Search Results for author: Tal Wagner

Found 15 papers, 4 papers with code

Generalization Bounds for Data-Driven Numerical Linear Algebra

no code implementations16 Jun 2022 Peter Bartlett, Piotr Indyk, Tal Wagner

Our techniques are general, and provide generalization bounds for many other recently proposed data-driven algorithms in numerical linear algebra, covering both sketching-based and multigrid-based methods.

Generalization Bounds PAC learning

Unveiling Transformers with LEGO: a synthetic reasoning task

no code implementations9 Jun 2022 Yi Zhang, Arturs Backurs, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Tal Wagner

We propose a synthetic task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the transformer architecture learns this task.

Learning to Execute

Triangle and Four Cycle Counting with Predictions in Graph Streams

no code implementations ICLR 2022 Justin Y. Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David P. Woodruff, Michael Zhang

We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature.

Few-Shot Data-Driven Algorithms for Low Rank Approximation

no code implementations NeurIPS 2021 Piotr Indyk, Tal Wagner, David Woodruff

Recently, data-driven and learning-based algorithms for low rank matrix approximation were shown to outperform classical data-oblivious algorithms by wide margins in terms of accuracy.

Learning-based Support Estimation in Sublinear Time

no code implementations ICLR 2021 Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner

We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements.

Faster Kernel Matrix Algebra via Density Estimation

no code implementations16 Feb 2021 Arturs Backurs, Piotr Indyk, Cameron Musco, Tal Wagner

In particular, we consider estimating the sum of kernel matrix entries, along with its top eigenvalue and eigenvector.

Density Estimation

Space and Time Efficient Kernel Density Estimation in High Dimensions

1 code implementation NeurIPS 2019 Arturs Backurs, Piotr Indyk, Tal Wagner

We instantiate our framework with the Laplacian and Exponential kernels, two popular kernels which possess the aforementioned property.

Density Estimation

Scalable Nearest Neighbor Search for Optimal Transport

1 code implementation ICML 2020 Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Our extensive experiments, on real-world text and image datasets, show that Flowtree improves over various baselines and existing methods in either running time or accuracy.

Data Structures and Algorithms

Sample-Optimal Low-Rank Approximation of Distance Matrices

no code implementations2 Jun 2019 Piotr Indyk, Ali Vakilian, Tal Wagner, David Woodruff

Recent work by Bakshi and Woodruff (NeurIPS 2018) showed it is possible to compute a rank-$k$ approximation of a distance matrix in time $O((n+m)^{1+\gamma}) \cdot \mathrm{poly}(k, 1/\epsilon)$, where $\epsilon>0$ is an error parameter and $\gamma>0$ is an arbitrarily small constant.

Handwriting Recognition

Scalable Fair Clustering

1 code implementation10 Feb 2019 Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner

In the fair variant of $k$-median, the points are colored, and the goal is to minimize the same average distance objective while ensuring that all clusters have an "approximately equal" number of points of each color.

Fairness

Learning Space Partitions for Nearest Neighbor Search

1 code implementation ICLR 2020 Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.

General Classification graph partitioning +1

Semi-Supervised Learning on Data Streams via Temporal Label Propagation

no code implementations ICML 2018 Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra

We consider the problem of labeling points on a fast-moving data stream when only a small number of labeled examples are available.

A graph-theoretic approach to multitasking

no code implementations NeurIPS 2017 Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Ozcimder

A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.

Volume Regularization for Binary Classification

no code implementations NeurIPS 2012 Koby Crammer, Tal Wagner

We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes.

Classification General Classification +1

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