publications

2024

1. Sci. Adv.

   Generative emulation of weather forecast ensembles with diffusion models

   Lizao Li, Robert Carver, Ignacio Lopez-Gomez, and 2 more authors

   Science Advances, 2024

   Bib

   @article{li24seeds,
     author = {Li, Lizao and Carver, Robert and Lopez-Gomez, Ignacio and Sha, Fei and Anderson, John},
     title = {Generative emulation of weather forecast ensembles with diffusion models},
     journal = {Science Advances},
     volume = {10},
     number = {13},
     year = {2024},
     pmid = {38552014}
   }

2. ICML

   DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems

   Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, and 4 more authors

   In Proc. of ICML, 2024

   Bib

   @inproceedings{schiff24dyslim,
     title = {DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems},
     author = {Schiff, Yair and Wan, Zhong Yi and Parker, Jeffrey B. and Hoyer, Stephan and Kuleshov, Volodymyr and Sha, Fei and Zepeda-Nunez, Leonardo},
     year = {2024},
     booktitle = {Proc. of ICML},
   }

3. NAACL

   A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models

   Tiwalayo Eisape, Michael Tessler, Ishita Dasgupta, and 3 more authors

   In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics, 2024

   Bib

   @inproceedings{eisape24syllogistic,
     title = {A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models},
     author = {Eisape, Tiwalayo and Tessler, Michael and Dasgupta, Ishita and Sha, Fei and Steenkiste, Sjoerd and Linzen, Tal},
     booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics},
     year = {2024},
   }

4. AAAI

   V2Meow: Meowing to the Visual Beat via Video-to-Music Generation

   Kun Su, Judith Yue Li, Qingqing Huang, and 8 more authors

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

   Bib

   @inproceedings{su24v2meow,
     title = {V2Meow: Meowing to the Visual Beat via Video-to-Music Generation},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     author = {Su, Kun and Li, Judith Yue and Huang, Qingqing and Kuzmin, Dima and Lee, Joonseok and Donahue, Chris and Sha, Fei and Jansen, Aren and Wang, Yu and Verzetti, Mauro and Denk, Timo},
     year = {2024},
   }

5. JAMES

   WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models

   Stephan Rasp, Stephan Hoyer, Alexander Merose, and 15 more authors

   Journal of Advances in Modeling Earth Systems, 2024

   Bib

   @article{rasp24weatherbench,
     author = {Rasp, Stephan and Hoyer, Stephan and Merose, Alexander and Langmore, Ian and Battaglia, Peter and Russell, Tyler and Sanchez-Gonzalez, Alvaro and Yang, Vivian and Carver, Rob and Agrawal, Shreya and Chantry, Matthew and Ben Bouallegue, Zied and Dueben, Peter and Bromberg, Carla and Sisk, Jared and Barrington, Luke and Bell, Aaron and Sha, Fei},
     title = {WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models},
     journal = {Journal of Advances in Modeling Earth Systems},
     volume = {16},
     number = {6},
     year = {2024},
     doi = {10.1029/2023ms004019}
   }

6. NAACL

   The Impact of Depth on Compositional Generalization in Transformer Language Models

   Jackson Petty, Sjoerd Steenkiste, Ishita Dasgupta, and 3 more authors

   In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

   Bib

   @inproceedings{petty24impact,
     title = {The Impact of Depth on Compositional Generalization in Transformer Language Models},
     author = {Petty, Jackson and Steenkiste, Sjoerd and Dasgupta, Ishita and Sha, Fei and Garrette, Dan and Linzen, Tal},
     booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
     year = {2024},
   }

2023

1. NeurIPS

   Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations

   Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Nunez, and 5 more authors

   In Advances in Neural Information Processing Systems, 2023

   Bib

   @inproceedings{boral23les,
     author = {Boral, Anudhyan and Wan, Zhong Yi and Zepeda-Nunez, Leonardo and Lottes, James and Wang, Qing and Chen, Yi-Fan and Anderson, John and Sha, Fei},
     booktitle = {Advances in Neural Information Processing Systems},
     editor = {Oh, A. and Naumann, T. and Globerson, A. and Saenko, K. and Hardt, M. and Levine, S.},
     pages = {69270--69283},
     publisher = {Curran Associates, Inc.},
     title = {Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations},
     volume = {36},
     year = {2023},
   }

