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Found 30 papers, 4 papers with code
Predicting Time Series of Networked Dynamical Systems without Knowing Topology
To address these gaps, we propose a novel framework for learning network dynamics directly from observed time-series data, when prior knowledge of graph topology or governing dynamical equations is absent.
The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
Differential Privacy (DP) (and its variants) is the most common method for machine learning (ML) on privacy-sensitive data.
Reduced Label Complexity For Tight $\ell_2$ Regression
Open question: Can label complexity be reduced by $\Omega(n)$ with tight $(1+d/n)$-approximation?
Learning GraphQL Query Costs (Extended Version)
GraphQL is a query language for APIs and a runtime for executing those queries, fetching the requested data from existing microservices, REST APIs, databases, or other sources.
NoisyCUR: An algorithm for two-cost budgeted matrix completion
Specifically, we consider that it is possible to obtain low noise, high cost observations of individual entries or high noise, low cost observations of entire columns.
Training Deep Neural Networks with Constrained Learning Parameters
We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics.
A New Mathematical Model for Controlled Pandemics Like COVID-19 : AI Implemented Predictions
We present a new mathematical model to explicitly capture the effects that the three restriction measures: the lockdown date and duration, social distancing and masks, and, schools and border closing, have in controlling the spread of COVID-19 infections $i(r, t)$.
Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics
We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time.
Fast Fixed Dimension L2-Subspace Embeddings of Arbitrary Accuracy, With Application to L1 and L2 Tasks
We give a fast oblivious L2-embedding of $A\in \mathbb{R}^{n x d}$ to $B\in \mathbb{R}^{r x d}$ satisfying $(1-\varepsilon)\|A x\|_2^2 \le \|B x\|_2^2 <= (1+\varepsilon) \|Ax\|_2^2.$ Our embedding dimension $r$ equals $d$, a constant independent of the distortion $\varepsilon$.
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines
The agnostic and naive methodologies quantify the error contribution and propagation respectively from the computational steps, algorithms and hyperparameters in the image classification pipeline.
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
We apply our methodology to two major ML algorithms, namely non-negative matrix factorization (NMF) and singular value decomposition (SVD).
The Intrinsic Scale of Networks is Small
We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network.
Network Lens: Node Classification in Topologically Heterogeneous Networks
We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network.
Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines, and K-Nearest Neighbor Classifiers
In this study, we use neural networks to extract features from both images and numeric data and use these extracted features as inputs for other machine learning models, namely support vector machines (SVMs) and k-nearest neighbor classifiers (KNNs), in order to see if neural-network-extracted features enhance the capabilities of these models.
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
We propose a novel subgraph image representation for classification of network fragments with the targets being their parent networks.
Optimal Sparse Linear Encoders and Sparse PCA
Principal components analysis~(PCA) is the optimal linear encoder of data.
Node-By-Node Greedy Deep Learning for Interpretable Features
Multilayer networks have seen a resurgence under the umbrella of deep learning.
Approximating Sparse PCA from Incomplete Data
We show that for a wide class of optimization problems, if the sketch is close (in the spectral norm) to the original data matrix, then one can recover a near optimal solution to the optimization problem by using the sketch.
Recovering PCA from Hybrid-$(\ell_1,\ell_2)$ Sparse Sampling of Data Elements
This paper addresses how well we can recover a data matrix when only given a few of its elements.
Optimal Sparse Linear Auto-Encoders and Sparse PCA
Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features.
NP-Hardness and Inapproximability of Sparse PCA
We give a reduction from {\sc clique} to establish that sparse PCA is NP-hard.
Feature Selection for Linear SVM with Provable Guarantees
In the unsupervised setting, we also provide worst-case guarantees of the radius of the minimum enclosing ball, thereby ensuring comparable generalization as in the full feature space and resolving an open problem posed in Dasgupta et al. We present extensive experiments on real-world datasets to support our theory and to demonstrate that our method is competitive and often better than prior state-of-the-art, for which there are no known provable guarantees.
Random Projections for Linear Support Vector Machines
Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space.
The Fast Cauchy Transform and Faster Robust Linear Regression
We provide fast algorithms for overconstrained $\ell_p$ regression and related problems: for an $n\times d$ input matrix $A$ and vector $b\in\mathbb{R}^n$, in $O(nd\log n)$ time we reduce the problem $\min_{x\in\mathbb{R}^d} \|Ax-b\|_p$ to the same problem with input matrix $\tilde A$ of dimension $s \times d$ and corresponding $\tilde b$ of dimension $s\times 1$.
Near-optimal Coresets For Least-Squares Regression
We study (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression.
Sparse Features for PCA-Like Linear Regression
Principal Components Analysis~(PCA) is often used as a feature extraction procedure.
Deterministic Feature Selection for $k$-means Clustering
We study feature selection for $k$-means clustering.
Permutation Complexity Bound on Out-Sample Error
We define a data dependent permutation complexity for a hypothesis set \math{\hset}, which is similar to a Rademacher complexity or maximum discrepancy.
Adapting to a Market Shock: Optimal Sequential Market-Making
We study the profit-maximization problem of a monopolistic market-maker who sets two-sided prices in an asset market.
