LCA: Loss Change Allocation for Neural Network Training. - Janice Lan; Rosanne Liu; Hattie Zhou; Jason Yosinski (NeurIPS, 2019)

Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask. - Hattie Zhou; Janice Lan; Rosanne Liu; Jason Yosinski (NeurIPS, 2019)

Fortuitous Forgetting in Connectionist Networks. - Hattie Zhou; Ankit Vani; Hugo Larochelle; Aaron C. Courville (ICLR, 2022)

Teaching Algorithmic Reasoning via In-context Learning. - Hattie Zhou; Azade Nova; Hugo Larochelle; Aaron C. Courville; Behnam Neyshabur; Hanie Sedghi (CoRR, 2022)

Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok. - Pascal Tikeng Notsawo Jr.; Hattie Zhou; Mohammad Pezeshki; Irina Rish; Guillaume Dumas (CoRR, 2023)

Vanishing Gradients in Reinforcement Finetuning of Language Models. - Noam Razin; Hattie Zhou; Omid Saremi; Vimal Thilak; Arwen Bradley; Preetum Nakkiran; Joshua M. Susskind; Etai Littwin (ICLR, 2024)

What Algorithms can Transformers Learn? A Study in Length Generalization. - Hattie Zhou; Arwen Bradley; Etai Littwin; Noam Razin; Omid Saremi; Joshua M. Susskind; Samy Bengio; Preetum Nakkiran (ICLR, 2024)

A Formal Framework for Understanding Length Generalization in Transformers. - Xinting Huang; Andy Yang; Satwik Bhattamishra; Yash Raj Sarrof; Andreas Krebs; Hattie Zhou; Preetum Nakkiran; Michael Hahn 001 (ICLR, 2025)

Step-by-Step Diffusion: An Elementary Tutorial. - Preetum Nakkiran; Arwen Bradley; Hattie Zhou; Madhu Advani (Found. Trends Comput. Graph. Vis., 2025)

Societal Alignment Frameworks Can Improve LLM Alignment. - Karolina Stanczak; Nicholas Meade; Mehar Bhatia; Hattie Zhou; Konstantin Böttinger; Jeremy Barnes 002; Jason Stanley; Jessica Montgomery; Richard S. Zemel; Nicolas Papernot; Nicolas Chapados; Denis Therien; et al. (CoRR, 2025)