Rohan Taori

I am a PhD student in the CS department at Stanford University, advised by the wonderful Tatsu Hashimoto. I like studying the foundations of machine learning in the context of real-world systems. I am supported by the NSF GRFP Fellowship.

Previously, I graduated with my BS in EECS from UC Berkeley. I am very fortunate to have worked with Ludwig Schmidt & Ben Recht during my time there. I also had an amazing time teaching at and interacting with the Machine Learning @ Berkeley community.

Google Scholar / Github / Twitter

Email: rtaori_at_stanford_dot_edu

Research Stuff

Stanford Alpaca: A Strong, Replicable Instruction-Following Model
Rohan Taori*, Ishaan Gulrajani*, Tianyi Zhang*, Yann Dubois*, Xuechen Li*, Carlos Guestrin, Percy Liang, Tatsunori B Hashimoto
Blog Post / Code & Data

Data Feedback Loops: Model-driven Amplification of Dataset Biases
Rohan Taori, Tatsunori B Hashimoto
Oral at International Conference on Machine Learning (ICML), 2023. Spotlight at NeurIPS Workshop on Distribution Shifts, 2022.
Paper / Code

Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning
Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori B Hashimoto
International Conference on Learning Representations (ICLR), 2023.
Paper

Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning
Thomas Liao, Rohan Taori, Inioluwa Deborah Raji, Ludwig Schmidt
Benchmarks and Datasets Track, NeurIPS, 2021.
Paper

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt
International Conference on Machine Learning (ICML), 2021.
Paper / Talk / Interactive Plotting

Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
Spotlight at Advances in Neural Information Processing Systems (NeurIPS), 2020.
Paper / Website / Talk / Code & Data / Interactive Plotting

Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Guilin Liu, Rohan Taori, Ting-Chun Wang, Zhiding Yu, Shiqiu Liu, Fitsum Reda, Karan Sapra, Andrew Tao, Bryan Catanzaro
Preprint, 2020.
Paper / Short Video / Long Video

Targeted Adversarial Examples for Black Box Audio Systems
Rohan Taori, Amog Kamsetty, Brenton Chu, Nikita Vemuri
IEEE Deep Learning and Security Workshops, 2019.
Paper / Code

Teaching Stuff

I ran or was heavily involved with Machine Learning @ Berkeley's Data Science Class for a number of semesters. You can find complete course content (lecture slides, demos, & homeworks) from those semesters online: Fall '17, Spring '18, Fall '18. Some lectures I gave:

Data Science 101 Lecture on analyzing 2016 campaign finance donations using numpy, pandas, and matplotlib.
Clustering Lecture on common clustering techniques and a very cool flower compression demo.
Linear Regression Lecture on the basics of linear regression.
SVM Lecture on how Support Vector Machines (SVMs) work along with a cool demo.
GAN Lecture on how Generative Adversarial Networks (GANs) work.
Image Captioning Lecture on how common image captioning models are trained.
Intro to Deep Learning Workshop I gave with Sajel Shah at CalHacks 5.0.
Intro to Reinforcement Learning Workshop I gave with Brenton Chu.

Website template from Jon Barron.