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Found 9 papers, 0 papers with code
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model.
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization
Neural network pruning is a key technique towards engineering large yet scalable, interpretable, and generalizable models.
Greedy PIG: Adaptive Integrated Gradients
We demonstrate the success of Greedy PIG on a wide variety of tasks, including image feature attribution, graph compression/explanation, and post-hoc feature selection on tabular data.
Performance of $\ell_1$ Regularization for Sparse Convex Optimization
We give the first recovery guarantees for the Group LASSO for sparse convex optimization with vector-valued features.
Gradient Descent Converges Linearly for Logistic Regression on Separable Data
We show that running gradient descent with variable learning rate guarantees loss $f(x) \leq 1. 1 \cdot f(x^*) + \epsilon$ for the logistic regression objective, where the error $\epsilon$ decays exponentially with the number of iterations and polynomially with the magnitude of the entries of an arbitrary fixed solution $x^*$.
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
We propose a simple modification to the iterative hard thresholding (IHT) algorithm, which recovers asymptotically sparser solutions as a function of the condition number.
Decomposable Submodular Function Minimization via Maximum Flow
This paper bridges discrete and continuous optimization approaches for decomposable submodular function minimization, in both the standard and parametric settings.
Local Search Algorithms for Rank-Constrained Convex Optimization
We propose greedy and local search algorithms for rank-constrained convex optimization, namely solving $\underset{\mathrm{rank}(A)\leq r^*}{\min}\, R(A)$ given a convex function $R:\mathbb{R}^{m\times n}\rightarrow \mathbb{R}$ and a parameter $r^*$.
Sparse Convex Optimization via Adaptively Regularized Hard Thresholding
We present a new Adaptively Regularized Hard Thresholding (ARHT) algorithm that makes significant progress on this problem by bringing the bound down to $\gamma=O(\kappa)$, which has been shown to be tight for a general class of algorithms including LASSO, OMP, and IHT.
