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Qirong Ho

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12 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

FedNAR: Federated Optimization with Normalized Annealing Regularization

Junbo Li
Ang Li
Chong Tian
Qirong Ho
Eric Xing
Hongyi Wang

Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prevent overfitting in local clients. In this paper, we first explore the choices of weight decay and identify that weight decay value appreciably influences the convergence of existing FL algorithms. While preventing overfitting is crucial, weight decay can introduce a different optimization goal towards the global objective, which is further amplified in FL due to multiple local updates and heterogeneous data distribution. To address this challenge, we develop {\it Federated optimization with Normalized Annealing Regularization} (FedNAR), a simple yet effective and versatile algorithmic plug-in that can be seamlessly integrated into any existing FL algorithms. Essentially, we regulate the magnitude of each update by performing co-clipping of the gradient and weight decay. We provide a comprehensive theoretical analysis of FedNAR's convergence rate and conduct extensive experiments on both vision and language datasets with different backbone federated optimization algorithms. Our experimental results consistently demonstrate that incorporating FedNAR into existing FL algorithms leads to accelerated convergence and heightened model accuracy. Moreover, FedNAR exhibits resilience in the face of various hyperparameter configurations. Specifically, FedNAR has the ability to self-adjust the weight decay when the initial specification is not optimal, while the accuracy of traditional FL algorithms would markedly decline. Our codes are released at \href{https: //anonymous. 4open. science/r/fednar-BE8F}{https: //anonymous. 4open. science/r/fednar-BE8F}.

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NeurIPS Conference 2022 Conference Paper

Unsupervised Image-to-Image Translation with Density Changing Regularization

Shaoan Xie
Qirong Ho
Kun Zhang

Unpaired image-to-image translation aims to translate an input image to another domain such that the output image looks like an image from another domain while important semantic information are preserved. Inferring the optimal mapping with unpaired data is impossible without making any assumptions. In this paper, we make a density changing assumption where image patches of high probability density should be mapped to patches of high probability density in another domain. Then we propose an efficient way to enforce this assumption: we train the flows as density estimators and penalize the variance of density changes. Despite its simplicity, our method achieves the best performance on benchmark datasets and needs only $56-86\%$ of training time of the existing state-of-the-art method. The training and evaluation code are avaliable at $$\url{https: //github. com/Mid-Push/Decent}. $$

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JMLR Journal 2016 Journal Article

Latent Space Inference of Internet-Scale Networks

Qirong Ho
Junming Yin
Eric P. Xing

The rise of Internet-scale networks, such as web graphs and social media with hundreds of millions to billions of nodes, presents new scientific opportunities, such as overlapping community detection to discover the structure of the Internet, or to analyze trends in online social behavior. However, many existing probabilistic network models are difficult or impossible to deploy at these massive scales. We propose a scalable approach for modeling and inferring latent spaces in Internet-scale networks, with an eye towards overlapping community detection as a key application. By applying a succinct representation of networks as a bag of triangular motifs, developing a parsimonious statistical model, deriving an efficient stochastic variational inference algorithm, and implementing it as a distributed cluster program via the Petuum parameter server system, we demonstrate overlapping community detection on real networks with up to 100 million nodes and 1000 communities on 5 machines in under 40 hours. Compared to other state-of-the-art probabilistic network approaches, our method is several orders of magnitude faster, with competitive or improved accuracy at overlapping community detection. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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UAI Conference 2016 Conference Paper

Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting

Pengtao Xie
Jin Kyu Kim
Yi Zhou 0017
Qirong Ho
Abhimanu Kumar
Yaoliang Yu
Eric P. Xing

