As machine learning is deployed more widely, researchers and practitioners keep running into a fundamental problem: how do we get enough labeled data? This seminar course will survey research on learning when only limited labeled data is available. Topics covered include semi-supervised learning, transfer learning, weak supervision, few-shot learning, and zero-shot learning. Students will lead discussions on recent research papers and develop final research projects.
For questions, discussion, and other course-related posts, use Canvas.
If you have an atypical question that you are certain does not belong on Canvas, email the instructor.
Suplemental reading:
- Introduction to Semi-Supervised Learning. Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. In Semi-Supervised Learning, MIT Press, 2006. [PDF] [Online, requires Brown login]
- Incidental Supervision: Moving beyond Supervised Learning. Dan Roth. AAAI 2017. [PDF]
- How to Read a CS Research Paper? Philip W. L. Wong. [PDF]
- How to Read a Technical Paper. Jason Eisner. [Online]
- How to Read a Paper. S. Keshav. [PDF]
[PDF] [Supplemental (Zip)] [Reviews] [Code]
Suplemental reading:
- FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A. Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Neural Information Processing Systems (NeurIPS) 2020. [PDF] [Supplemental] [Reviews] [Code]
[PDF] [Reviews]
Suplemental reading:
- Unsupervised Data Augmentation for Consistency Training. Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Neural Information Processing Systems (NeurIPS) 2020. [PDF] [Supplemental] [Reviews] [Code]
[PDF] [Blog] [Code]
Suplemental reading:
- XLNet: Generalized Autoregressive Pretraining for Language Understanding. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, and Quoc V. Le. Neural Information Processing Systems (NeurIPS) 2019. [PDF] [Supplemental (Zip)] [Reviews]
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. International Conference on Learning Representations (ICLR) 2020.
[PDF] [Reviews] [Code]
Suplemental reading:
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Meeting of the North American Association for Computational Linguistics (NAACL) 2019. [PDF]
[PDF] [Blog]
Supplemental reading:
- SLIP: Self-supervision meets Language-Image Pre-training. Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. ArXiv 2112.12750 2021. [PDF]
[PDF] [Code]
Supplemental reading:
- Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm. A. P. Dawid and A. M. Skene. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979. [Online, requires Brown login]
[PDF] [Code] [Video]
Supplemental reading:
- WRENCH: A Comprehensive Benchmark for Weak Supervision. Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. NeurIPS Datasets and Benchmarks Track 2022. [PDF] [Supplemental] [Code] [Reviews]
[PDF] [Supplemental (Zip)] [Reviews]
Supplemental reading:
- Language Models in the Loop: Incorporating Prompting into Weak Supervision. Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach. ArXiv 2205.02318 2022. [PDF]
[PDF] [Supplemental (Zip)] [Reviews]
Supplemental reading:
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Chelsea Finn, Pieter Abbeel, and Sergey Levine. International Conference on Machine Learning (ICML) 2017. [PDF]
Language Models are Few-Shot Learners. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Neural Information Processing Systems (NeurIPS) 2020.
[PDF]
Supplemental reading:
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2021. [PDF]
[PDF] [Supplemental] [Video]
Supplemental reading:
[PDF] [Supplemental] [Code] [Video]
Supplemental reading:
[PDF] [Code]
Supplemental reading:
- DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations. Ximeng Sun, Ping Hu, and Kate Saenko. ArXiv 2206.09541 2022. [PDF]
[PDF]
Supplemental reading:
- None
(No class)
[PDF] [Supplemental (Zip)] [Reviews]
Supplemental reading:
[PDF]
Supplemental reading:
- Rethinking Knowledge Graph Propagation for Zero-Shot Learning. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, and Eric P. Xing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019. [PDF]
Multitask Prompted Training Enables Zero-Shot Task Generalization. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. International Conference on Learning Representations (ICLR) 2022.
[PDF] [Code] [Data] [Reviews]
Supplemental reading:
- Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks. Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, and Daniel Khashabi. ArXiv 2204.07705 2022. [PDF] [Data]
[PDF] [Code]
Supplemental reading:
- Can language models learn from explanations in context? Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, and Felix Hill. ArXiv 2204.02329 2022 [PDF]
(No class)
[PDF] [Supplemental] [Code]
Supplemental reading:
[PDF] [Talk] [Code] [Video]
Supplemental reading:
- Generating Object Stamps. Youssef Alami Mejjati, Zejiang Shen, Michael Snower, Aaron Gokaslan, Oliver Wang, James Tompkin, and Kwang In Kim. ArXiv 2001.02595 2020. [PDF]
[PDF] [Blog]
Supplemental reading:
- Diffusion Models Beat GANs on Image Synthesis. Prafulla Dhariwal and Alexander Nichol. Neural Information Processing Systems (NeurIPS) 2021. [PDF] [Supplemental] [Reviews]
(No class)
Students who complete this course will:
- Acquire a working knowledge of the landscape of research on machine learning with limited labeled data.
