Search Results for author: Chris Piech

Found 27 papers, 15 papers with code

From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation

no code implementations5 Jun 2024 Ali Malik, Stephen Mayhew, Chris Piech, Klinton Bicknell

We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language learners.

Language Modelling Reinforcement Learning (RL)

Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency

no code implementations14 Nov 2023 Yunsung Kim, Sreechan Sankaranarayanan, Chris Piech, Candace Thille

Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability.

Machine Translation for Nko: Tools, Corpora and Baseline Results

2 code implementations24 Oct 2023 Moussa Koulako Bala Doumbouya, Baba Mamadi Diané, Solo Farabado Cissé, Djibrila Diané, Abdoulaye Sow, Séré Moussa Doumbouya, Daouda Bangoura, Fodé Moriba Bayo, Ibrahima Sory 2. Condé, Kalo Mory Diané, Chris Piech, Christopher Manning

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.

Machine Translation Translation

The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues

no code implementations12 Jun 2023 Anaïs Tack, Ekaterina Kochmar, Zheng Yuan, Serge Bibauw, Chris Piech

This paper describes the results of the first shared task on the generation of teacher responses in educational dialogues.

Bayesian Decision Trees via Tractable Priors and Probabilistic Context-Free Grammars

no code implementations15 Feb 2023 Colin Sullivan, Mo Tiwari, Sebastian Thrun, Chris Piech

Once the posterior has been learned, trees can be sampled efficiently (linearly in the number of nodes).

MABSplit: Faster Forest Training Using Multi-Armed Bandits

1 code implementation14 Dec 2022 Mo Tiwari, Ryan Kang, Je-Yong Lee, Sebastian Thrun, Chris Piech, Ilan Shomorony, Martin Jinye Zhang

We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods.

Feature Importance Multi-Armed Bandits

Faster Maximum Inner Product Search in High Dimensions

no code implementations14 Dec 2022 Mo Tiwari, Ryan Kang, Je-Yong Lee, DongHyun Lee, Chris Piech, Sebastian Thrun, Ilan Shomorony, Martin Jinye Zhang

We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$.

Multi-Armed Bandits Recommendation Systems +1

Giving Feedback on Interactive Student Programs with Meta-Exploration

1 code implementation16 Nov 2022 Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn

However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.

The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues

1 code implementation16 May 2022 Anaïs Tack, Chris Piech

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?

Play to Grade: Testing Coding Games as Classifying Markov Decision Process

1 code implementation NeurIPS 2021 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.

Modeling Item Response Theory with Stochastic Variational Inference

no code implementations26 Aug 2021 Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman

Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions.

Bayesian Inference Variational Inference

On the Opportunities and Risks of Foundation Models

2 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

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.

Transfer Learning

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

1 code implementation23 Jul 2021 Mike Wu, Noah Goodman, Chris Piech, Chelsea Finn

High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale.

Few-Shot Learning

Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users

1 code implementation27 Apr 2021 Moussa Doumbouya, Lisa Einstein, Chris Piech

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.

Language Identification Representation Learning +2

Play to Grade: Grading Interactive Coding Games as Classifying Markov Decision Process

no code implementations1 Jan 2021 Allen Nie, Emma Brunskill, Chris Piech

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.

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

1 code implementation NeurIPS 2020 Mo Tiwari, Martin J. Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony

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.

Clustering Multi-Armed Bandits

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits

2 code implementations11 Jun 2020 Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony

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.

Clustering Multi-Armed Bandits

Variational Item Response Theory: Fast, Accurate, and Expressive

1 code implementation1 Feb 2020 Mike Wu, Richard L. Davis, Benjamin W. Domingue, Chris Piech, Noah Goodman

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.

Bayesian Inference

Human Languages in Source Code: Auto-Translation for Localized Instruction

no code implementations10 Sep 2019 Chris Piech, Sami Abu-El-Haija

The study is to the best of our knowledge the first on human-language in code and covers 2. 9 million Java repositories.


The Stanford Acuity Test: A Precise Vision Test Using Bayesian Techniques and a Discovery in Human Visual Response

no code implementations5 Jun 2019 Chris Piech, Ali Malik, Laura M Scott, Robert T. Chang, Charles Lin

First, we uncover a new parametric probabilistic model of visual acuity response based on detailed measurements of patients with eye disease.

Using Latent Variable Models to Observe Academic Pathways

no code implementations31 May 2019 Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers.

General Classification

Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems

1 code implementation23 May 2019 Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, Madison Coots, John Mitchell, Noah Goodman, Chris Piech

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.

Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

1 code implementation5 Sep 2018 Mike Wu, Milan Mosse, Noah Goodman, Chris Piech

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.

Misconceptions Zero-Shot Learning

Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

2 code implementations30 Jun 2018 Christina Wadsworth, Francesca Vera, Chris Piech

Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates.

Fairness Management

Deep Knowledge Tracing

6 code implementations NeurIPS 2015 Chris Piech, Jonathan Spencer, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas Guibas, Jascha Sohl-Dickstein

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.

Knowledge Tracing

Learning Program Embeddings to Propagate Feedback on Student Code

no code implementations22 May 2015 Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas

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

no code implementations9 Jul 2013 Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller

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.

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