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ORCIDShi Feng
This is just a disambiguation page, and is not intended to be the bibliography of an actual person. The links to all actual bibliographies of persons of the same or a similar name can be found below. Any publication listed on this page has not been assigned to an actual author yet. If you know the true author of one of the publications listed below, you are welcome to contact us.
Person information
Other persons with the same name
- Shi Feng 0001
![[0000-0002-2846-7652]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-2846-7652")
— Northeastern University, School of Computer Science and Engineering, Shenyang, China - Shi Feng 0002 — Harvard University, John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA (and 1 more)
- Shi Feng 0003 — Beijing Institute of Technology, Department of Computer Science and Engineering, Beijing, China
- Shi Feng 0004 — Purdue University, West Lafayette, IN, USA (and 1 more)
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2020 – today
- 2024
[c34]Chenglei Si, Navita Goyal, Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan L. Boyd-Graber:
Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong. NAACL-HLT 2024: 1459-1474- [c33]Shi Feng, Jiawei Zhang, Jun Dai, Yashe Liu, Xiaorun Wang, Yuefeng Ji:
Demonstration of Hitless OCS Provision for Multi-modal Traffic in a Centralized Scheduling Hybrid Optical/Electrical Datacenter Network. OFC 2024: 1-3 - [i29]Matthew Shu, Nishant Balepur, Shi Feng, Jordan L. Boyd-Graber:
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students. CoRR abs/2402.12291 (2024) - [i28]Shi Feng, Nuoya Xiong, Zhijie Zhang, Wei Chen:
A Correction of Pseudo Log-Likelihood Method. CoRR abs/2403.18127 (2024) - [i27]Arjun Panickssery, Samuel R. Bowman, Shi Feng:
LLM Evaluators Recognize and Favor Their Own Generations. CoRR abs/2404.13076 (2024) - [i26]Wencheng Bao, Shi Feng, Kaiwen Zhang:
Application of Kalman Filter in Stochastic Differential Equations. CoRR abs/2404.13748 (2024) - [i25]Siwei Wang, Yifei Shen, Shi Feng, Haoran Sun, Shang-Hua Teng, Wei Chen:
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models. CoRR abs/2405.09220 (2024) - [i24]Nishant Balepur, Matthew Shu, Alexander Miserlis Hoyle, Alison Robey, Shi Feng, Seraphina Goldfarb-Tarrant, Jordan L. Boyd-Graber:
A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick. CoRR abs/2406.15352 (2024) - [i23]Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, Ziyu Yao:
A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models. CoRR abs/2407.02646 (2024) - [i22]Jane Pan, He He, Samuel R. Bowman, Shi Feng:
Spontaneous Reward Hacking in Iterative Self-Refinement. CoRR abs/2407.04549 (2024) - [i21]Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng:
Language Models Learn to Mislead Humans via RLHF. CoRR abs/2409.12822 (2024) - 2023
- [j9]Shi Feng, John P. T. Mo:
Prognostic fault prevention by segmented digital transformation of manufacturing process signals. Adv. Eng. Informatics 57: 102125 (2023) - [j8]Yimeng Wang, Shi Feng, Bing Wang, Jihong Ouyang:
Deep transition network with gating mechanism for multivariate time series forecasting. Appl. Intell. 53(20): 24346-24359 (2023) - [j7]Jihong Ouyang, Shi Feng, Bing Wang, Zhiyao Yang:
Pseudo dense counterfactual augmentation for aspect-based sentiment analysis. Neurocomputing 561: 126869 (2023) - [j6]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. Trans. Mach. Learn. Res. 2023 (2023) - [c32]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. ACL (1) 2023: 11289-11310 - [c31]Jing Li, Donghong Han, Shi Feng, Yifei Zhang:
Optimizing Empathetic Response by Generating and Integrating Emotion Feedback and Topic Discussion. DASFAA (3) 2023: 623-638 - [c30]Renxi Wang, Shi Feng:
Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation. EACL (Findings) 2023: 2075-2082 - [c29]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. FAccT 2023: 1 - [c28]Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan:
Learning Human-Compatible Representations for Case-Based Decision Support. ICLR 2023 - [i20]Shi Feng, Nuoya Xiong, Wei Chen:
