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Found 8 papers, 4 papers with code
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
However, most of those iterative methods are based on the $\ell_1$ norm which requires restrictive applicability conditions and could fail in many cases.
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory
Specifically, we establish a clear connection between the variance of online gradients and the regret, and construct online gradients using the most recent stratified samples with a limited buffer of size of $s$ representing all past data, which have a complexity of $O(sT)$ and employs $O(\sqrt{T}\log{T})$ random Fourier features for kernel approximation.
Energy Efficient Training of SNN using Local Zeroth Order Method
To circumvent the problem surrogate method uses a differentiable approximation of the Heaviside in the backward pass, while the forward pass uses the Heaviside as the spiking function.
AGGLIO: Global Optimization for Locally Convex Functions
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees for non-convex optimization problems whose objectives offer only local convexity and may fail to be even quasi-convex at a global scale.
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems
We provide the first global model recovery results for the IRLS (iteratively reweighted least squares) heuristic for robust regression problems.
Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease.
Model Extraction Warning in MLaaS Paradigm
In this work, we present a cloud-based extraction monitor that can quantify the extraction status of models by observing the query and response streams of both individual and colluding adversarial users.
A Parameter-free Affinity Based Clustering
Several methods have been proposed to estimate the number of clusters in a dataset; the basic ideal behind all of them has been to study an index that measures inter-cluster separation and intra-cluster cohesion over a range of cluster numbers and report the number which gives an optimum value of the index.
