- Charles Lovering*, Jessica Forde*, George Konidaris, Ellie Pavlick, Michael Littman. Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. Neurips, 2022. (*Equal contribution.)
- Charles Lovering, Ellie Pavlick. Unit Testing for Concepts in Neural Networks. TACL, 2022.
- Charles Lovering, Rohan Jha, Tal Linzen, Ellie Pavlick. Predicting Inductive Biases of Pre-Trained Models. ICLR, 2021.
- Rohan Jha, Charles Lovering, Ellie Pavlick. Does Data Augmentation Improve Generalization in NLP? 2020. PREPRINT.
Research Experience
Work Experience:
- Presented at Jane Street Research Symposium
- Spoke at NLP & Fairness, Interpretability, and Robustness; Google
- Gave talks at Language Understanding and Representations; Brown University
- Worked on projects involving concept analysis in AlphaZero for Hex, unit testing for concepts in neural networks, predicting inductive biases of pre-trained models, etc.
Education
Degree: PhD in Computer Science
School: Not explicitly mentioned
Advisor: Not explicitly mentioned
Time: Not provided
Field: Natural Language Understanding
Background
PhD Student in Computer Science (NLU).
Miscellany
Interests and Hobbies:
- Creating Lindenmayer systems
- Developing interactive visualizations
- Writing introductions to byte-encoding representations, beam search, transformer architecture, and neural turing machines.
Other:
- This site replicates the distill design.
- Uses Adobe XD CC for diagrams, D3 for visualizations, and PyTorch for deep learning.