Search Results for author: Haris Vikalo

Found 21 papers, 3 papers with code

Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications

no code implementations24 Mar 2021 Sangsu Lee, Xi Zheng, Jie Hua, Haris Vikalo, Christine Julien

We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences.

Federated Learning

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

1 code implementation NeurIPS 2020 Ziqi Ke, Haris Vikalo

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.

Dimensionality Reduction

On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization

no code implementations20 Nov 2020 Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit Dhillon

In this paper, we show that, in such compressed decentralized optimization settings, there are benefits to having {\em multiple} gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e. g. by means of reducing the precision of compressed information.

Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder

no code implementations16 Nov 2020 Ziqi Ke, Haris Vikalo

Identification of the type of communication technology and/or modulation scheme based on detected radio signal are challenging problems encountered in a variety of applications including spectrum allocation and radio interference mitigation.

Denoising General Classification

Communication-Efficient Federated Learning via Optimal Client Sampling

no code implementations30 Jul 2020 Monica Ribero, Haris Vikalo

The central server collects updated local models from only the selected clients and combines them with estimated model updates of the clients that were not selected for communication.

Federated Learning Language Modelling +1

Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints

no code implementations27 May 2020 Yiyue Chen, Abolfazl Hashemi, Haris Vikalo

We propose a communication-efficient algorithm for decentralized convex optimization that rely on sparsification of local updates exchanged between neighboring agents in the network.

Federating Recommendations Using Differentially Private Prototypes

no code implementations1 Mar 2020 Mónica Ribero, Jette Henderson, Sinead Williamson, Haris Vikalo

However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn such a model without accessing the sensitive data, and without inadvertently leaking private information?

Recommendation Systems

Evolutionary Clustering via Message Passing

no code implementations27 Dec 2019 Natalia M. Arzeno, Haris Vikalo

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step.

A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)

no code implementations25 Dec 2019 Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, Haris Vikalo, Sarfraz Khurshid

However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.

ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly

no code implementations27 Nov 2019 Abishek Sankararaman, Haris Vikalo, François Baccelli

Results: We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph.

Community Detection

A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

1 code implementation13 Nov 2019 Ziqi Ke, Haris Vikalo

Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities.

Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach

no code implementations27 Sep 2019 Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo, Ufuk Topcu

We formulate the task of representation learning as that of mapping the state space of the model to a low-dimensional state space, called the kernel space.

Representation Learning

Progressive Stochastic Greedy Sparse Reconstruction and Support Selection

no code implementations22 Jul 2019 Abolfazl Hashemi, Haris Vikalo, Gustavo de Veciana

In this paper, we present the first sparse support selection algorithm for arbitrary sparse vectors that achieves exact identification of the optimal subset from $n = \mathcal{O}(k\log\frac{m}{k})$ measurements with $\tilde{\mathcal{O}}(m)$ computational complexity.

Evolutionary Self-Expressive Models for Subspace Clustering

no code implementations29 Oct 2018 Abolfazl Hashemi, Haris Vikalo

The problem of organizing data that evolves over time into clusters is encountered in a number of practical settings.

Matrix Completion and Performance Guarantees for Single Individual Haplotyping

no code implementations13 Jun 2018 Somsubhra Barik, Haris Vikalo

In this paper, we consider a binary matrix factorization formulation of the single individual haplotyping problem and efficiently solve it by means of alternating minimization.

Matrix Completion

Accelerated Sparse Subspace Clustering

no code implementations31 Oct 2017 Abolfazl Hashemi, Haris Vikalo

State-of-the-art algorithms for sparse subspace clustering perform spectral clustering on a similarity matrix typically obtained by representing each data point as a sparse combination of other points using either basis pursuit (BP) or orthogonal matching pursuit (OMP).

Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation

no code implementations31 Oct 2017 Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo, Gonzalo Mateos

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE).

Sparse recovery via Orthogonal Least-Squares under presence of Noise

no code implementations8 Aug 2016 Abolfazl Hashemi, Haris Vikalo

We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of a $m$-dimensional $k$-sparse signal from a low number of noisy linear measurements.

Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares

1 code implementation8 Aug 2016 Abolfazl Hashemi, Haris Vikalo

We analyze the performance of AOLS and establish lower bounds on the probability of exact recovery for both noiseless and noisy random linear measurements.

Sparse Linear Regression via Generalized Orthogonal Least-Squares

no code implementations22 Feb 2016 Abolfazl Hashemi, Haris Vikalo

Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an $l_0$-constrained least-squares problem.

Designing Optimal Mortality Risk Prediction Scores that Preserve Clinical Knowledge

no code implementations19 Nov 2014 Natalia M. Arzeno, Karla A. Lawson, Sarah V. Duzinski, Haris Vikalo

In this paper, we seek to develop risk prediction scores that preserve clinical knowledge embedded in features and structure of the existing additive stepwise scores while addressing limitations caused by variable dichotomization.

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