Matrix-parametrized models (MPMs) are widely used in machine learning (ML) applications. In large-scale ML problems, the parameter matrix of a MPM can grow at an unexpected rate, resulting in high communication and parameter synchronization costs. To address this issue, we offer two contributions: first, we develop a computation model for a large family of MPMs, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i. e. the outer product of two “sufficient factors” (SFs). Second, we implement a decentralized, peer-to-peer system, Sufficient Factor Broadcasting (SFB), which broadcasts the SFs among worker machines, and reconstructs the update matrices locally at each worker. SFB takes advantage of small rank-1 matrix updates and efficient partial broadcasting strategies to dramatically improve communication efficiency. We propose a graph optimization based partial broadcasting scheme, which minimizes the delay of information dissemination under the constraint that each machine only communicates with a subset rather than all of machines. Furthermore, we provide theoretical analysis to show that SFB guarantees convergence of algorithms (under full broadcasting) without requiring a centralized synchronization mechanism. Experiments corroborate SFB’s efficiency on four MPMs.

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AAAI Conference 2015 Conference Paper

High-Performance Distributed ML at Scale through Parameter Server Consistency Models

Wei Dai
Abhimanu Kumar
Jinliang Wei
Qirong Ho
Garth Gibson
Eric Xing

As Machine Learning (ML) applications embrace greater data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Effective use of clusters for ML programs requires considerable expertise in writing distributed code, but existing highlyabstracted frameworks like Hadoop that pose low barriers to distributed-programming have not, in practice, matched the performance seen in highly specialized and advanced ML implementations. The recent Parameter Server (PS) paradigm is a middle ground between these extremes, allowing easy conversion of single-machine parallel ML programs into distributed ones, while maintaining high throughput through relaxed “consistency models” that allow asynchronous (and, hence, inconsistent) parameter reads. However, due to insufficient theoretical study, it is not clear which of these consistency models can really ensure correct ML algorithm output; at the same time, there remain many theoreticallymotivated but undiscovered opportunities to maximize computational throughput. Inspired by this challenge, we study both the theoretical guarantees and empirical behavior of iterative-convergent ML algorithms in existing PS consistency models. We then use the gleaned insights to improve a consistency model using an “eager” PS communication mechanism, and implement it as a new PS system that enables ML programs to reach their solution more quickly.

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ICML Conference 2015 Conference Paper

Large-scale Distributed Dependent Nonparametric Trees

Zhiting Hu
Qirong Ho
Avinava Dubey
Eric P. Xing

Practical applications of Bayesian nonparametric (BNP) models have been limited, due to their high computational complexity and poor scaling on large data. In this paper, we consider dependent nonparametric trees (DNTs), a powerful infinite model that captures time-evolving hierarchies, and develop a large-scale distributed training system. Our major contributions include: (1) an effective memoized variational inference for DNTs, with a novel birth-merge strategy for exploring the unbounded tree space; (2) a model-parallel scheme for concurrent tree growing/pruning and efficient model alignment, through conflict-free model partitioning and lightweight synchronization; (3) a data-parallel scheme for variational parameter updates that allows distributed processing of massive data. Using 64 cores in 36 hours, our system learns a 10K-node DNT topic model on 8M documents that captures both high-frequency and long-tail topics. Our data and model scales are orders-of-magnitude larger than recent results on the hierarchical Dirichlet process, and the near-linear scalability indicates great potential for even bigger problem sizes.

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NeurIPS Conference 2014 Conference Paper

Dependent nonparametric trees for dynamic hierarchical clustering

Kumar Avinava Dubey
Qirong Ho
Sinead Williamson
Eric Xing

Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to evolve with time. In this paper, we present a distribution over collections of time-dependent, infinite-dimensional trees that can be used to model evolving hierarchies, and present an efficient and scalable algorithm for performing approximate inference in such a model. We demonstrate the efficacy of our model and inference algorithm on both synthetic data and real-world document corpora.

