Towards a mathematical theory of trajectory inference. - Hugo Lavenant; Stephen Zhang; Young-Heon Kim; Geoffrey Schiebinger (CoRR, 2021)

Optimal transport analysis reveals trajectories in steady-state systems. - Stephen Zhang; Anton Afanassiev; Laura Greenstreet; Tetsuya Matsumoto; Geoffrey Schiebinger (PLoS Comput. Biol., 2021)

Trajectory Inference via Mean-field Langevin in Path Space. - Lénaïc Chizat; Stephen Zhang; Matthieu Heitz; Geoffrey Schiebinger (NeurIPS, 2022)

Beyond kNN: Adaptive, Sparse Neighborhood Graphs via Optimal Transport. - Tetsuya Matsumoto; Stephen Zhang; Geoffrey Schiebinger (CoRR, 2022)

Manifold Learning with Sparse Regularised Optimal Transport. - Stephen Zhang; Gilles Mordant; Tetsuya Matsumoto; Geoffrey Schiebinger (CoRR, 2023)

Sparsest Models Elude Pruning: An Exposé of Pruning's Current Capabilities. - Stephen Zhang; Vardan Papyan (ICML, 2024)

Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots. - Vincent Guan; Joseph Janssen; Hossein Rahmani 005; Andrew Warren; Stephen Zhang; Elina Robeva; Geoffrey Schiebinger (CoRR, 2024)

OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition. - Stephen Zhang; Vardan Papyan (ICLR, 2025)

Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings. - Stephen Zhang; Mustafa Khan; Vardan Papyan (CoRR, 2025)

Inferring stochastic dynamics with growth from cross-sectional data. - Stephen Zhang; Suryanarayana Maddu; Xiaojie Qiu; Victor Chardès (CoRR, 2025)

On Fitting Flow Models with Large Sinkhorn Couplings. - Michal Klein; Alireza Mousavi Hosseini; Stephen Zhang; Marco Cuturi (CoRR, 2025)