| 2026 | AAAI | Vision-G1: Towards General Reasoning Vision-Language Models via Reinforcement Learning. | Yuheng Zha, Kun Zhou, Yujia Wu, Yushu Wang, Jie Feng, Zhi Xu, Shibo Hao, Zhengzhong Liu, Eric P. Xing, Zhiting Hu |
| 2026 | ACL | Advancing Reasoning in Diffusion Language Models with Denoising Process Rewards. | Shaoan Xie, Lingjing Kong, Xiangchen Song, Xinshuai Dong, Guangyi Chen, Eric P. Xing, Kun Zhang |
| 2026 | ACL | Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models. | Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu |
| 2026 | KDD | In-context Learning of Evolving Data Streams with Tabular Foundational Models. | Afonso Loureno, Joo Gama, Eric P. Xing, Goreti Marreiros |
| 2025 | ACL | Token Level Routing Inference System for Edge Devices. | Jianshu She, Wenhao Zheng, Zhengzhong Liu, Hongyi Wang, Eric P. Xing, Huaxiu Yao, Qirong Ho |
| 2025 | COLING | Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect. | Guokan Shang, Hadi Abdine, Yousef Khoubrane, Amr Mohamed, Yassine Abbahaddou, Sofiane Ennadir, Imane Momayiz, Xuguang Ren, Eric Moulines, Preslav Nakov, Michalis Vazirgiannis, Eric P. Xing |
| 2025 | CVPR | VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos. | Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric P. Xing, Fahad Shahbaz Khan, Salman H. Khan |
| 2025 | CVPR | SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. | Shaoan Xie, Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang |
| 2025 | EMNLP | Linear Steerability in Language Models: When It Emerges and How It Evolves. | Jianshu She, Xinyue Li, Eric P. Xing, Zhengzhong Liu, Qirong Ho |
| 2025 | ICCV | Imore: Implicit Program-Guided Reasoning for Human Motion QA. | Chen Li, Chinthani Sugandhika, Ee Yeo Keat, Eric P. Xing, Hao Zhang, Hong Yang, Deepu Rajan, Basura Fernando |
| 2025 | ICLR | Scaling Long Context Training Data by Long-Distance Referrals. | Yonghao Zhuang, Lanxiang Hu, Longfei Yun, Souvik Kundu, Zhengzhong Liu, Eric P. Xing, Hao Zhang |
| 2025 | ICLR | 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 |
| 2025 | ICLR | 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 |
| 2025 | ICML | Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism. | Aviv Bick, Eric P. Xing, Albert Gu |
| 2025 | ICML | Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models. | Yuan Li, Zhengzhong Liu, Eric P. Xing |
| 2025 | ICML | Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs. | Bowen Tan, Zheng Xu, Eric P. Xing, Zhiting Hu, Shanshan Wu |
| 2025 | ICML | Learning Vision and Language Concepts for Controllable Image Generation. | Shaoan Xie, Lingjing Kong, Yujia Zheng, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang |
| 2024 | BMVC | MixMask: Revisiting Masking Strategy for Siamese ConvNets. | Kirill Vishniakov, Eric P. Xing, Zhiqiang Shen |
| 2024 | CVPR | Efficient Test-Time Adaptation of Vision-Language Models. | Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik, Eric P. Xing |
| 2024 | CVPR | GLaMM: Pixel Grounding Large Multimodal Model. | Hanoona Abdul Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman M. Shaker, Salman H. Khan, Hisham Cholakkal, Rao Muhammad Anwer, Eric P. Xing, Ming-Hsuan Yang, Fahad Shahbaz Khan |
| 2024 | CVPR | FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization. | Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, Eric P. Xing |
| 2024 | EMNLP | Fast Matrix Multiplications for Lookup Table-Quantized LLMs. | Han Guo, William Brandon, Radostin Cholakov, Jonathan Ragan-Kelley, Eric P. Xing, Yoon Kim |
| 2024 | EMNLP | Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models. | Somanshu Singla, Zhen Wang, Tianyang Liu, Abdullah Ashfaq, Zhiting Hu, Eric P. Xing |
| 2024 | ICLR | Fusing Models with Complementary Expertise. | Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin |
| 2024 | ICLR | LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. | Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim |
| 2024 | ICLR | PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. | Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu |
| 2024 | ICLR | LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset. | Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang |
| 2024 | ICML | Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning. | Jannik Deuschel, Caleb Ellington, Yingtao Luo, Benjamin J. Lengerich, Pascal Friederich, Eric P. Xing |
| 2024 | ICML | 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 C. 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 Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao |
| 2024 | ICML | 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 J. McAuley, Zichao Yang, Eric P. Xing, Zhiting Hu |
| 2024 | NAACL | RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs. | Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu |
| 2024 | NAACL | Evaluating Step-by-Step Reasoning through Symbolic Verification. | Yifan Zhang, Hanlin Zhang, Li Li, Eric P. Xing |
| 2024 | NAACL | A Study on the Calibration of In-context Learning. | Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade |
| 2023 | ACL | BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models. | Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu |
| 2023 | ACL | Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. | Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing |
| 2023 | CVPR | KD-DLGAN: Data Limited Image Generation via Knowledge Distillation. | Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric P. Xing |
| 2023 | CVPR | 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 |
| 2023 | CVPR | 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 |
| 2023 | CVPR | 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 |
| 2023 | ICLR | Betty: An Automatic Differentiation Library for Multilevel Optimization. | Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing |
| 2023 | ICLR | Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. | Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing |
| 2023 | ICLR | MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. | Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang |
| 2022 | AAAI | Learning from Mistakes - a Framework for Neural Architecture Search. | Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie |
| 2022 | AISTATS | Dropout as a Regularizer of Interaction Effects. | Benjamin J. Lengerich, Eric P. Xing, Rich Caruana |
| 2022 | CVPR | Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space. | Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric P. Xing |
| 2022 | CVPR | 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 |
| 2022 | CVPR | Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. | Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P. Xing, Zhiqiang Shen |
| 2022 | CVPR | Towards Principled Disentanglement for Domain Generalization. | Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schlkopf, Eric P. Xing |
| 2022 | ECCV | Data-Free Neural Architecture Search via Recursive Label Calibration. | Zechun Liu, Zhiqiang Shen, Yun Long, Eric P. Xing, Kwang-Ting Cheng, Chas Leichner |
| 2022 | ECCV | Sliced Recursive Transformer. | Zhiqiang Shen, Zechun Liu, Eric P. Xing |
| 2022 | ECCV | A Fast Knowledge Distillation Framework for Visual Recognition. | Zhiqiang Shen, Eric P. Xing |
| 2022 | EMNLP | RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning. | Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu |
| 2022 | EMNLP | Efficient (Soft) Q-Learning for Text Generation with Limited Good Data. | Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu |
| 2022 | EMNLP | ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models. | Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu |
| 2022 | ICML | SDQ: Stochastic Differentiable Quantization with Mixed Precision. | Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, Kwang-Ting Cheng |
| 2022 | KDD | Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation. | Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing |
| 2022 | OSDI | Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning. | Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica |
| 2022 | RECOMB | Gene Set Priorization Guided by Regulatory Networks with p-values through Kernel Mixed Model. | Haohan Wang, Oscar L. Lopez, Wei Wu, Eric P. Xing |
| 2022 | UAI | Toward learning human-aligned cross-domain robust models by countering misaligned features. | Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing |
| 2021 | AAAI | Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach. | Seo-Jin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric P. Xing |
| 2021 | ACL | GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning. | Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xing, Liang Lin |
| 2021 | ACL | Towards Visual Question Answering on Pathology Images. | Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric P. Xing, Pengtao Xie |
| 2021 | ACL | On the Generation of Medical Dialogs for COVID-19. | Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric P. Xing, Pengtao Xie |
| 2021 | AISTATS | On Data Efficiency of Meta-learning. | Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar |
| 2021 | EMNLP | Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation. | Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu |
| 2021 | EMNLP | Knowledge-Aware Meta-learning for Low-Resource Text Classification. | Huaxiu Yao, Yingxin Wu, Maruan Al-Shedivat, Eric P. Xing |
| 2021 | ICLR | Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. | Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, Afshin Rostamizadeh |
| 2021 | ICLR | Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. | Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski |
| 2021 | NAACL | Progressive Generation of Long Text with Pretrained Language Models. | Bowen Tan, Zichao Yang, Maruan Al-Shedivat, Eric P. Xing, Zhiting Hu |
| 2021 | OSDI | Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. | Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing |
| 2020 | AISTATS | Distributed, partially collapsed MCMC for Bayesian Nonparametrics. | Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson |
