33 citations · 48 across the 8 of their papers we have counts for
8 papers
Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability
Rodrigo Mendoza-Smith
Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\i…
Geometric Measurements of the Axiom of Choice in Neural Proof Embeddings
Rodrigo Mendoza-Smith
The axiom of choice has divided the foundations of mathematics for over a century, but the distinction between classical and constructive proofs has remained a philosophical and me…
Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
Azul Garza, Renée Rosillo, Rodrigo Mendoza-Smith +5
Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style models. While these models often claim broad generalization, existing evaluation protoc…
Geometric Data Valuation via Leverage Scores
Rodrigo Mendoza-Smith
Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and prici…
Federated Principal Component Analysis
Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft +1
We present a federated, asynchronous, and -differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local mode…
Parallel multi-scale reduction of persistent homology filtrations
Rodrigo Mendoza-Smith, Jared Tanner
The persistent homology pipeline includes the reduction of a, so-called, boundary matrix. We extend the work of Bauer et al. (2014) and Chen et al. (2011) where they show how to us…
A robust parallel algorithm for combinatorial compressed sensing
Rodrigo Mendoza-Smith, Jared Tanner, Florian Wechsung
In previous work two of the authors have shown that a vector with at most nonzeros can be recovered from an expander sketch in $\mathcal{O}(\mathr…
Expander -Decoding
Rodrigo Mendoza-Smith, Jared Tanner
We introduce two new algorithms, Serial- and Parallel- for solving a large underdetermined linear system of equations when it is known tha…