2.3k citations · 2.6k across the 14 of their papers we have counts for
14 papers
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie +1
Despite the recent advancements in offline reinforcement learning via supervised learning (RvS) and the success of the decision transformer (DT) architecture in various domains, DT…
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Sherry Ruan, Allen Nie, William Steenbergen +9
Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key…
Model-based Offline Reinforcement Learning with Local Misspecification
Kefan Dong, Yannis Flet-Berliac, Allen Nie +1
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we pro…
Giving Feedback on Interactive Student Programs with Meta-Exploration
Evan Zheran Liu, Moritz Stephan, Allen Nie +3
Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. However, teaching and giving feedback on such software is time…
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data
Allen Nie, Yannis Flet-Berliac, Deon R. Jordan +2
Offline reinforcement learning (RL) can be used to improve future performance by leveraging historical data. There exist many different algorithms for offline RL, and it is well re…
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
Allen Nie, Emma Brunskill, Chris Piech
Contemporary coding education often presents students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games. While…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Pragmatic Issue-Sensitive Image Captioning
Allen Nie, Reuben Cohn-Gordon, Christopher Potts
Image captioning systems have recently improved dramatically, but they still tend to produce captions that are insensitive to the communicative goals that captions should meet. To…
LitGen: Genetic Literature Recommendation Guided by Human Explanations
Allen Nie, Arturo L. Pineda, Matt W. Wright Hannah Wand +5
As genetic sequencing costs decrease, the lack of clinical interpretation of variants has become the bottleneck in using genetics data. A major rate limiting step in clinical inter…
Learning to Explain: Answering Why-Questions via Rephrasing
Allen Nie, Erin D. Bennett, Noah D. Goodman
Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction. Explanations are challenging in that they require ma…
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
Yuhui Zhang, Allen Nie, James Zou
Supervised learning is limited both by the quantity and quality of the labeled data. In the field of medical record tagging, writing styles between hospitals vary drastically. The…
DeepTag: inferring all-cause diagnoses from clinical notes in under-resourced medical domain
Allen Nie, Ashley Zehnder, Rodney L. Page +4
Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records wit…
DisSent: Sentence Representation Learning from Explicit Discourse Relations
Allen Nie, Erin D. Bennett, Noah D. Goodman
Learning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require cos…
Data Noising as Smoothing in Neural Network Language Models
Ziang Xie, Sida I. Wang, Jiwei Li +4
Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noisi…