| 2020 | AISTATS | ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. | Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabs Pczos, Jeff Schneider, Eric P. Xing |
| 2020 | AISTATS | Learning Sparse Nonparametric DAGs. | Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing |
| 2020 | CVPR | High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks. | Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing |
| 2020 | ECAI | Adversarial Domain Adaptation Being Aware of Class Relationships. | Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing |
| 2020 | ECCV | Self-challenging Improves Cross-Domain Generalization. | Zeyi Huang, Haohan Wang, Eric P. Xing, Dong Huang |
| 2020 | EMNLP | Record-to-Text Generation with Style Imitation. | Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric P. Xing, Zhiting Hu |
| 2020 | EMNLP | A Data-Centric Framework for Composable NLP Workflows. | Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu |
| 2020 | EMNLP | Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach. | Bowen Tan, Lianhui Qin, Eric P. Xing, Zhiting Hu |
| 2020 | IJCAI | Generalized Zero-Shot Text Classification for ICD Coding. | Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing |
| 2020 | KDD | Learning from All Types of Experiences: A Unifying Machine Learning Perspective. | Zhiting Hu, Eric P. Xing |
| 2020 | RECOMB | Supervised Adversarial Alignment of Single-Cell RNA-seq Data. | Songwei Ge, Haohan Wang, Amir Alavi, Eric P. Xing, Ziv Bar-Joseph |
| 2019 | AAAI | Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. | Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing |
| 2019 | AAAI | 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 |
| 2019 | ACL | Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation. | Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Singh Sachan, Eric P. Xing |
| 2019 | ACL | Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports. | Baoyu Jing, Zeya Wang, Eric P. Xing |
| 2019 | ACL | Target-Guided Open-Domain Conversation. | Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric P. Xing, Zhiting Hu |
| 2019 | CVPR | Rethinking Knowledge Graph Propagation for Zero-Shot Learning. | Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing |
| 2019 | EuroSys | Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory. | Jinliang Wei, Garth A. Gibson, Phillip B. Gibbons, Eric P. Xing |
| 2019 | ICLR | Toward Understanding the Impact of Staleness in Distributed Machine Learning. | Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing |
| 2019 | ICLR | Connecting the Dots Between MLE and RL for Sequence Generation. | Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing |
| 2019 | ICLR | Learning Robust Representations by Projecting Superficial Statistics Out. | Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing |
| 2019 | ICLR | AutoLoss: Learning Discrete Schedule for Alternate Optimization. | Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing |
| 2019 | ICML | Fault Tolerance in Iterative-Convergent Machine Learning. | Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing |
| 2019 | ICML | Theoretically Principled Trade-off between Robustness and Accuracy. | Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan |
| 2019 | MICCAI | Neural Architecture Search for Adversarial Medical Image Segmentation. | Nanqing Dong, Min Xu, Xiaodan Liang, Yiliang Jiang, Wei Dai, Eric P. Xing |
| 2019 | PSB | Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning. | Haohan Wang, Xiang Liu, Yifeng Tao, Wenting Ye, Qiao Jin, William W. Cohen, Eric P. Xing |
| 2019 | PSB | Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications. | Haohan Wang, Zhenglin Wu, Eric P. Xing |
| 2019 | USENIX | STRADS-AP: Simplifying Distributed Machine Learning Programming without Introducing a New Programming Model. | Jin Kyu Kim, Abutalib Aghayev, Garth A. Gibson, Eric P. Xing |
| 2018 | ACL | A Neural Architecture for Automated ICD Coding. | Pengtao Xie, Haoran Shi, Ming Zhang, Eric P. Xing |
| 2018 | ACL | On the Automatic Generation of Medical Imaging Reports. | Baoyu Jing, Pengtao Xie, Eric P. Xing |
| 2018 | BMVC | Few-Shot Semantic Segmentation with Prototype Learning. | Nanqing Dong, Eric P. Xing |
| 2018 | BMVC | Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption. | Peilun Li, Xiaodan Liang, Daoyuan Jia, Eric P. Xing |
| 2018 | BMVC | Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching. | Kaiwen Wang, Xiangrui Zeng, Xiaodan Liang, Zhiguang Huo, Eric P. Xing, Min Xu |
| 2018 | BMVC | Query-Conditioned Three-Player Adversarial Network for Video Summarization. | Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang, Min Tan, Eric P. Xing |
| 2018 | CLOUD | Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. | Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing |
| 2018 | CVPR | Dynamic-Structured Semantic Propagation Network. | Xiaodan Liang, Hongfei Zhou, Eric P. Xing |
| 2018 | ECCV | CIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving. | Xiaodan Liang, Tairui Wang, Luona Yang, Eric P. Xing |