2. NeurIPS

   Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models

   Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, and 4 more authors

   In Advances in Neural Information Processing Systems, 2023

   Bib

   @inproceedings{wan23debias,
     author = {Wan, Zhong Yi and Baptista, Ricardo and Boral, Anudhyan and Chen, Yi-Fan and Anderson, John and Sha, Fei and Zepeda-N\'{u}\~{n}ez, Leonardo},
     booktitle = {Advances in Neural Information Processing Systems},
     editor = {Oh, A. and Naumann, T. and Globerson, A. and Saenko, K. and Hardt, M. and Levine, S.},
     pages = {47749--47763},
     publisher = {Curran Associates, Inc.},
     title = {Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models},
     volume = {36},
     year = {2023}
   }

3. ICML

   User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems

   Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, and 2 more authors

   In ICML, 2023

   Bib

   @inproceedings{finzi23user,
     title = {User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems},
     author = {Finzi, Marc and Boral, Anudhyan and Wilson, Andrew Gordon and Sha, Fei and Zepeda-N\'{u}\~{n}ez, Leonardo},
     booktitle = {ICML},
     year = {2023},
   }

4. ICML

   Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute

   Michiel Jong, Yury Zemlyanskiy, Nicholas FitzGerald, and 4 more authors

   In ICML, 2023

   Bib

   @inproceedings{dejong23lumen,
     title = {Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute},
     author = {de Jong, Michiel and Zemlyanskiy, Yury and Fitz{G}erald, Nicholas and Ainslie, Joshua and Sanghai, Sumit and Sha, Fei and Cohen, William},
     booktitle = {ICML},
     year = {2023},
   }

5. ACL

   FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference

   Michiel Jong, Yury Zemlyanskiy, Joshua Ainslie, and 4 more authors

   In Findings of ACL, 2023

   Bib

   @inproceedings{dejong23fido,
     author = {de Jong, Michiel and Zemlyanskiy, Yury and Ainslie, Joshua and FitzGerald, Nicholas and Sanghai, Sumit and Sha, Fei and Cohen, William},
     booktitle = {Findings of ACL},
     year = {2023},
   }

6. ICLR

   Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems

   Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, and 1 more author

   In ICLR, 2023

   Bib

   @inproceedings{wan23evolve,
     author = {Wan, Zhong Yi and Zepeda-N\'{u}\~{n}ez, Leonardo and Boral, Anudhyan and Sha, Fei},
     title = {Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems},
     booktitle = {ICLR},
     year = {2023},
   }

2022

1. PAMI

   Drinking from a firehose: Continual learning with web-scale natural language

   Hexiang Hu, Ozan Sener, Fei Sha, and 1 more author

   IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

   Bib

   @article{hu22firehose,
     title = {Drinking from a firehose: Continual learning with web-scale natural language},
     author = {Hu, Hexiang and Sener, Ozan and Sha, Fei and Koltun, Vladlen},
     journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
     year = {2022},
     volume = {45},
     no = {5},
   }

2. EMNLP

   Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing

   Linlu Qiu, Peter Shaw, Panupong Pasupat, and 5 more authors

   In EMNLP, 2022

   Bib

   @inproceedings{liu22evaluating,
     author = {Qiu, Linlu and Shaw, Peter and Pasupat, Panupong and Shi, Tianze and Herzig, Jonathan and Pitler, Emily and Sha, Fei and Toutanova, Kristina},
     title = {Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing},
     booktitle = {EMNLP},
     year = {2022},
   }

3. COLING

   Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing

   Yury Zemlyanskiy, Michiel Jong, Joshua Ainslie, and 5 more authors

   In COLING, 2022

   Bib

   @inproceedings{zemlyansky22generate,
     author = {Zemlyanskiy, Yury and de Jong, Michiel and Ainslie, Joshua and Pasupat, Panupong and Shaw, Peter and Qiu, Linlu and Sanghai, Sumit and Sha, Fei},
     title = {Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing},
     booktitle = {COLING},
     year = {2022},
   }

4. NeurIPS

   ALMA: Hierarchical Learning for Composite Multi-Agent Tasks

   Shariq Iqbal, Robby Costales, and Fei Sha

   In NeurIPS, 2022

   Bib

   @inproceedings{iqbal22alma,
     author = {Iqbal, Shariq and Costales, Robby and Sha, Fei},
     title = {{ALMA}: Hierarchical Learning for Composite Multi-Agent Tasks
     },
     booktitle = {NeurIPS},
     year = {2022},
   }

5. ICLR

   Mention Memory: incorporating textual knowledge into Transformers through entity mention attention

