arXiv Machine Learning
Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting
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arXiv Machine Learning
Sep 2
Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing
The paper introduces a manifold‑aware encoding strategy for general coded computing (GCC) that preserves the intrinsic low‑dimensional structure of high‑dimensional datasets. Unlike traditional coded‑computing designs that ignore data structure, this approach generates coded samples that follow the natural manifold of the data, inspired by graph‑based manifold learning. Experiments on neural network inference and high‑dimensional polynomial evaluation show that the new strategy consistently and significantly reduces mean squared recovery error under straggling compared with standard GCC.
arXiv Machine Learning
Sep 15
Resolution-Independent Analysis of Encoder--Decoder Operator Learning via Limiting Kernels
The paper studies operator learning on function spaces using encoder–decoder architectures. It shows that as input and output resolutions grow, the induced kernels converge to a limiting kernel, enabling regularity assumptions independent of resolution. The authors derive upper and lower bounds for regularized stochastic gradient descent, extend the analysis to neural networks via the limiting neural tangent kernel, and provide error bounds and complexity guarantees for various kernel and encoding constructions.
arXiv AI
Jul 3
Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability
arXiv:2607. 01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcomplete dictionary $\mathbf{W}\in\mathbb{R}^{m\times n}$ with $m<n$, and inferring a sparse code $\mathbf{x}\in\mathbb{R}^n$ from $\mathbf{h}\approx\mathbf{W}\mathbf{x}$.
arXiv Machine Learning
Sep 21
On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation
The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².
arXiv Machine Learning
Jun 19
How to sketch a learning algorithm
arXiv:2604. 07328v3 Announce Type: replace Abstract: How does the choice of training data influence an AI model?
arXiv Machine Learning
Aug 4
SparseKAN: Compressing Kolmogorov--Arnold Networks Across Basis Functions, Neurons, and Bits
arXiv:2608. 00859v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) replace scalar edge weights with learnable univariate functions parameterized by multiple basis coefficients.