| 2018 | ECCV | Generative Semantic Manipulation with Mask-Contrasting GAN. | Xiaodan Liang, Hao Zhang, Liang Lin, Eric P. Xing |
| 2018 | ECCV | Real-to-Virtual Domain Unification for End-to-End Autonomous Driving. | Luona Yang, Xiaodan Liang, Tairui Wang, Eric P. Xing |
| 2018 | ICIP | Deep Learning Based Supervised Semantic Segmentation of Electron Cryo-Subtomograms. | Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric P. Xing, Min Xu |
| 2018 | ICLR | DiCE: The Infinitely Differentiable Monte-Carlo Estimator. | Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktschel, Eric P. Xing, Shimon Whiteson |
| 2018 | ICLR | On Unifying Deep Generative Models. | Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing |
| 2018 | ICML | DiCE: The Infinitely Differentiable Monte Carlo Estimator. | Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktschel, Eric P. Xing, Shimon Whiteson |
| 2018 | ICML | Gated Path Planning Networks. | Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric P. Xing, Ruslan Salakhutdinov |
| 2018 | ICML | Transformation Autoregressive Networks. | Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabs Pczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider |
| 2018 | ICML | Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. | Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing |
| 2018 | ICML | Nonoverlap-Promoting Variable Selection. | Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric P. Xing |
| 2018 | KDD | Parsing to Programs: A Framework for Situated QA. | Mrinmaya Sachan, Eric P. Xing |
| 2018 | KDD | SysML: On System and Algorithm Co-design for Practical Machine Learning. | Eric P. Xing |
| 2018 | MICCAI | SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays. | Wei Dai, Nanqing Dong, Zeya Wang, Xiaodan Liang, Hao Zhang, Eric P. Xing |
| 2018 | MICCAI | Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio. | Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing |
| 2018 | MICCAI | Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images. | Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing |
| 2018 | NAACL | Self-Training for Jointly Learning to Ask and Answer Questions. | Mrinmaya Sachan, Eric P. Xing |
| 2018 | USENIX | Litz: Elastic Framework for High-Performance Distributed Machine Learning. | Aurick Qiao, Abutalib Aghayev, Weiren Yu, Haoyang Chen, Qirong Ho, Garth A. Gibson, Eric P. Xing |
| 2018 | USENIX | Cavs: An Efficient Runtime System for Dynamic Neural Networks. | Shizhen Xu, Hao Zhang, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P. Xing |
| 2017 | ACL | Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification. | Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric P. Xing |
| 2017 | ACL | A Constituent-Centric Neural Architecture for Reading Comprehension. | Pengtao Xie, Eric P. Xing |
| 2017 | CVPR | Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. | Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing |
| 2017 | CVPR | Interpretable Structure-Evolving LSTM. | Xiaodan Liang, Liang Lin, Xiaohui Shen, Jiashi Feng, Shuicheng Yan, Eric P. Xing |
| 2017 | CVPR | Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection. | Xiaodan Liang, Lisa Lee, Eric P. Xing |
| 2017 | EMNLP | From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems. | Mrinmaya Sachan, Avinava Dubey, Eric P. Xing |
| 2017 | ICCV | Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning. | Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing, Carnegie Mellon |
| 2017 | ICCV | Recurrent Topic-Transition GAN for Visual Paragraph Generation. | Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing |
| 2017 | ICCV | Dual Motion GAN for Future-Flow Embedded Video Prediction. | Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing |
| 2017 | ICCV | Deep Determinantal Point Process for Large-Scale Multi-label Classification. | Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, Eric P. Xing |
| 2017 | ICML | Toward Controlled Generation of Text. | Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing |
| 2017 | ICML | Post-Inference Prior Swapping. | Willie Neiswanger, Eric P. Xing |
| 2017 | ICML | Learning Latent Space Models with Angular Constraints. | Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing |
| 2017 | ICML | Uncorrelation and Evenness: a New Diversity-Promoting Regularizer. | Pengtao Xie, Aarti Singh, Eric P. Xing |
| 2017 | KDD | Efficient Correlated Topic Modeling with Topic Embedding. | Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing |
| 2017 | KDD | PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. | Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing |
| 2017 | KDD | Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling. | Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric P. Xing, Jieping Ye |
| 2017 | UAI | Near-Orthogonality Regularization in Kernel Methods. | Pengtao Xie, Barnabs Pczos, Eric P. Xing |
| 2017 | USENIX | Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. | Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing |
