
Waitfree approximate agreement on graphs
Approximate agreement is one of the few variants of consensus that can b...
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Fast Graphical Population Protocols
Let G be a graph on n nodes. In the stochastic population protocol model...
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CommunicationEfficient Distributed Optimization with Quantized Preconditioners
We investigate fast and communicationefficient algorithms for the class...
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Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven th...
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ByzantineResilient NonConvex Stochastic Gradient Descent
We study adversaryresilient stochastic distributed optimization, in whi...
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Adaptive Gradient Quantization for DataParallel SGD
Many communicationefficient variants of SGD use gradient quantization s...
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Improved Communication Lower Bounds for Distributed Optimisation
Motivated by the interest in communicationefficient methods for distrib...
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The SplayList: A DistributionAdaptive Concurrent SkipList
The design and implementation of efficient concurrent data structures ha...
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Fast General Distributed Transactions with Opacity using Global Time
Transactions can simplify distributed applications by hiding data distri...
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Stochastic Gradient Langevin with Delayed Gradients
Stochastic Gradient Langevin Dynamics (SGLD) ensures strong guarantees w...
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Breaking (Global) Barriers in Parallel Stochastic Optimization with WaitAvoiding Group Averaging
Deep learning at scale is dominated by communication time. Distributing ...
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WoodFisher: Efficient secondorder approximations for model compression
Secondorder information, in the form of Hessian or InverseHessianvec...
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Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers
Several classic problems in graph processing and computational geometry ...
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Dynamic Averaging Load Balancing on Cycles
We consider the following dynamic loadbalancing process: given an under...
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Robust Comparison in Population Protocols
There has recently been a surge of interest in the computational and com...
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Relaxed Scheduling for Scalable Belief Propagation
The ability to leverage largescale hardware parallelism has been one of...
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On the Sample Complexity of Adversarial MultiSource PAC Learning
We study the problem of learning from multiple untrusted data sources, a...
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Distributed Mean Estimation with Optimal Error Bounds
Motivated by applications to distributed optimization and machine learni...
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Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent
Machine learning has made tremendous progress in recent years, with mode...
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Analysis and Evaluation of NonBlocking Interpolation Search Trees
We start by summarizing the recently proposed implementation of the firs...
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In Search of the Fastest Concurrent UnionFind Algorithm
UnionFind (or DisjointSet Union) is one of the fundamental problems in...
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PopSGD: Decentralized Stochastic Gradient Descent in the Population Model
The population model is a standard way to represent largescale decentra...
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Powerset Convolutional Neural Networks
We present a novel class of convolutional neural networks (CNNs) for set...
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Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations
Load imbalance pervasively exists in distributed deep learning training ...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Why ExtensionBased Proofs Fail
We prove that a class of fundamental shared memory tasks are not amenabl...
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Distributed Learning over Unreliable Networks
Most of today's distributed machine learning systems assume reliable ne...
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The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular d...
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Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms
There has been significant progress in understanding the parallelism inh...
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Distributionally Linearizable Data Structures
Relaxed concurrent data structures have become increasingly popular, due...
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The Transactional Conflict Problem
The transactional conflict problem arises in transactional systems whene...
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Byzantine Stochastic Gradient Descent
This paper studies the problem of distributed stochastic optimization in...
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The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine ...
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SparCML: HighPerformance Sparse Communication for Machine Learning
One of the main drivers behind the rapid recent advances in machine lear...
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Model compression via distillation and quantization
Deep neural networks (DNNs) continue to make significant advances, solvi...
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Compressive Sensing with Low Precision Data Representation: Radio Astronomy and Beyond
Modern scientific instruments produce vast amounts of data, which can ov...
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DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation
It is safe to assume that, for the foreseeable future, machine learning,...
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The ZipML Framework for Training Models with EndtoEnd Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
Recently there has been significant interest in training machinelearnin...
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Dan Alistarh
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