Combinatorial Causal Bandits without Graph Skeleton. CoRR abs/2301.13392 (2023) - [i19]Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan:
Learning Human-Compatible Representations for Case-Based Decision Support. CoRR abs/2303.04809 (2023) - [i18]Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He:
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations. CoRR abs/2305.13299 (2023) - [i17]Kevin Zhang, Shi Feng, Yuri D. Lensky, Nandini Trivedi, Eun-Ah Kim:
Machine learning feature discovery of spinon Fermi surface. CoRR abs/2306.03143 (2023) - [i16]Wencheng Bao, Shi Feng:
Machine learning assist nyc subway navigation safer and faster. CoRR abs/2310.02447 (2023) - [i15]Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan L. Boyd-Graber:
Large Language Models Help Humans Verify Truthfulness - Except When They Are Convincingly Wrong. CoRR abs/2310.12558 (2023) - 2022
- [j5]Shi Feng, Bing Wang, Zhiyao Yang, Jihong Ouyang:
Aspect-based sentiment analysis with attention-assisted graph and variational sentence representation. Knowl. Based Syst. 258: 109975 (2022) - [c27]Shi Feng, Jordan L. Boyd-Graber:
Learning to Explain Selectively: A Case Study on Question Answering. EMNLP 2022: 8372-8382 - [c26]Yiming Zhang, Shi Feng, Chenhao Tan:
Active Example Selection for In-Context Learning. EMNLP 2022: 9134-9148 - [c25]Zhihao Shen, Nanaka Urano, Chih-Pu Chen, Shi Feng, Scean Mitchell, Masao Katagiri, Yegang Du, Franco Pariasca Trevejo, Tito Pradhono Tomo, Alexander Schmitz, Ryan Browne, Toshimi Ogawa, Yasuyuki Taki, Gabriele Trovato:
Participatory Design and Early Deployment of DarumaTO-3 Social Robot. ICSR (2) 2022: 550-559 - [i14]Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan:
Machine Explanations and Human Understanding. CoRR abs/2202.04092 (2022) - [i13]Yiming Zhang, Shi Feng, Chenhao Tan:
Active Example Selection for In-Context Learning. CoRR abs/2211.04486 (2022) - 2021
- [b1]Shi Feng:
Towards Human-AI Cooperation on Sequential Decision Making Problems. University of Maryland, College Park, MD, USA, 2021 - [j4]Shi Feng, Qinmin Yang, Alice C. Hughes, Jiming Chen, Huijie Qiao:
A novel method for multi-trajectory reconstruction based on LoMcT for avian migration in population level. Ecol. Informatics 63: 101319 (2021) - [c24]Zhuojin Liu, Zhongxin Jin, Chaodi Wei, Xiangju Li, Shi Feng:
CoEmoCause: A Chinese Fine-Grained Emotional Cause Extraction Dataset. WISA 2021: 519-530 - [c23]Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh:
Calibrate Before Use: Improving Few-shot Performance of Language Models. ICML 2021: 12697-12706 - [c22]Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh:
Concealed Data Poisoning Attacks on NLP Models. NAACL-HLT 2021: 139-150 - [i12]Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh:
Calibrate Before Use: Improving Few-Shot Performance of Language Models. CoRR abs/2102.09690 (2021) - 2020
- [i11]Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh:
Customizing Triggers with Concealed Data Poisoning. CoRR abs/2010.12563 (2020)
2010 – 2019
- 2019
- [j3]Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan L. Boyd-Graber:
Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Trans. Assoc. Comput. Linguistics 7: 387-401 (2019) - [c21]Shi Feng, Eric Wallace, Jordan L. Boyd-Graber:
Misleading Failures of Partial-input Baselines. ACL (1) 2019: 5533-5538 - [c20]Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh:
Universal Adversarial Triggers for Attacking and Analyzing NLP. EMNLP/IJCNLP (1) 2019: 2153-2162 - [c19]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. ICML 2019: 5848-5856 - [c18]Shi Feng, Jordan L. Boyd-Graber:
What can AI do for me?: evaluating machine learning interpretations in cooperative play. IUI 2019: 229-239 - [i10]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. CoRR abs/1902.00407 (2019) - [i9]Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan L. Boyd-Graber:
Quizbowl: The Case for Incremental Question Answering. CoRR abs/1904.04792 (2019) - [i8]Shi Feng, Eric Wallace, Jordan L. Boyd-Graber:
Misleading Failures of Partial-input Baselines. CoRR abs/1905.05778 (2019) - [i7]Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh:
Universal Adversarial Triggers for NLP. CoRR abs/1908.07125 (2019) - 2018