| 2016 | ACL | Harnessing Deep Neural Networks with Logic Rules. | Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, Eric P. Xing |
| 2016 | ACL | Science Question Answering using Instructional Materials. | Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing |
| 2016 | ACL | Easy Questions First? A Case Study on Curriculum Learning for Question Answering. | Mrinmaya Sachan, Eric P. Xing |
| 2016 | ACL | Machine Comprehension using Rich Semantic Representations. | Mrinmaya Sachan, Eric P. Xing |
| 2016 | ACL | Learning Concept Taxonomies from Multi-modal Data. | Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing |
| 2016 | AISTATS | Scalable and Sound Low-Rank Tensor Learning. | Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans |
| 2016 | AISTATS | 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 |
| 2016 | AISTATS | Bayesian Nonparametric Kernel-Learning. | Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabs Pczos, Jeff G. Schneider, Eric P. Xing |
| 2016 | AISTATS | Deep Kernel Learning. | Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing |
| 2016 | AISTATS | On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. | Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing |
| 2016 | CLOUD | Addressing the straggler problem for iterative convergent parallel ML. | Aaron Harlap, Henggang Cui, Wei Dai, Jinliang Wei, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing |
| 2016 | CVPR | They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. | Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing |
| 2016 | CVPR | Closed-Form Training of Mahalanobis Distance for Supervised Clustering. | Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing |
| 2016 | EMNLP | Deep Neural Networks with Massive Learned Knowledge. | Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing |
| 2016 | EuroSys | GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server. | Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing |
| 2016 | EuroSys | STRADS: a distributed framework for scheduled model parallel machine learning. | Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, Eric P. Xing |
| 2016 | ICML | Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. | Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing |
| 2016 | ICML | Diversity-Promoting Bayesian Learning of Latent Variable Models. | Pengtao Xie, Jun Zhu, Eric P. Xing |
| 2016 | IJCAI | Grounding Topic Models with Knowledge Bases. | Zhiting Hu, Gang Luo, Mrinmaya Sachan, Eric P. Xing, Zaiqing Nie |
| 2016 | KDD | Scalable Time-Decaying Adaptive Prediction Algorithm. | Yinyan Tan, Zhe Fan, Guilin Li, Fangshan Wang, Zhengbing Li, Shikai Liu, Qiuling Pan, Eric P. Xing, Qirong Ho |
| 2016 | UAI | 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 |
| 2015 | AAAI | 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 |
| 2015 | AAAI | Mining User Interests from Personal Photos. | Pengtao Xie, Yulong Pei, Yuan Xie, Eric P. Xing |
| 2015 | AAAI | Integrating Image Clustering and Codebook Learning. | Pengtao Xie, Eric P. Xing |
| 2015 | ACL | Entity Hierarchy Embedding. | Zhiting Hu, Poyao Huang, Yuntian Deng, Yingkai Gao, Eric P. Xing |
| 2015 | ACL | Learning Answer-Entailing Structures for Machine Comprehension. | Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing, Matthew Richardson |
| 2015 | AISTATS | Fast Function to Function Regression. | Junier B. Oliva, Willie Neiswanger, Barnabs Pczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider |
| 2015 | AISTATS | Minimizing Nonconvex Non-Separable Functions. | Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing |
| 2015 | CLOUD | Managed communication and consistency for fast data-parallel iterative analytics. | Jinliang Wei, Wei Dai, Aurick Qiao, Qirong Ho, Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing |
| 2015 | ICML | Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. | Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu |
| 2015 | ICML | Large-scale Distributed Dependent Nonparametric Trees. | Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing |
| 2015 | IJCAI | Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. | Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu |
| 2015 | IJCAI | An Active Learning Approach to Coreference Resolution. | Mrinmaya Sachan, Eduard H. Hovy, Eric P. Xing |
| 2015 | KDD | Diversifying Restricted Boltzmann Machine for Document Modeling. | Pengtao Xie, Yuntian Deng, Eric P. Xing |
| 2015 | KDD | Petuum: A New Platform for Distributed Machine Learning on Big Data. | Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu |
| 2015 | KDD | Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data. | Hao Zhang, Gunhee Kim, Eric P. Xing |
| 2015 | KDD | Linear Time Samplers for Supervised Topic Models using Compositional Proposals. | Xun Zheng, Yaoliang Yu, Eric P. Xing |
| 2015 | NAACL | Incorporating Word Correlation Knowledge into Topic Modeling. | Pengtao Xie, Diyi Yang, Eric P. Xing |
| 2015 | RECOMB | An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations. | Seunghak Lee, Aurlie C. Lozano, Prabhanjan Kambadur, Eric P. Xing |
