Statistical Learning
$\ell_1$-regression with Heavy-tailed Distributions
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Using Embeddings to Correct for Unobserved Confounding in Networks
Victor Veitch, Yixin Wang, David Blei
Neural Information Processing Systems http://nips.cc/
Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi
Neural Information Processing Systems http://nips.cc/
Estimating Convergence of Markov chains with L-Lag Couplings
Niloy Biswas, Pierre E. Jacob, Paul Vanetti
Various theoretical results provideupper bounds onthedistance between the target and marginal distribution after a fixed number of iterations.
ae614c557843b1df326cb29c57225459-Paper.pdf
In this work, we showthat this "lazy training" phenomenon isnot specific tooverparameterized neural networks, and is due to a choice of scaling, often implicit, that makes the model behave as its linearization around the initialization, thus yielding amodel equivalenttolearning withpositive-definite kernels.
Model-based targeted dimensionality reduction for neuronal population data
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Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han, Haim Avron, Jinwoo Shin
Unfortunately, computing the gradient of a spectral function is generally of cubic complexity, as such gradient descent methods are rather expensive for optimizing objectives involving the spectral function.
Data Selection for Language Models via Importance Resampling
Selecting a suitable pretraining dataset is crucial for both general-domain (e.g., GPT -
How to tell when a clustering is (approximately) correct using convex relaxations
Neural Information Processing Systems http://nips.cc/