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UAI Conference 2014 Conference Paper

Modeling Citation Networks Using Latent Random Offsets

Willie Neiswanger
Chong Wang 0002
Qirong Ho
Eric P. Xing

Out of the many potential factors that determine which links form in a document citation network, two in particular are of high importance: first, a document may be cited based on its subject matter—this can be modeled by analyzing document content; second, a document may be cited based on which other documents have previously cited it—this can be modeled by analyzing citation structure. Both factors are important for users to make informed decisions and choose appropriate citations as the network grows. In this paper, we present a novel model that integrates the merits of content and citation analyses into a single probabilistic framework. We demonstrate our model on three real-world citation networks. Compared with existing baselines, our model can be used to effectively explore a citation network and provide meaningful explanations for links while still maintaining competitive citation prediction performance.

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NeurIPS Conference 2014 Conference Paper

On Model Parallelization and Scheduling Strategies for Distributed Machine Learning

Seunghak Lee
Jin Kyu Kim
Xun Zheng
Qirong Ho
Garth Gibson
Eric Xing

Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness. A sibling problem that has received relatively less attention is how to ensure efficient and correct model parallel execution of ML algorithms, where parameters of an ML program are partitioned to different workers and undergone concurrent iterative updates. We argue that model and data parallelisms impose rather different challenges for system design, algorithmic adjustment, and theoretical analysis. In this paper, we develop a system for model-parallelism, STRADS, that provides a programming abstraction for scheduling parameter updates by discovering and leveraging changing structural properties of ML programs. STRADS enables a flexible tradeoff between scheduling efficiency and fidelity to intrinsic dependencies within the models, and improves memory efficiency of distributed ML. We demonstrate the efficacy of model-parallel algorithms implemented on STRADS versus popular implementations for topic modeling, matrix factorization, and Lasso.

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NeurIPS Conference 2013 Conference Paper

A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

Junming Yin
Qirong Ho
Eric Xing

We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction.

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NeurIPS Conference 2013 Conference Paper

More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server

Qirong Ho
James Cipar
Henggang Cui
Seunghak Lee
Jin Kyu Kim
Phillip B. Gibbons
Garth Gibson
Greg Ganger

We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees. The parameter server provides an easy-to-use shared interface for read/write access to an ML model's values (parameters and variables), and the SSP model allows distributed workers to read older, stale versions of these values from a local cache, instead of waiting to get them from a central storage. This significantly increases the proportion of time workers spend computing, as opposed to waiting. Furthermore, the SSP model ensures ML algorithm correctness by limiting the maximum age of the stale values. We provide a proof of correctness under SSP, as well as empirical results demonstrating that the SSP model achieves faster algorithm convergence on several different ML problems, compared to fully-synchronous and asynchronous schemes.

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NeurIPS Conference 2012 Conference Paper

On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks

Qirong Ho
Junming Yin
Eric Xing

In this paper, we argue for representing networks as a bag of {\it triangular motifs}, particularly for important network problems that current model-based approaches handle poorly due to computational bottlenecks incurred by using edge representations. Such approaches require both 1-edges and 0-edges (missing edges) to be provided as input, and as a consequence, approximate inference algorithms for these models usually require $\Omega(N^2)$ time per iteration, precluding their application to larger real-world networks. In contrast, triangular modeling requires less computation, while providing equivalent or better inference quality. A triangular motif is a vertex triple containing 2 or 3 edges, and the number of such motifs is $\Theta(\sum_{i}D_{i}^{2})$ (where $D_i$ is the degree of vertex $i$), which is much smaller than $N^2$ for low-maximum-degree networks. Using this representation, we develop a novel mixed-membership network model and approximate inference algorithm suitable for large networks with low max-degree. For networks with high maximum degree, the triangular motifs can be naturally subsampled in a {\it node-centric} fashion, allowing for much faster inference at a small cost in accuracy. Empirically, we demonstrate that our approach, when compared to that of an edge-based model, has faster runtime and improved accuracy for mixed-membership community detection. We conclude with a large-scale demonstration on an $N\approx 280, 000$-node network, which is infeasible for network models with $\Omega(N^2)$ inference cost.

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