| 2015 | SIGMOD | Community Level Diffusion Extraction. | Zhiting Hu, Junjie Yao, Bin Cui, Eric P. Xing |
| 2015 | WSDM | Big Data: New Paradigm or "Sound and Fury, Signifying Nothing"? | Andrei Z. Broder, Lada A. Adamic, Michael J. Franklin, Maarten de Rijke, Eric P. Xing, Kai Yu |
| 2014 | ACL | Spectral Unsupervised Parsing with Additive Tree Metrics. | Ankur P. Parikh, Shay B. Cohen, Eric P. Xing |
| 2014 | AISTATS | Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. | Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing |
| 2014 | AISTATS | The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. | Willie Neiswanger, Frank D. Wood, Eric P. Xing |
| 2014 | AISTATS | Fast Distribution To Real Regression. | Junier B. Oliva, Willie Neiswanger, Barnabs Pczos, Jeff G. Schneider, Eric P. Xing |
| 2014 | CLOUD | Exploiting iterative-ness for parallel ML computations. | Henggang Cui, Alexey Tumanov, Jinliang Wei, Lianghong Xu, Wei Dai, Jesse Haber-Kucharsky, Qirong Ho, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing |
| 2014 | CVPR | Joint Summarization of Large-Scale Collections of Web Images and Videos for Storyline Reconstruction. | Gunhee Kim, Leonid Sigal, Eric P. Xing |
| 2014 | CVPR | Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos. | Gunhee Kim, Eric P. Xing |
| 2014 | CVPR | Hierarchical Feature Hashing for Fast Dimensionality Reduction. | Bin Zhao, Eric P. Xing |
| 2014 | CVPR | Quasi Real-Time Summarization for Consumer Videos. | Bin Zhao, Eric P. Xing |
| 2014 | EMNLP | Language Modeling with Power Low Rank Ensembles. | Ankur P. Parikh, Avneesh Saluja, Chris Dyer, Eric P. Xing |
| 2014 | PSB | Robust Reverse Engineering of Dynamic Gene Networks Under Sample Size Heterogeneity. | Ankur P. Parikh, Wei Wu, Eric P. Xing |
| 2014 | UAI | Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. | Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing |
| 2014 | UAI | Modeling Citation Networks Using Latent Random Offsets. | Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing |
| 2014 | UAI | Asymptotically Exact, Embarrassingly Parallel MCMC. | Willie Neiswanger, Chong Wang, Eric P. Xing |
| 2014 | WSDM | Visualizing brand associations from web community photos. | Gunhee Kim, Eric P. Xing |
| 2014 | WSDM | Spatial compactness meets topical consistency: jointly modeling links and content for community detection. | Mrinmaya Sachan, Avinava Dubey, Shashank Srivastava, Eric P. Xing, Eduard H. Hovy |
| 2014 | SDM | FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop. | Alex Beutel, Partha Pratim Talukdar, Abhimanu Kumar, Christos Faloutsos, Evangelos E. Papalexakis, Eric P. Xing |
| 2014 | USENIX | Exploiting Bounded Staleness to Speed Up Big Data Analytics. | Henggang Cui, James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Abhimanu Kumar, Jinliang Wei, Wei Dai, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing |
| 2013 | AISTATS | Block Regularized Lasso for Multivariate Multi-Response Linear Regression. | Weiguang Wang, Yingbin Liang, Eric P. Xing |
| 2013 | CVPR | Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines. | Gunhee Kim, Eric P. Xing |
| 2013 | CVPR | Sparse Output Coding for Large-Scale Visual Recognition. | Bin Zhao, Eric P. Xing |
| 2013 | HotOS | Solving the Straggler Problem with Bounded Staleness. | James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Gregory R. Ganger, Garth Gibson, Kimberly Keeton, Eric P. Xing |
| 2013 | ICML | Markov Network Estimation From Multi-attribute Data. | Mladen Kolar, Han Liu, Eric P. Xing |
| 2013 | ICML | An Adaptive Learning Rate for Stochastic Variational Inference. | Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing |
| 2013 | ICML | Hierarchical Tensor Decomposition of Latent Tree Graphical Models. | Le Song, Mariya Ishteva, Ankur P. Parikh, Eric P. Xing, Haesun Park |
| 2013 | ICML | Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. | Sinead Williamson, Avinava Dubey, Eric P. Xing |
| 2013 | IJCAI | Scalable Dynamic Nonparametric Bayesian Models of Content and Users. | Amr Ahmed, Eric P. Xing |
| 2013 | IJCAI | Multi-Modal Distance Metric Learning. | Pengtao Xie, Eric P. Xing |
| 2013 | KDD | Fast structure learning in generalized stochastic processes with latent factors. | Mohammad Taha Bahadori, Yan Liu, Eric P. Xing |
| 2013 | RECOMB | NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources. | Kriti Puniyani, Eric P. Xing |
| 2013 | UAI | Integrating Document Clustering and Topic Modeling. | Pengtao Xie, Eric P. Xing |
| 2013 | WSDM | Time-sensitive web image ranking and retrieval via dynamic multi-task regression. | Gunhee Kim, Eric P. Xing |
| 2013 | SDM | A Nonparametric Mixture Model for Topic Modeling over Time. | Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing |
| 2012 | AAAI | Supervised Probabilistic Robust Embedding with Sparse Noise. | Yu Zhang, Dit-Yan Yeung, Eric P. Xing |
| 2012 | ACCV | Multi-Level Structured Image Coding on High-Dimensional Image Representation. | Li-Jia Li, Jun Zhu, Hao Su, Eric P. Xing, Li Fei-Fei |
