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Found 13 papers, 2 papers with code
Exphormer: Sparse Transformers for Graphs
We show that incorporating Exphormer into the recently-proposed GraphGPS framework produces models with competitive empirical results on a wide variety of graph datasets, including state-of-the-art results on three datasets.
Ranked #1 on
Graph Classification
on MNIST
Oblivious Sketching of High-Degree Polynomial Kernels
Oblivious sketching has emerged as a powerful approach to speeding up numerical linear algebra over the past decade, but our understanding of oblivious sketching solutions for kernel matrices has remained quite limited, suffering from the aforementioned exponential dependence on input parameters.
Data Structures and Algorithms
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Qualitatively, our results are twofold: on the one hand, we show that random Fourier feature approximation can provably speed up kernel ridge regression under reasonable assumptions.
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
We formalize this intuition by showing that, roughly, a continuous signal from a given class can be approximately reconstructed using a number of samples proportional to the *statistical dimension* of the allowed power spectrum of that class.
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods.
On the Power of Multiple Anonymous Messages
- Protocols in the multi-message shuffled model with $poly(\log{B}, \log{n})$ bits of communication per user and $poly\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms.
Private Aggregation from Fewer Anonymous Messages
Using a reduction of Balle et al. (2019), our improved analysis of the protocol of Ishai et al. yields, in the same model, an $\left(\varepsilon, \delta\right)$-differentially private protocol for aggregation that, for any constant $\varepsilon > 0$ and any $\delta = \frac{1}{\mathrm{poly}(n)}$, incurs only a constant error and requires only a constant number of messages per party.
Cryptography and Security Data Structures and Algorithms
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing.
Robust Learning for Congestion-Aware Routing
We consider the problem of routing users through a network with unknown congestion functions over an infinite time horizon.
Private Robust Estimation by Stabilizing Convex Relaxations
Prior works obtained private robust algorithms for mean estimation of subgaussian distributions with bounded covariance.
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 $\mathbf{V}\in\{0, 1\}^{k\times d}$.
Locality-Aware Graph-Rewiring in GNNs
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors.
Weisfeiler-Leman at the margin: When more expressivity matters
Moreover, we focus on augmenting $1$-WL and MPNNs with subgraph information and employ classical margin theory to investigate the conditions under which an architecture's increased expressivity aligns with improved generalization performance.
