This paper studies distributed Bayesian learning in a setting encompassing a central server and multiple workers by focusing on the problem of mitigating the impact of stragglers. The standard one-shot, or embarrassingly parallel, Bayesian learning protocol known as consensus Monte Carlo (CMC) is generalized by proposing two straggler-resilient solutions based on grouping and coding. Two main challenges in designing straggler-resilient algorithms for CMC are the need to estimate the statistics of the workers' outputs across multiple shots, and the joint non-linear post-processing of the outputs of the workers carried out at the server. This is in stark contrast to other distributed settings like gradient coding, which only require the per-s...
In today’s world machine learning has major applications in a wide variety of tasks such as image c...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
Distributed implementations are crucial in speeding up large scale machine learning applications. Di...
AbstractThis paper addresses the issue of designing an effective distributed learning system in whic...
To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribut...
Probabilistic inference on a big data scale is becoming increasingly relevant to both the machine le...
Probabilistic inference on a big data scale is becoming increasingly relevant to both the machine le...
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stoc...
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, wh...
A useful definition of ‘big data’ is data that is too big to process comfortably on a single machine...
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning appli...
Consensus-based distributed learning is a machine learning technique used to find the general consen...
Big data, including applications with high security requirements, are often collected and stored on...
In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of ...
A current challenge for data management systems is to support the construction and maintenance of ma...
In today’s world machine learning has major applications in a wide variety of tasks such as image c...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
Distributed implementations are crucial in speeding up large scale machine learning applications. Di...
AbstractThis paper addresses the issue of designing an effective distributed learning system in whic...
To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribut...
Probabilistic inference on a big data scale is becoming increasingly relevant to both the machine le...
Probabilistic inference on a big data scale is becoming increasingly relevant to both the machine le...
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stoc...
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, wh...
A useful definition of ‘big data’ is data that is too big to process comfortably on a single machine...
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning appli...
Consensus-based distributed learning is a machine learning technique used to find the general consen...
Big data, including applications with high security requirements, are often collected and stored on...
In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of ...
A current challenge for data management systems is to support the construction and maintenance of ma...
In today’s world machine learning has major applications in a wide variety of tasks such as image c...
We present a new parallel algorithm for learning Bayesian inference networks from data. Our learning...
Distributed implementations are crucial in speeding up large scale machine learning applications. Di...