| 2012 | ACL | Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus. | Eric P. Xing |
| 2012 | CHI | TopicViz: interactive topic exploration in document collections. | Jacob Eisenstein, Duen Horng Chau, Aniket Kittur, Eric P. Xing |
| 2012 | CVPR | On multiple foreground cosegmentation. | Gunhee Kim, Eric P. Xing |
| 2012 | ECCV | Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models. | Kriti Puniyani, Eric P. Xing |
| 2012 | ICML | Consistent Covariance Selection From Data With Missing Values. | Mladen Kolar, Eric P. Xing |
| 2012 | ICML | Group Sparse Additive Models. | Junming Yin, Xi Chen, Eric P. Xing |
| 2012 | ISIT | Nonparametric decentralized detection based on weighted count kernel. | Jiayao Hu, Yingbin Liang, Eric P. Xing |
| 2012 | KDD | Web image prediction using multivariate point processes. | Gunhee Kim, Li Fei-Fei, Eric P. Xing |
| 2012 | PSB | Finding Genome-Transcriptome-Phenome Associations with Structured Association Mapping and Visualization in GenAMap. | Ross E. Curtis, Junming Yin, Peter Kinnaird, Eric P. Xing |
| 2012 | WWW | Document hierarchies from text and links. | Qirong Ho, Jacob Eisenstein, Eric P. Xing |
| 2012 | UAI | A Spectral Algorithm for Latent Junction Trees. | Ankur P. Parikh, Le Song, Mariya Ishteva, Gabi Teodoru, Eric P. Xing |
| 2011 | ACL | Discovering Sociolinguistic Associations with Structured Sparsity. | Jacob Eisenstein, Noah A. Smith, Eric P. Xing |
| 2011 | CIKM | Max margin learning on domain-independent web information extraction. | Bin Zhao, Xiaoxin Yin, Eric P. Xing |
| 2011 | CVPR | Online detection of unusual events in videos via dynamic sparse coding. | Bin Zhao, Li Fei-Fei, Eric P. Xing |
| 2011 | EMNLP | Structured Databases of Named Entities from Bayesian Nonparametrics. | Jacob Eisenstein, Tae Yano, William W. Cohen, Noah A. Smith, Eric P. Xing |
| 2011 | ICCV | Distributed cosegmentation via submodular optimization on anisotropic diffusion. | Gunhee Kim, Eric P. Xing, Li Fei-Fei, Takeo Kanade |
| 2011 | ICML | Sparse Additive Generative Models of Text. | Jacob Eisenstein, Amr Ahmed, Eric P. Xing |
| 2011 | ICML | Approximating Correlated Equilibria using Relaxations on the Marginal Polytope. | Hetunandan Kamisetty, Eric P. Xing, Christopher James Langmead |
| 2011 | ICML | An Augmented Lagrangian Approach to Constrained MAP Inference. | Andr F. T. Martins, Mrio A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing |
| 2011 | ICML | A Spectral Algorithm for Latent Tree Graphical Models. | Ankur P. Parikh, Le Song, Eric P. Xing |
| 2011 | ICML | Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines. | Jun Zhu, Ning Chen, Eric P. Xing |
| 2011 | KDD | Conditional topical coding: an efficient topic model conditioned on rich features. | Jun Zhu, Ni Lao, Ning Chen, Eric P. Xing |
| 2011 | WWW | Unified analysis of streaming news. | Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alexander J. Smola, Choon Hui Teo |
| 2011 | UAI | Smoothing Proximal Gradient Method for General Structured Sparse Learning. | Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing |
| 2011 | UAI | Sparse Topical Coding. | Jun Zhu, Eric P. Xing |
| 2010 | ECCV | Modeling and Analysis of Dynamic Behaviors of Web Image Collections. | Gunhee Kim, Eric P. Xing, Antonio Torralba |
| 2010 | ECCV | Image Segmentation with Topic Random Field. | Bin Zhao, Li Fei-Fei, Eric P. Xing |
| 2010 | EMNLP | Staying Informed: Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective. | Amr Ahmed, Eric P. Xing |
| 2010 | EMNLP | A Latent Variable Model for Geographic Lexical Variation. | Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, Eric P. Xing |
| 2010 | EMNLP | Turbo Parsers: Dependency Parsing by Approximate Variational Inference. | Andr F. T. Martins, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, Mrio A. T. Figueiredo |
| 2010 | ICML | Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. | Seyoung Kim, Eric P. Xing |
| 2010 | ICML | On Sparse Nonparametric Conditional Covariance Selection. | Mladen Kolar, Ankur P. Parikh, Eric P. Xing |
| 2010 | ICML | Conditional Topic Random Fields. | Jun Zhu, Eric P. Xing |
| 2010 | KDD | Grafting-light: fast, incremental feature selection and structure learning of Markov random fields. | Jun Zhu, Ni Lao, Eric P. Xing |
| 2010 | RECOMB | MoGUL: Detecting Common Insertions and Deletions in a Population. | Seunghak Lee, Eric P. Xing, Michael Brudno |
| 2010 | UAI | Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream. | Amr Ahmed, Eric P. Xing |
| 2009 | ACL | Concise Integer Linear Programming Formulations for Dependency Parsing. | Andr F. T. Martins, Noah A. Smith, Eric P. Xing |
| 2009 | ICML | Dynamic mixed membership blockmodel for evolving networks. | Wenjie Fu, Le Song, Eric P. Xing |
| 2009 | ICML | Polyhedral outer approximations with application to natural language parsing. | Andr F. T. Martins, Noah A. Smith, Eric P. Xing |
| 2009 | ICML | MedLDA: maximum margin supervised topic models for regression and classification. | Jun Zhu, Amr Ahmed, Eric P. Xing |
