Efficient Model-Free Learning to Overcome Hardware Nonidealities in Analog-to-Information Converters. - Sam Buchanan; Tanbir Haque; Peter R. Kinget; John Wright 001 (ICASSP, 2018)

Efficient Dictionary Learning with Gradient Descent. - Dar Gilboa; Sam Buchanan; John Wright 001 (ICML, 2019)

Deep Networks and the Multiple Manifold Problem. - Sam Buchanan; Dar Gilboa; John Wright 001 (ICLR, 2021)

Deep Networks Provably Classify Data on Curves. - Tingran Wang; Sam Buchanan; Dar Gilboa; John Wright 001 (NeurIPS, 2021)

Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization. - Sam Buchanan; Jingkai Yan; Ellie Haber; John Wright 001 (CoRR, 2022)

Deep Networks Through the Lens of Low-Dimensional Structure: Towards Mathematical and Computational Principles for Nonlinear Data - Sam Buchanan (2022)

Canonical Factors for Hybrid Neural Fields. - Brent Yi; Weijia Zeng; Sam Buchanan; Yi Ma 001 (ICCV, 2023)

White-Box Transformers via Sparse Rate Reduction. - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Benjamin D. Haeffele; Yi Ma 001 (NeurIPS, 2023)

White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? - Yaodong Yu; Sam Buchanan; Druv Pai; Tianzhe Chu; Ziyang Wu; Shengbang Tong; Hao Bai; Yuexiang Zhai; Benjamin D. Haeffele; Yi Ma 001 (CoRR, 2023)

Emergence of Segmentation with Minimalistic White-Box Transformers. - Yaodong Yu; Tianzhe Chu; Shengbang Tong; Ziyang Wu; Druv Pai; Sam Buchanan; Yi Ma 001 (CPAL, 2024)

What's in a Prior? Learned Proximal Networks for Inverse Problems. - Zhenghan Fang; Sam Buchanan; Jeremias Sulam (ICLR, 2024)

Masked Completion via Structured Diffusion with White-Box Transformers. - Druv Pai; Sam Buchanan; Ziyang Wu; Yaodong Yu; Yi Ma 001 (ICLR, 2024)

Beyond Scores: Proximal Diffusion Models. - Zhenghan Fang; Mateo Díaz; Sam Buchanan; Jeremias Sulam (CoRR, 2025)