Search Results for author: Ali Malik

Found 8 papers, 3 papers with code

Lifting uniform learners via distributional decomposition

no code implementations27 Mar 2023 Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan

We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$.

LEMMA PAC learning +1

Popular decision tree algorithms are provably noise tolerant

no code implementations17 Jun 2022 Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan

Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4. 5, and CART, are highly noise tolerant.

On the power of adaptivity in statistical adversaries

no code implementations19 Nov 2021 Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan

Specifically, can the behavior of an algorithm $\mathcal{A}$ in the presence of oblivious adversaries always be well-approximated by that of an algorithm $\mathcal{A}'$ in the presence of adaptive adversaries?

On the Opportunities and Risks of Foundation Models

3 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

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.