Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph

Hao, Ning; Oghbaee, Amirreza; Rostami, Mohammad; Derbinsky, Nate; Bento, José

doi:10.1109/ipdpsw.2016.162

Cited by 8 publications

(9 citation statements)

References 25 publications

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“…6 Convergence of ADMM algorithm (Boyd et al 2011) computing DSL2 on two copies of the collaboration graph as a function of time, implemented using Apache Spark (Zaharia et al 2010) on a 40 CPU machine of the communication network in a cluster than, e.g. gradient descent (França and Bento 2017a;2017b), and it parallelizes well both on share-memory multiprocessor systems, GPUs and computer clusters (Boyd et al 2011;Parikh and Boyd 2014;Hao et al 2016).…”

Section: Resultsmentioning

confidence: 99%

A family of tractable graph metrics

Bento

Ioannidis

2019

Appl Netw Sci

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Important data mining problems such as nearest-neighbor search and clustering admit theoretical guarantees when restricted to objects embedded in a metric space. Graphs are ubiquitous, and clustering and classification over graphs arise in diverse areas, including, e.g., image processing and social networks. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics and thus come with no guarantees. Classic graph distances such as, e.g., the chemical distance and the Chartrand-Kubiki-Shultz distance are arguably natural and intuitive, and are indeed also metrics, but they are intractable: as such, their computation does not scale to large graphs. We define a broad family of graph distances, that includes both the chemical and the Chartrand-Kubiki-Shultz distances, and prove that these are all metrics. Crucially, we show that our family includes metrics that are tractable. Moreover, we extend these distances by incorporating auxiliary node attributes, which is important in practice, while maintaining both the metric property and tractability.

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Section: Resultsmentioning

confidence: 99%

A family of tractable graph metrics

Bento

Ioannidis

2019

Appl Netw Sci

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“…The Efficient Lifelong Learning Algorithm (ELLA) framework (Ruvolo & Eaton, 2013) used this same approach of a shared latent dictionary, trained online, to facilitate transfer as tasks arrive consecutively. The ELLA framework was first created for regression and classification (Ruvolo & Eaton, 2013), and later developed for policy gradient reinforcement learning (PG-ELLA) (Bou Ammar, Eaton, & Ruvolo, 2014) and collective multi-agent learning (Rostami, Kolouri, Kim, & Eaton, 2018) using distributed optimization (Hao, Oghbaee, Rostami, Derbinsky, & Bento, 2016). Other approaches that extend MTL to online settings also exist (Cavallanti, Cesa-Bianchi, & Gentile, 2010).…”

Section: Related Workmentioning

confidence: 99%

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Isele

Rostami²,

Eaton³

2020

jair

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Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.

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“…There are 3 + k function-nodes and variablenodes in total. We interpret ADMM as an iterative scheme that operates on iterates that live on the edges/nodes of the factor-graph in Figure 1, similar to the approaches in [11,12,20]. The function-nodes have the following labels: quadratic (QP), sparse (SP), bi-linear (BI (1) , .…”

Section: Solution Procedures Using Admmmentioning

confidence: 99%

“…To maximize parallelism, we implemented a fine-grained version of our algorithm, similar to [20]. The fact that we are using ADMM is important to achieve this.…”

Section: Multi-core Speedupmentioning

confidence: 99%

Estimating Cellular Goals from High-Dimensional Biological Data

Yang

Saunders

Lachance

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Optimization-based models have been used to predict cellular behavior for over 25 years. The constraints in these models are derived from genome annotations, measured macro-molecular composition of cells, and by measuring the cell's growth rate and metabolism in different conditions. The cellular goal (the optimization problem that the cell is trying to solve) can be challenging to derive experimentally for many organisms, including human or mammalian cells, which have complex metabolic capabilities and are not well understood. Existing approaches to learning goals from data include (a) estimating a linear objective function, or (b) estimating linear constraints that model complex biochemical reactions and constrain the cell's operation. The latter approach is important because often the known/observed biochemical reactions are not enough to explain observations, and hence there is a need to extend automatically the model complexity by learning new chemical reactions. However, this leads to nonconvex optimization problems, and existing tools cannot scale to realistically large metabolic models. Hence, constraint estimation is still used sparingly despite its benefits for modeling cell metabolism, which is important for developing novel antimicrobials against pathogens, discovering cancer drug targets, and producing value-added chemicals. Here, we develop the first approach to estimating constraint reactions from data that can scale to realistically large metabolic models. Previous tools have been used on problems having less than 75 biochemical reactions and 60 metabolites, which limits real-life-size applications. We perform extensive experiments using 75 large-scale metabolic network models for different organisms (including bacteria, yeasts, and mammals) and show that our algorithm can recover cellular constraint reactions. The recovered constraints enable accurate prediction of metabolic states in hundreds of growth environments not seen in training data, and we recover useful cellular goals even when some measurements are missing. * Both authors contributed equally to this project and are corresponding authors.

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Testing Fine-Grained Parallelism for the ADMM on a Factor-Graph

Cited by 8 publications

References 25 publications

A family of tractable graph metrics

A family of tractable graph metrics

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Estimating Cellular Goals from High-Dimensional Biological Data

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