Context-Free Transductions with Neural Stacks. - Yiding Hao; William Merrill; Dana Angluin; Robert Frank 001; Noah Amsel; Andrew Benz; Simon Mendelsohn (BlackboxNLP@EMNLP, 2018)

Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing. - William Merrill; Lenny Khazan; Noah Amsel; Yiding Hao; Simon Mendelsohn; Robert Frank 001 (BlackboxNLP@ACL, 2019)

Finding Syntactic Representations in Neural Stacks. - William Merrill; Lenny Khazan; Noah Amsel; Yiding Hao; Simon Mendelsohn; Robert Frank 001 (CoRR, 2019)

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis. - Noah Amsel; Jordi Ros-Giralt; Sruthi Yellamraju; James R. Ezick; Brendan von Hofe; Alison Ryan; Richard Lethin (INDIS@SC, 2020)

Spectral neighbor joining for reconstruction of latent tree models. - Ariel Jaffe; Noah Amsel; Boaz Nadler; Joseph T. Chang; Yuval Kluger (CoRR, 2020)

Boundary Integral Solver Approaches for Particle Accelerator Simulation Problems and Deployment on NERSC Hardware. - Julia Wei; Matthew Harper Langston; Pierre-David Letourneau; Matthew J. Morse; Larry Weintraub; Aimee Nogoy; Noah Amsel; Richard Lethin (HPEC, 2021)

Designing data center networks using bottleneck structures. - Jordi Ros-Giralt; Noah Amsel; Sruthi Yellamraju; James R. Ezick; Richard Lethin; Yuang Jiang; Aosong Feng; Leandros Tassiulas; Zhenguo Wu; Min Yee Teh; Keren Bergman (SIGCOMM, 2021)

Spectral Top-Down Recovery of Latent Tree Models. - Yariv Aizenbud; Ariel Jaffe; Meng Wang; Amber Hu; Noah Amsel; Boaz Nadler; Joseph T. Chang; Yuval Kluger (CoRR, 2021)

Spectral Neighbor Joining for Reconstruction of Latent Tree Models. - Ariel Jaffe; Noah Amsel; Yariv Aizenbud; Boaz Nadler; Joseph T. Chang; Yuval Kluger (SIAM J. Math. Data Sci., 2021)

A Quantitative Theory of Bottleneck Structures for Data Networks. - Jordi Ros-Giralt; Noah Amsel; Sruthi Yellamraju; James R. Ezick; Richard Lethin; Yuang Jiang; Aosong Feng; Leandros Tassiulas (CoRR, 2022)

Near-Optimality Guarantees for Approximating Rational Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (CoRR, 2023)

Nearly Optimal Approximation of Matrix Functions by the Lanczos Method. - Noah Amsel; Tyler Chen; Anne Greenbaum; Cameron Musco; Christopher Musco (NeurIPS, 2024)

Fixed-sparsity matrix approximation from matrix-vector products. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco (CoRR, 2024)

On the Benefits of Rank in Attention Layers. - Noah Amsel; Gilad Yehudai; Joan Bruna (CoRR, 2024)

Quality over Quantity in Attention Layers: When Adding More Heads Hurts. - Noah Amsel; Gilad Yehudai; Joan Bruna (ICLR, 2025)

Compositional Reasoning with Transformers, RNNs, and Chain of Thought. - Gilad Yehudai; Noah Amsel; Joan Bruna (CoRR, 2025)

The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm. - Noah Amsel; David Persson; Christopher Musco; Robert Gower (CoRR, 2025)

Quasi-optimal hierarchically semi-separable matrix approximation. - Noah Amsel; Tyler Chen; Feyza Duman Keles; Diana Halikias; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)

Query Efficient Structured Matrix Learning. - Noah Amsel; Pratyush Avi; Tyler Chen; Feyza Duman Keles; Chinmay Hegde; Cameron Musco; Christopher Musco; David Persson (CoRR, 2025)