   Michiel Jong, Yury Zemlyanskiy, Nicholas FitzGerald, and 2 more authors

   In ICLR, 2022

   Bib

   @inproceedings{dejong22mention,
     title = {Mention Memory: incorporating textual knowledge into Transformers through entity mention attention},
     author = {de Jong, Michiel and Zemlyanskiy, Yury and FitzGerald, Nicholas and Sha, Fei and Cohen, William},
     booktitle = {ICLR},
     year = {2022},
   }

6. ICLR

   Possibility Before Utility: Learning And Using Hierarchical Affordances

   Robby Costales, Shariq Iqbal, and Fei Sha

   2022

   Bib

   @inproceedinngs{costales22affordance,
     title = {Possibility Before Utility: Learning And Using Hierarchical Affordances},
     author = {Costales, Robby and Iqbal, Shariq and Sha, Fei},
     booktitle = {ICLR},
     year = {2022},
   }

7. AISTAS

   Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

   Sebastien M. R. Arnold, Pierre L’Ecuyer, Liyu Chen, and 2 more authors

   In AISTATS, 2022

   Bib

   @inproceedings{arnold22policy,
     title = {Policy Learning and Evaluation with Randomized Quasi-Monte Carlo},
     author = {Arnold, Sebastien M. R. and L'Ecuyer, Pierre and Chen, Liyu and Chen, Yi-fan and Sha, Fei},
     booktitle = {AISTATS},
     year = {2022},
   }

2021

1. EMNLP

   Systematic Generalization on gSCAN: what is nearly solved and what is next?

   Linlu Qiu, Hexiang Hu, Bowen Zhang, and 2 more authors

   In Proc. of EMNLP, 2021

   Bib

   @inproceedings{qiu21gscan,
     author = {Qiu, Linlu and Hu, Hexiang and Zhang, Bowen and Shaw, Pete and Sha, Fei},
     title = {Systematic Generalization on {gSCAN}: what is nearly solved and what is next?},
     booktitle = {Proc. of EMNLP},
     year = {2021},
   }

2. EMNLP

   Visually Grounded Concept Composition

   Bowen Zhang, Hexiang Hu, Linlu Qiu, and 2 more authors

   In Findings of EMNLP, 2021

   Bib

   @inproceedings{zhang21crg,
     author = {Zhang, Bowen and Hu, Hexiang and Qiu, Linlu and and Pete Shaw and Sha, Fei},
     title = {Visually Grounded Concept Composition},
     booktitle = {Findings of EMNLP},
     year = {2021},
   }

3. ICML

   Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

   Shariq Iqbal, Christian A. Schroeder Witt, Bei Peng, and 3 more authors

   In Proc. of Intl. Conf. on Machine Learning (ICML), 2021

   Bib

   @inproceedings{iqbal2020aiqmix,
     title = {Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning},
     author = {Iqbal, Shariq and de Witt, Christian A. Schroeder and Peng, Bei and B\"{o}hmer, Wendelin and Whiteson, Shimon and Sha, Fei},
     year = {2021},
     booktitle = {Proc. of Intl. Conf. on Machine Learning (ICML)},
   }

4. AISTATS

   When MAML Can Adapt Fast and How to Assist When It Cannot

   Sebastien M. R. Arnold, Shariq Iqbal, and Fei Sha

   In AISTATS, 2021

   Bib

   @inproceedings{arnold2021maml,
     title = {When MAML Can Adapt Fast and How to Assist When It Cannot},
     author = {Arnold, Sebastien M. R. and Iqbal, Shariq and Sha, Fei},
     booktitle = {AISTATS},
     year = {2021},
   }

5. ICDAR

   CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-Document Summarization

   Sayali Kulkarn, Sheide Chammas, Wan Zhu, and 2 more authors

   In Proceedings of Intl. Conf. on Document Analysis and Recognition, 2021

   Bib

   @inproceedings{kulkarni21comsum,
     author = {Kulkarn, Sayali and Chammas, Sheide and Zhu, Wan and Sha, Fei and Ie, Eugene},
     title = { {CoMSum and SIBERT}: A Dataset and Neural Model for Query-Based Multi-Document Summarization},
     booktitle = {Proceedings of Intl. Conf. on Document Analysis and Recognition},
     year = {2021},
   }