- Practice identifying and critically assessing the claims, contributions, and supporting evidence in machine learning research papers.
- Develop their ability to share scientific ideas via writing and discussion.
- Gain practical experience with the course's subject matter by applying and extending it to their own research interests though an open-ended project.
The following standards will be used to assign grades. Anyone who doesn't complete the standards to earn a B will receive NC.
- Participate actively in class discussions by asking questions, sharing opinions, and listening carefully to others.
- Meet all deadlines in the course schedule related to the research project.
- Submit two discussion questions to Canvas by 6 PM the evening before class for assigned readings, missing no more than 3 readings.
- Attend class meetings, missing no more than 3 meetings.
- Fulfill the requirements below to earn a B.
- Conduct an original research project related to course materials and submit a written report meeting the assignment guidelines.
- Lead the assigned class discussion demonstrating preparation and inclusion.
- Submit two discussion questions to Canvas by 6 PM the evening before class for assigned readings, missing no more than 6 readings.
- Attend class meetings, missing no more than 6 meetings.
Activity | Hours |
Class Meetings | 28 |
Readings | 65 |
Submitting Discussion Questions | 10 |
Preparing to Lead Discussion | 2 |
Project Research | 60+ |
Project Proposal / Status | 10 |
Project Final Report | 5 |
Total | 180+ |
Everyone attending class is required to wear a high-quality mask (KN95 or better). Students who are leading discussions may optionally remove their masks while presenting. Attendance and discussion question policies will be flexible with respect to COVID-19 and other health issues. Please contact the instructor if you have any issues.
The Brown computer science department has made it its mission to create and sustain a diverse and inclusive environment in which all students, faculty, and staff can thrive. In this course, that responsibility falls on us all, students and teaching staff alike. In particular, Brown's Nondiscrimination and Anti-Harassment Policy applies to all participants.
If you have not been treated in an inclusive manner by any of the course members, please contact either me (Stephen) or the department chair (Prof. Tamassia). Laura Dobler is also available as a resource for members of underrepresented groups. Additional resources are listed on the department's website. We, the computer science department, take all complaints about discrimination, harassment, and other unprofessional behavior seriously.
In addition, Brown welcomes students from all around the country and the world, and their unique perspectives enrich our learning community. To empower students whose first language is not English, an array of support is available on campus, including language and culture workshops and individual appointments. For more information, contact the English Language Learning Specialists at ellwriting@brown.edu.
Academic dishonesty will not be tolerated. This includes cheating, lying about course matters, plagiarism, or helping others commit a violation. Plagiarism includes reproducing the words of others without both the use of quotation marks and citation. Students are reminded of the obligations and expectations associated with the Brown Academic Code and Brown Code of Student Conduct. For project work, feel free to build on third-party software, datasets, or other resources, as long as you credit them in your report(s) and clearly state what work is solely your own. As a general policy (for this course and for the rest of your academic career): if you use any idea, text, code, or data that you did not create, then cite it.
Brown University is committed to full inclusion of all students. Please inform me if you have a disability or other condition that might require accommodations or modification of any of these course procedures. You may email me, come to office hours, or speak with me after class, and your confidentiality is respected. I will do whatever I can to support accommodations recommended by SAS. For more information contact Student Accessibility Services (SAS) at 401-863-9588 or SAS@brown.edu.
Being a student can be very stressful. If you feel you are under too much pressure or there are psychological issues that are keeping you from performing well at Brown, I encourage you to contact Brown’s Counseling and Psychological Services CAPS. They provide confidential counseling and can provide notes supporting accommodations for health reasons.
I expect everyone to complete the course on time. However, I understand that there may be factors beyond your control, such as health problems and family crises, that prevent you from finishing the course on time. If you feel you cannot complete the course on time, please discuss with me (Stephen) the possibility of being given a grade of Incomplete for the course and setting a schedule for completing the course in the upcoming year.
Thanks to Tom Doeppner, Laura Dobler, and Daniel Ritchie for borrowed text.