| 2009 | ICML | On primal and dual sparsity of Markov networks. | Jun Zhu, Eric P. Xing |
| 2009 | KDD | Structured correspondence topic models for mining captioned figures in biological literature. | Amr Ahmed, Eric P. Xing, William W. Cohen, Robert F. Murphy |
| 2009 | KDD | Primal sparse Max-margin Markov networks. | Jun Zhu, Eric P. Xing, Bo Zhang |
| 2008 | ECCV | Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks. | Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xing |
| 2008 | EMNLP | Stacking Dependency Parsers. | Andr F. T. Martins, Dipanjan Das, Noah A. Smith, Eric P. Xing |
| 2008 | ICML | Nonextensive entropic kernels. | Andr F. T. Martins, Mrio A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing |
| 2008 | ICML | Untitled record | Suyash Shringarpure, Eric P. Xing |
| 2008 | ICML | Laplace maximum margin Markov networks. | Jun Zhu, Eric P. Xing, Bo Zhang |
| 2008 | ISMB | Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature. | Lus Pedro Coelho, Amr Ahmed, Andrew Arnold, Joshua D. Kangas, Abdul-Saboor Sheikh, Eric P. Xing, William W. Cohen, Robert F. Murphy |
| 2008 | KDD | Joint latent topic models for text and citations. | Ramesh Nallapati, Amr Ahmed, Eric P. Xing, William W. Cohen |
| 2008 | RECOMB | BayCis: A Bayesian Hierarchical HMM for Cis-Regulatory Module Decoding in Metazoan Genomes. | Tien-ho Lin, Pradipta Ray, Geir Kjetil Sandve, Selen Uguroglu, Eric P. Xing |
| 2008 | UAI | Feature Selection via Block-Regularized Regression. | Seyoung Kim, Eric P. Xing |
| 2008 | SDM | Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process: with Applications to Evolutionary Clustering. | Amr Ahmed, Eric P. Xing |
| 2008 | SDM | Semi-Supervised Learning Based on Semiparametric Regularization. | Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Christos Faloutsos |
| 2007 | CVPR | Learning GMRF Structures for Spatial Priors. | Lie Gu, Eric P. Xing, Takeo Kanade |
| 2007 | ICDM | Sparse Word Graphs: A Scalable Algorithm for Capturing Word Correlations in Topic Models. | Ramesh Nallapati, Amr Ahmed, William W. Cohen, Eric P. Xing |
| 2007 | ICML | Recovering temporally rewiring networks: a model-based approach. | Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing |
| 2007 | ICMLA | Probabilistic Graphical Models-Theory, Algorithm, and Application. | Eric P. Xing |
| 2007 | IROS | Feature selection for grasp recognition from optical markers. | Lillian Y. Chang, Nancy S. Pollard, Tom M. Mitchell, Eric P. Xing |
| 2007 | ISMB | Untitled record | Kyung-Ah Sohn, Eric P. Xing |
| 2007 | KDD | Enhanced max margin learning on multimodal data mining in a multimedia database. | Zhen Guo, Zhongfei Zhang, Eric P. Xing, Christos Faloutsos |
| 2007 | RECOMB | Free Energy Estimates of All-Atom Protein Structures Using Generalized Belief Propagation. | Hetunandan Kamisetty, Eric P. Xing, Christopher James Langmead |
| 2007 | RECOMB | GIMscan: A New Statistical Method for Analyzing Whole-Genome Array CGH Data. | Yanxin Shi, Fan Guo, Wei Wu, Eric P. Xing |
| 2007 | SDM | Harmonium Models for Semantic Video Representation and Classification. | Jun Yang, Yan Liu, Eric P. Xing, Alexander G. Hauptmann |
| 2006 | ACL | BiTAM: Bilingual Topic AdMixture Models for Word Alignment. | Bing Zhao, Eric P. Xing |
| 2006 | ICML | Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis. | Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing |
| 2006 | ICML | Discrete Temporal Models of Social Networks. | Steve Hanneke, Eric P. Xing |
| 2006 | ICML | Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. | Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh |
| 2006 | ISMB | Interpreting anonymous DNA samples from mass disasters - probabilistic forensic inference using genetic markers. | Tien-ho Lin, Eugene W. Myers, Eric P. Xing |
| 2006 | KDD | Automatic mining of fruit fly embryo images. | Jia-Yu Pan, Andr G. R. Balan, Eric P. Xing, Agma J. M. Traina, Christos Faloutsos |
| 2005 | ACL | Bilingual Word Spectral Clustering for Statistical Machine Translation. | Bing Zhao, Eric P. Xing, Alex Waibel |
| 2005 | ICML | Predicting protein folds with structural repeats using a chain graph model. | Yan Liu, Eric P. Xing, Jaime G. Carbonell |
| 2005 | KDD | A latent mixed membership model for relational data. | Edoardo M. Airoldi, David M. Blei, Eric P. Xing, Stephen E. Fienberg |
| 2005 | UAI | Mining Associated Text and Images with Dual-Wing Harmoniums. | Eric P. Xing, Rong Yan, Alexander G. Hauptmann |
| 2004 | ICML | Bayesian haplo-type inference via the dirichlet process. | Eric P. Xing, Roded Sharan, Michael I. Jordan |
| 2004 | UAI | Graph Partition Strategies for Generalized Mean Field Inference. | Eric P. Xing, Michael I. Jordan |
| 2003 | UAI | A generalized mean field algorithm for variational inference in exponential families. | Eric P. Xing, Michael I. Jordan, Stuart Russell |
| 2001 | ICML | Feature selection for high-dimensional genomic microarray data. | Eric P. Xing, Michael I. Jordan, Richard M. Karp |
| 2001 | ISMB | CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. | Eric P. Xing, Richard M. Karp |