6. EACL

   DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections

   Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, and 5 more authors

   In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Apr 2021

   Bib

   @inproceedings{zemlyanskiy21docent,
     title = {{DOCENT}: Learning Self-Supervised Entity Representations from Large Document Collections},
     author = {Zemlyanskiy, Yury and Gandhe, Sudeep and He, Ruining and Kanagal, Bhargav and Ravula, Anirudh and Gottweis, Juraj and Sha, Fei and Eckstein, Ilya},
     booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
     month = apr,
     year = {2021},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     pages = {2540--2549},
   }

7. NAACL

   ReadTwice: Reading Very Large Documents with Memories

   Yury Zemlyanskiy, Joshua Ainslie, Michiel Jong, and 3 more authors

   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2021

   Bib

   @inproceedings{zemlyanskiy21readtwice,
     title = {{R}ead{T}wice: Reading Very Large Documents with Memories},
     author = {Zemlyanskiy, Yury and Ainslie, Joshua and de Jong, Michiel and Pham, Philip and Eckstein, Ilya and Sha, Fei},
     booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = jun,
     year = {2021},
     address = {Online},
     pages = {5189--5195},
   }

2020

1. Sci. R

   Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images

   Rishi R Rawat, Itzel Ortega, Preeyam Roy, and 4 more authors

   Scientific reports, Jun 2020

   Abs Bib

   Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of “tissue fingerprints,” which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call “fingerprints,” to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

   @article{rawat20fingerprints,
     title = {{Deep learned tissue "fingerprints" classify breast cancers by
                      ER/PR/Her2 status from H\&E images}},
     author = {Rawat, Rishi R and Ortega, Itzel and Roy, Preeyam and Sha, Fei and Shibata, Darryl and Ruderman, Daniel and Agus, David B},
     affiliation = {Lawrence J. Ellison Institute for Transformative Medicine,
                      University of Southern California, 12414 Exposition Blvd, Los
                      Angeles, CA, 90064, USA. DASH Center at USC, 1002 Childs Way,
                      MCB 114, Los Angeles, CA, 90089-0005, USA. Department of
                      Pathology, University of Southern California Health Sciences
                      Campus, NOR 1441 Eastlake Ave, Los Angeles, 90033, USA.
                      Lawrence J. Ellison Institute for Transformative Medicine,
                      University of Southern California, 12414 Exposition Blvd, Los
                      Angeles, CA, 90064, USA. ruderman@usc.edu.},
     journal = {Scientific reports},
     volume = {10},
     number = {1},
     pages = {7275},
     year = {2020},
     language = {en},
     issn = {2045-2322},
     pmid = {32350370},
   }

2. IJCV

   Classifier and Exemplar Synthesis for Zero-Shot Learning

   Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and 1 more author

   Int. J. Comput. Vis., Jun 2020

   Bib

   @article{changpinyo20classifier,
     author = {Changpinyo, Soravit and Chao, Wei{-}Lun and Gong, Boqing and Sha, Fei},
     title = {Classifier and Exemplar Synthesis for Zero-Shot Learning},
     journal = {Int. J. Comput. Vis.},
     volume = {128},
     number = {1},
     pages = {166--201},
     year = {2020},
   }

3. EMNLP

   Learning to Represent Image and Text with Denotation Graph

   Bowen Zhang, Hexiang Hu, Vihan Jain, and 2 more authors

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2020

   Bib

   @inproceedings{zhang20vdg,
     title = {Learning to Represent Image and Text with Denotation Graph},
     author = {Zhang, Bowen and Hu, Hexiang and Jain, Vihan and Ie, Eugene and Sha, Fei},
     booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     pages = {823--839},
   }

4. ACL

   BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps

   Wang Zhu, Hexiang Hu, Jiacheng Chen, and 4 more authors

   In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, Nov 2020

   Bib

   @inproceedings{zhu20babywalk,
     author = {Zhu, Wang and Hu, Hexiang and Chen, Jiacheng and Deng, Zhiwei and Jain, Vihan and Ie, Eugene and Sha, Fei},
     editor = {Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel R.},
     title = {BabyWalk: Going Farther in Vision-and-Language Navigation by Taking
                    Baby Steps},
     booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational
                    Linguistics, {ACL} 2020, Online, July 5-10, 2020},
     pages = {2539--2556},
     publisher = {Association for Computational Linguistics},
     year = {2020},
   }

5. CVPR

   Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions

   Han-Jia Ye, Hexiang Hu, De-Chuan Zhang, and 1 more author

   In The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2020

   Bib

   @inproceedings{ye20fsl,
     author = {Ye, Han-Jia and Hu, Hexiang and Zhang, De-Chuan and Sha, Fei},
     title = {Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions},
     booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     month = jun,
     year = {2020},
   }

