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Found 25 papers, 3 papers with code
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in\mathbb{R}^d$.
Learning-Augmented Skip Lists
We study the integration of machine learning advice into the design of skip lists to improve upon traditional data structure design.
Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization
We introduce efficient $(1+\varepsilon)$-approximation algorithms for the binary matrix factorization (BMF) problem, where the inputs are a matrix $\mathbf{A}\in\{0, 1\}^{n\times d}$, a rank parameter $k>0$, as well as an accuracy parameter $\varepsilon>0$, and the goal is to approximate $\mathbf{A}$ as a product of low-rank factors $\mathbf{U}\in\{0, 1\}^{n\times k}$ and $\mathbf{V}\in\{0, 1\}^{k\times d}$.
Provable Data Subset Selection For Efficient Neural Network Training
In this paper, we introduce the first algorithm to construct coresets for \emph{RBFNNs}, i. e., small weighted subsets that approximate the loss of the input data on any radial basis function network and thus approximate any function defined by an \emph{RBFNN} on the larger input data.
Streaming Algorithms for Learning with Experts: Deterministic Versus Robust
In the online learning with experts problem, an algorithm must make a prediction about an outcome on each of $T$ days (or times), given a set of $n$ experts who make predictions on each day (or time).
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.
On Differential Privacy and Adaptive Data Analysis with Bounded Space
We study the space complexity of the two related fields of differential privacy and adaptive data analysis.
Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation
We build on the recent Kernel Density Estimation framework, which (after preprocessing in time subquadratic in $n$) can return estimates of row/column sums of the kernel matrix.
Learning-Augmented Algorithms for Online Linear and Semidefinite Programming
We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i. e., consistency.
Adaptive Sketches for Robust Regression with Importance Sampling
In this paper, we introduce an algorithm that approximately samples $T$ gradients of dimension $d$ from nearly the optimal importance sampling distribution for a robust regression problem over $n$ rows.
Hardness and Algorithms for Robust and Sparse Optimization
We further show fine-grained hardness of robust regression through a reduction from the minimum-weight $k$-clique conjecture.
Memory Bounds for the Experts Problem
We initiate the study of the learning with expert advice problem in the streaming setting, and show lower and upper bounds.
New Coresets for Projective Clustering and Applications
$(j, k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
Learning-Augmented $k$-means Clustering
$k$-means clustering is a well-studied problem due to its wide applicability.
Dimensionality Reduction for Wasserstein Barycenter
In order to cope with this "curse of dimensionality," we study dimensionality reduction techniques for the Wasserstein barycenter problem.
Adversarial Robustness of Streaming Algorithms through Importance Sampling
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction.
Learning a Latent Simplex in Input-Sparsity Time
We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$).
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time
Moreover, we show that our algorithm can be generalized to approximately sample Hessians and thus provides variance reduction for second-order methods as well.
Learning a Latent Simplex in Input Sparsity Time
Bhattacharyya and Kannan (SODA 2020) give an algorithm for learning such a $k$-vertex latent simplex in time roughly $O(k\cdot\text{nnz}(\mathbf{A}))$, where $\text{nnz}(\mathbf{A})$ is the number of non-zeros in $\mathbf{A}$.
Data-Independent Structured Pruning of Neural Networks via Coresets
Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest.
Approximation Algorithms for Sparse Principal Component Analysis
To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA).
Non-Adaptive Adaptive Sampling on Turnstile Streams
Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation.
"Bring Your Own Greedy"+Max: Near-Optimal $1/2$-Approximations for Submodular Knapsack
Our analysis is based on a new set of first-order linear differential inequalities and their robust approximate versions.
Data-Independent Neural Pruning via Coresets
We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.
Adversarially Robust Submodular Maximization under Knapsack Constraints
We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings.
