Statistical Learning
An adaptive nearest neighbor rule for classification
Akshay Balsubramani, Sanjoy Dasgupta, yoav Freund, Shay Moran
We introduce an adaptive nearest neighbor classification rule.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, anderson nascimento
Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations.
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
We propose a novel, efficient search method through a general, structured kernel space.
Beyond L1: Faster and Better Sparse Models with skglm
Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration.
Nonconvex Low-Rank Tensor Completion from Noisy Data
Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen
Focusing on "incoherent" and well-conditioned tensors of a constant CP rank, we propose a two-stage nonconvex algorithm -- (vanilla) gradient descent following a rough initialization -- that achieves the best of both worlds.
Appendices for SI O: Smoothing Inference with Twisted Objectives AT able of Notation
We reproduce the general SMC algorithm in Algorithm 2 .
A Spectral Approach to Item Response Theory
The core of our algorithm is the computation of the stationary distribution of a Markov chain defined on an item-item graph.
Capturing Graphs with Hypo-Elliptic Diffusions Csaba Toth Mathematical Institute University of Oxford
Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks.