodels Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing

ICML 2008 Nonextensive Entropic Kernels André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing

NeurIPS 2008 Partially Observed Maximum Entropy Discrimination Markov Networks Jun Zhu, Eric P. Xing, Bo Zhang

ECCV 2008 Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xing

ICML 2008 mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations Suyash Shringarpure, Eric P. Xing

NeurIPS 2007 HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation Bing Zhao, Eric P. Xing

CVPR 2007 Learning GMRF Structures for Spatial Priors Lie Gu, Eric P. Xing, Takeo Kanade

ICML 2007 Recovering Temporally Rewiring Networks: A Model-Based Approach Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing

AISTATS 2007 Seeking the Truly Correlated Topic Posterior - On Tight Approximate Inference of Logistic-Normal Admixture Model Amr Ahmed, Eric P. Xing

ICML 2006 Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh

ICML 2006 Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing

ICML 2006 Discrete Temporal Models of Social Networks Steve Hanneke, Eric P. Xing

NeurIPS 2006 Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space Kyung-ah Sohn, Eric P. Xing

ICML 2006 Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Anna Goldenberg, Eric P. Xing, Alice X. Zheng

NeurIPS 2005 From Lasso Regression to Feature Vector Machine Fan Li, Yiming Yang, Eric P. Xing

UAI 2005 Mining Associated Text and Images with Dual-Wing Harmoniums Eric P. Xing, Rong Yan, Alexander G. Hauptmann

ICML 2005 Predicting Protein Folds with Structural Repeats Using a Chain Graph Model Yan Liu, Eric P. Xing, Jaime G. Carbonell

ICML 2004 Bayesian Haplo-Type Inference via the Dirichlet Process Eric P. Xing, Roded Sharan, Michael I. Jordan

UAI 2004 Graph Partition Strategies for Generalized Mean Field Inference Eric P. Xing, Michael I. Jordan

UAI 2003 A Generalized Mean Field Algorithm for Variational Inference in Exponential Families Eric P. Xing, Michael I. Jordan, Stuart Russell

NeurIPS 2002 A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart Russell

NeurIPS 2002 Distance Metric Learning with Application to Clustering with Side-Information Eric P. Xing, Michael I. Jordan, Stuart Russell, Andrew Y. Ng

ICML 2001 Feature Selection for High-Dimensional Genomic Microarray Data Eric P. Xing, Michael I. Jordan, Richard M. Karp