Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. - Tomaso A. Poggio; Hrushikesh N. Mhaskar; Lorenzo Rosasco; Brando Miranda; Qianli Liao (Int. J. Autom. Comput., 2017)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Theory of Deep Learning IIb: Optimization Properties of SGD. - Chiyuan Zhang; Qianli Liao; Alexander Rakhlin; Brando Miranda; Noah Golowich; Tomaso A. Poggio (CoRR, 2018)

Theory IIIb: Generalization in Deep Networks. - Tomaso A. Poggio; Qianli Liao; Brando Miranda; Andrzej Banburski; Xavier Boix; Jack Hidary (CoRR, 2018)

A Surprising Linear Relationship Predicts Test Performance in Deep Networks. - Qianli Liao; Brando Miranda; Andrzej Banburski; Jack Hidary; Tomaso A. Poggio (CoRR, 2018)

Theory III: Dynamics and Generalization in Deep Networks. - Andrzej Banburski; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Bob Liang; Jack Hidary; Tomaso A. Poggio (CoRR, 2019)

Does MAML Only Work via Feature Re-use? A Data Centric Perspective. - Brando Miranda; Yu-Xiong Wang; Sanmi Koyejo (CoRR, 2021)

The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence. - Brando Miranda; Patrick Yu; Yu-Xiong Wang; Sanmi Koyejo (CoRR, 2022)

Are Emergent Abilities of Large Language Models a Mirage? - Rylan Schaeffer; Brando Miranda; Sanmi Koyejo (NeurIPS, 2023)

Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code. - Brando Miranda; Avi Shinnar; Vasily Pestun; Barry M. Trager (CoRR, 2023)

Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data. - Alycia Lee; Brando Miranda; Sanmi Koyejo (CoRR, 2023)

Is Pre-training Truly Better Than Meta-Learning? - Brando Miranda; Patrick Yu; Saumya Goyal; Yu-Xiong Wang; Sanmi Koyejo (CoRR, 2023)

A Systematic Study of the Role of Data Quality and Alignment for Fine-tuning LLMs for Enhanced Autoformalization. - Krrish Chawla; Mario DePavia; Aryan Sahai; Brando Miranda (Tiny Papers @ ICLR, 2024)

An Evaluation Benchmark for Autoformalization in Lean4. - Jasdeep Sidhu; Shubhra Mishra; Aryan Gulati; Devanshu Ladsaria; Brando Miranda (Tiny Papers @ ICLR, 2024)

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? - Rylan Schaeffer; Hailey Schoelkopf; Brando Miranda; Gabriel Mukobi; Varun Madan; Adam Ibrahim; Herbie Bradley; Stella Biderman; Sanmi Koyejo (CoRR, 2024)

When Do Universal Image Jailbreaks Transfer Between Vision-Language Models? - Rylan Schaeffer; Dan Valentine; Luke Bailey; James Chua; Cristóbal Eyzaguirre; Zane Durante; Joe Benton; Brando Miranda; Henry Sleight; John Hughes; Rajashree Agrawal; Mrinank Sharma; et al. (CoRR, 2024)

ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment. - Elyas Obbad; Iddah Mlauzi; Brando Miranda; Rylan Schaeffer; Kamal Obbad; Suhana Bedi; Sanmi Koyejo (CoRR, 2024)

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models. - Rylan Schaeffer; Dan Valentine; Luke Bailey; James Chua; Cristóbal Eyzaguirre; Zane Durante; Joe Benton; Brando Miranda; Henry Sleight; Tony Tong Wang; John Hughes; Rajashree Agrawal; et al. (ICLR, 2025)

Pantograph: A Machine-to-Machine Interaction Interface for Advanced Theorem Proving, High Level Reasoning, and Data Extraction in Lean 4. - Leni Aniva; Chuyue Sun; Brando Miranda; Clark W. Barrett; Sanmi Koyejo (TACAS (1), 2025)

Quantifying the Importance of Data Alignment in Downstream Model Performance. - Krrish Chawla; Aryan Sahai; Mario DePavia; Sudharsan Sundar; Brando Miranda (CoRR, 2025)

Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training. - Kavita Selva; Satita Vittayaareekul; Brando Miranda (CoRR, 2025)

Lean-ing on Quality: How High-Quality Data Beats Diverse Multilingual Data in AutoFormalization. - Willy Chan; Michael Souliman; Jakob Nordhagen; Brando Miranda; Elyas Obbad; Oluwasanmi Koyejo (CoRR, 2025)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track. - Rylan Schaeffer; Joshua Kazdan; Yegor Denisov-Blanch; Brando Miranda; Matthias Gerstgrasser; Susan Zhang; Andreas Haupt; Isha Gupta; Elyas Obbad; Jesse Dodge; Jessica Zosa Forde; Koustuv Sinha; et al. (CoRR, 2025)

Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs. - Aryan Gulati; Brando Miranda; Eric Chen; Emily Xia; Kai Fronsdal; Bruno Dumont; Elyas Obbad; Sanmi Koyejo (CoRR, 2025)