- [c17]Ying Fang, Keith T. Shubeck, Anne Lippert, Qinyu Cheng, Genghu Shi, Shi Feng, Jessica Gatewood, Su Chen, Zhiqiang Cai, Philip I. Pavlik, Jan C. Frijters, Daphne Greenberg, Arthur C. Graesser:
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System. EDM 2018 - [c16]Eric Wallace, Shi Feng, Jordan L. Boyd-Graber:
Interpreting Neural Networks with Nearest Neighbors. BlackboxNLP@EMNLP 2018: 136-144 - [c15]Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan L. Boyd-Graber:
Pathologies of Neural Models Make Interpretation Difficult. EMNLP 2018: 3719-3728 - [i6]Shi Feng, Eric Wallace, Mohit Iyyer, Pedro Rodriguez, Alvin Grissom II, Jordan L. Boyd-Graber:
Right Answer for the Wrong Reason: Discovery and Mitigation. CoRR abs/1804.07781 (2018) - [i5]Eric Wallace, Pedro Rodriguez, Shi Feng, Jordan L. Boyd-Graber:
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. CoRR abs/1809.02701 (2018) - [i4]Eric Wallace, Shi Feng, Jordan L. Boyd-Graber:
Interpreting Neural Networks With Nearest Neighbors. CoRR abs/1809.02847 (2018) - [i3]Shi Feng, Jordan L. Boyd-Graber:
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play. CoRR abs/1810.09648 (2018) - 2017
- [c14]Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III:
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. WMT 2017: 667-673 - [i2]Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III:
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. CoRR abs/1708.01318 (2017) - 2016
- [c13]Chen Shi, Shujie Liu, Shuo Ren, Shi Feng, Mu Li, Ming Zhou, Xu Sun, Houfeng Wang:
Knowledge-Based Semantic Embedding for Machine Translation. ACL (1) 2016 - [c12]Shi Feng, Shujie Liu, Nan Yang, Mu Li, Ming Zhou, Kenny Q. Zhu:
Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation. COLING 2016: 3082-3092 - [i1]Shi Feng, Shujie Liu, Mu Li, Ming Zhou:
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model. CoRR abs/1601.03317 (2016) - 2015
- [j2]Zubiao Xiong, Shi Feng, Richard Kautz, Sandeep Chandra, Nevin Altunyurt, Ji Chen:
Multi-GPU Accelerated Admittance Method for High-Resolution Human Exposure Evaluation. IEEE Trans. Biomed. Eng. 62(12): 2920-2930 (2015) - [c11]Shi Feng, Janay Stewart, Danielle N. Clewley, Arthur C. Graesser:
Emotional, Epistemic, and Neutral Feedback in AutoTutor Trialogues to Improve Reading Comprehension. AIED 2015: 570-573 - [c10]Shi Feng, Gavin M. Bidelman:
Music familiarity modulates mind wandering during lexical processing. CogSci 2015 - [c9]Patricia Goedecke, Daqi Dong, Genghu Shi, Shi Feng, Evan F. Risko, Andrew Olney, Sidney K. D'Mello, Arthur C. Graesser:
Breaking Off Engagement: Readers' Cognitive Decoupling as a Function of Reader and Text Characteristics. EDM 2015: 448-451 - 2014
- [c8]Lisa Mintz, Dan Stefanescu, Shi Feng, Sidney K. D'Mello, Arthur C. Graesser:
Automatic assessment of student reading comprehension from short summaries. EDM 2014: 333-334 - 2013
- [c7]Benjamin Vega, Shi Feng, Blair Lehman, Arthur C. Graesser, Sidney K. D'Mello:
Reading into the Text: Investigating the Influence of Text Complexity on Cognitive Engagement. EDM 2013: 296-299 - [c6]Shi Feng, Hong Zhang, Wenguang Chen:
Shall I Use Heterogeneous Data Centers? - A Case Study on Video on Demand Systems. HPCC/EUC 2013: 557-564 - [c5]Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, Shi Feng:
Localized matrix factorization for recommendation based on matrix block diagonal forms. WWW 2013: 1511-1520 - 2011
- [c4]Shi Feng, Zhiqiang Cai, Scott A. Crossley, Danielle S. McNamara:
Simulating Human Ratings on Word Concreteness. FLAIRS 2011 - [c3]Vasile Rus, Shi Feng, Russell D. Brandon, Scott A. Crossley, Danielle S. McNamara:
A Linguistic Analysis of Student-Generated Paraphrases. FLAIRS 2011 - 2010
- [j1]Shi Feng, Huang Ning:
Computational simulations of blown sand fluxes over the surfaces of complex microtopography. Environ. Model. Softw. 25(3): 362-367 (2010)
2000 – 2009
- 2004
- [c2]Wang Long-Hui, Liu Juan, Zhou Huai-Bei, Shi Feng:
A new distance metric and its application in phylogenetic tree construction. CIBCB 2004: 108-115 - [c1]Hong Jing, Lu Jingui, Shi Feng:
Fuzzy and Rough Set. Intelligent Information Processing 2004: 143-146
Coauthor Index
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