Search Results for author:
Found 26 papers, 15 papers with code
On the Opportunities and Risks of Foundation Models
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.
BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits
Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets.
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations.
Deep Knowledge Tracing
Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education.
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Knowledge Tracing
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Variational Item Response Theory: Fast, Accurate, and Expressive
Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology.
Machine Translation for Nko: Tools, Corpora and Baseline Results
Currently, there is no usable machine translation system for Nko, a language spoken by tens of millions of people across multiple West African countries, which holds significant cultural and educational value.
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor.
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale.
Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users
Next, we share West African wav2vec, a speech encoder trained on the noisy radio corpus, and compare it with the baseline Facebook speech encoder trained on six times more data of higher quality.
The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues
How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue?
Giving Feedback on Interactive Student Programs with Meta-Exploration
However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
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.
MABSplit: Faster Forest Training Using Multi-Armed Bandits
We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods.
Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates.
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems
In this paper, we present generative grading: a novel computational approach for providing feedback at scale that is capable of accurately grading student work and providing nuanced, interpretable feedback.
Learning Program Embeddings to Propagate Feedback on Student Code
Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students.
Tuned Models of Peer Assessment in MOOCs
In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students.
Using Latent Variable Models to Observe Academic Pathways
Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers.
The Stanford Acuity Test: A Precise Vision Test Using Bayesian Techniques and a Discovery in Human Visual Response
First, we uncover a new parametric probabilistic model of visual acuity response based on detailed measurements of patients with eye disease.
Human Languages in Source Code: Auto-Translation for Localized Instruction
The study is to the best of our knowledge the first on human-language in code and covers 2. 9 million Java repositories.
Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process
Contemporary coding education often present students with the task of developing programs that have user interaction and complex dynamic systems, such as mouse based games.
Modeling Item Response Theory with Stochastic Variational Inference
Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions.
Faster Maximum Inner Product Search in High Dimensions
We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$.
Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars
Once the posterior has been learned, trees can be sampled efficiently (linearly in the number of nodes).
The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues
This paper describes the results of the first shared task on the generation of teacher responses in educational dialogues.
Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency
Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability.