6. AISTATS

   Amortized Inference of Variational Bounds for Learning Noisy-OR

   Yiming Yan, Melissa Ailem, and Fei Sha

   In Proc. of AISTATS, Jun 2020

   Bib

   @inproceedings{yan20noisy,
     title = {Amortized Inference of Variational Bounds for Learning Noisy-OR},
     author = {Yan, Yiming and Ailem, Melissa and Sha, Fei},
     booktitle = {Proc. of AISTATS},
     year = {2020},
   }

2019

1. ICML

   Actor-Attention-Critic for Multi-Agent Reinforcement Learning

   Shariq Iqbal, and Fei Sha

   In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Jun 2019

   Bib

   @inproceedings{iqbal19acc,
     author = {Iqbal, Shariq and Sha, Fei},
     editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
     title = {Actor-Attention-Critic for Multi-Agent Reinforcement Learning},
     booktitle = {Proceedings of the 36th International Conference on Machine Learning,
                    {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
     series = {Proceedings of Machine Learning Research},
     volume = {97},
     pages = {2961--2970},
     publisher = {{PMLR}},
     year = {2019},
   }

2016

1. CVPR

   Synthesized classifiers for zero-shot learning

   Soravit Changpinyo, Weilun Chao, Boqing Gong, and 1 more author

   In Proc. of CVPR, Jun 2016

   Bib

   @inproceedings{changpinyo16zero,
     author = {Changpinyo, Soravit and Chao, Weilun and Gong, Boqing and Sha, Fei},
     title = {Synthesized classifiers for zero-shot learning},
     booktitle = {Proc. of CVPR},
     year = {2016},
   }

2013

1. NeurIPS

   Similarity Component Analysis

   Soravit Changpinyo, Kuan Liu, and Fei Sha

   In Proc. of Annual Conference on Neural Information Processing Systems (NIPS), Jun 2013

   Bib

   @inproceedings{chanpingyo13sca,
     author = {Changpinyo, Soravit and Liu, Kuan and Sha, Fei},
     title = {Similarity Component Analysis},
     booktitle = {Proc. of Annual Conference on Neural Information Processing Systems (NIPS)},
     year = {2013},
   }

2012

1. CVPR

   Geodesic Flow Kernel for Unsupervised Domain Adaptation

   Boqing Gong, Yuan Shi, Fei Sha, and 1 more author

   In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Jun 2012

   Bib

   @inproceedings{gong12gfk,
     author = {Gong, Boqing and Shi, Yuan and Sha, Fei and Grauman, Kristen},
     title = {Geodesic Flow Kernel for Unsupervised Domain Adaptation},
     booktitle = {Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
     address = {Providence, Rhode Island},
     year = {2012}
   }

2010

1. NeurIPS

   Unsupervised Kernel Dimension Reduction

   Meihong Wang, Fei Sha, and Michael I. Jordan

   In Proceedings of Neural Information Processing (NIPS), Jun 2010

   Bib

   @inproceedings{wang10ukdr,
     author = {Wang, Meihong and Sha, Fei and Jordan, Michael I.},
     title = {Unsupervised Kernel Dimension Reduction},
     booktitle = {Proceedings of Neural Information Processing (NIPS)},
     year = {2010},
   }

2007

1. NeurIPS

   Large margin hidden Markov models for automatic speech recognition

   Fei Sha, and Lawrence K. Saul

   In Advances in Neural Information Processing Systems 19, Jun 2007

   Bib

   @inproceedings{sha07hmm,
     author = {Sha, Fei and Saul, Lawrence K.},
     title = {Large margin hidden {M}arkov models for automatic speech recognition},
     booktitle = {Advances in Neural Information Processing Systems 19},
     year = {2007},
     editor = {Sch\"{o}lkopf, B. and Platt, J.C. and Hofmann, T.},
     pages = {1249--1256},
     address = {Cambridge, MA},
     publisher = {MIT Press},
     owner = {feisha},
   }

2003

1. NAACL-HLT

   Shallow Parsing with Conditional Random Fields

   Fei Sha, and Fernando Pereira

   In Proceedings of Human Language Technology-NAACL 2003, Jun 2003

   Bib

   @inproceedings{sha03shallow,
     author = {Sha, Fei and Pereira, Fernando},
     title = {Shallow Parsing with Conditional Random Fields},
     booktitle = {Proceedings of Human Language Technology-NAACL 2003},
     year = {2003},
     pages = {213--220},
     address = {Edmonton, Canada}
   }