New Fourier transform method handles missing data and asynchronous observations.
problem Volatility inference with missing or asynchronous data.
method Spectral framework using Fourier transforms.
result Consistent volatility functional estimation with limit distributions.
Ringleader ASGD optimizes SGD for diverse edge devices with varying data and computation speeds.
problem Scalable distributed optimization with heterogeneous devices and data.
method Ringleader ASGD, an asynchronous SGD algorithm.
result Achieves optimal time complexity under data heterogeneity and arbitrary computation speeds.
PARyOpt optimizes functions asynchronously, reducing wall clock time.
problem Efficiently optimizing functions on distributed systems with asynchronous evaluations.
method Parallel asynchronous Bayesian optimization.
result Reduces total optimization time for various test problems.
Asynchronous method for hyperparameter and neural architecture search.
problem Efficiently searching for optimal hyperparameters and neural architectures.
method Model-based, asynchronous multi-fidelity method combining Hyperband and Gaussian process-based Bayesian optimization.
result Substantial speed-ups over current state-of-the-art methods on various benchmarks.
FedBuff improves federated learning scalability with asynchronous updates.
problem Limited scalability of federated learning with synchronous updates.
method Introduces asynchronous updates (staleness) in federated learning.
result Theoretical analysis shows improved convergence rate with boundedness removed.
Asynchronous framework speeds up model-based RL to real-time.
problem Real-time learning on real robots with model-based RL.
method Asynchronous framework for model-based reinforcement learning.
result Reduced run time to data collection time, improved sample complexity.
Unified analysis of asynchronous-SGD algorithms for distributed learning.
problem Analyzing asynchronous-SGD in heterogeneous settings with varying speeds and data distributions.
method Unified convergence theory for non-convex smooth functions, including pure asynchronous SGD and its modifications.
result Unified convergence rates for various asynchronous algorithms, including novel methods.
Improved SAR in asynchronous conversations using neural models and unlabeled data.
problem Lack of labeled data for SAR in asynchronous conversations.
method Hierarchical LSTM-CRF model, semi-supervised learning with word embeddings, adversarial training.
result Adversarial training improves SAR performance by leveraging labeled data from synchronous domains.
Asynchronous federated modeling improves spatial data sharing without centralizing raw data.
problem Privacy and bandwidth constraints in distributed spatial data.
method Asynchronous federated modeling using low-rank Gaussian process approximations with block-wise optimization and adaptive strategies.
result Asynchronous federated modeling achieves synchronous performance and outperforms it in heterogeneous settings.
Asynchronous federated learning for vertically partitioned data improves efficiency and privacy.
problem Efficiently train models on vertically partitioned data without a trusted third party.
method Proposed AFSGD-VP and its SVRG and SAGA variants for asynchronous federated learning.
result AFSGD-VP and its variants achieve higher efficiency than synchronous algorithms.
New rules found to maintain neural network performance in asynchronous training.
problem Asynchronous training leads to degradation in generalization.
method Examined dynamical stability, derived rules for learning rate adjustment.
result Learning rate should be inversely proportional to delay for high delay values.
Asynchronous SGD generalizes well with enough data, improving stability and reducing error.
problem Generalization performance of asynchronous distributed SGD systems.
method Algorithm stability framework, adaptive learning rate strategy.
result Distributed asynchronous SGD generalizes well with enough data samples.
A new model for analyzing noisy, asynchronous high-frequency data.
problem Challenges in analyzing intraday correlations due to market microstructure noise and asynchronicity.
method Score-driven conditional correlation model using multivariate local-level model with score-driven covariance matrices.
result Market microstructure noise is effectively accounted for, leading to more accurate correlation estimates.
New asynchronous SGD algorithms achieve optimal performance in distributed learning.
problem Asynchronous training introduces staleness, complicating optimization analysis.
method Developed rigorous framework for asynchronous first order stochastic optimization.
result Asynchronous SGD can achieve optimal time complexity, matching synchronous methods.
Secure asynchronous federated learning with differential privacy for edge intelligence.
problem Privacy concerns in federated learning for edge computing.
method Differential privacy applied to secure asynchronous federated learning.
result MAPA improves model accuracy and convergence speed with sufficient privacy.
New algorithm reduces distributed optimization time with stochastic delays.
problem Optimizing distributed data with stochastic delays.
method Developed ADSAGA, a variant of SAGA for distributed-data settings with stochastic delays.
result ADSAGA converges in $ ilde{O}\left(\left(n + \sqrt{m}κ
ight)\log(1/ε)
ight)$ iterations under mean delay m m m . The paper improves Q-learning by incorporating pessimism for better sample efficiency.
problem Improving sample efficiency in asynchronous Q-learning with non-i.i.d. data.
method Developed an algorithmic framework that incorporates the principle of pessimism into asynchronous Q-learning, penalizing infrequently-visited state-action pairs based on suitable lower confidence bounds (LCBs).
result Achieved near-optimal sample complexity, providing theoretical support for the use of pessimism in non-i.i.d. data.
LSAM optimizes deep learning training with improved efficiency.
problem Inefficiency in distributed large-batch training with Sharpness-Aware Minimization (SAM).
method Integrates SAM's adversarial steps with an asynchronous distributed sampling strategy.
result Higher final accuracy compared to data-parallel SAM.
Triadic-OCD detects changes in data streams robustly and optimally, even in asynchronous settings.
problem Online change detection in data streams with practical constraints.
method Triadic-OCD framework for asynchronous online change detection with provable robustness, optimality, and convergence.
result The proposed triadic-OCD algorithm achieves optimal performance and convergence in asynchronous settings.
A new estimator for asynchronous tick data shows better correlation estimates.
problem Estimating correlation from asynchronous tick data.
method Derive a minimum-variance estimator and a fast linear-time estimator.
result The fast tickwise estimator has smaller estimation errors than the usual method.
Asynchronous SVI scales up Bayesian computation for massive data.
problem Scaling stochastic variational inference to massive data.
method Lock-free asynchronous parallel implementation of SVI.
result Linear speed-up and asymptotic convergence rate O ( 1 / ( T ) ) O(1/\sqrt(T)) O ( 1/ ( T )) . Ringmaster ASGD improves Asynchronous SGD's efficiency under varying worker times.
problem Suboptimal performance of Asynchronous SGD under heterogeneous worker computation times.
method Ringmaster ASGD, a novel Asynchronous SGD method with optimal time complexity.
result Ringmaster ASGD achieves optimal time complexity under arbitrary worker heterogeneity.
Improves BO efficiency by allowing asynchronous parallel computing.
problem Wasteful use of resources in batch Bayesian optimisation.
method Developed PLAyBOOK for asynchronous local penalisation.
result Asynchronous BO often outperforms synchronous BO.
New method calibrates asynchronous, error-prone covariates for longitudinal data.
problem Estimation biases and slow convergence in analyzing time-varying covariates with measurement error.
method Functional calibration approach based on functional principal component analysis.
result Asymptotically unbiased and consistent estimators for time-invariant coefficients; optimal convergence rate for time-varying coefficients.
Paper introduces an asynchronous optimization algorithm for parameter servers.
problem Solving optimization problems with asynchrony and general regularizers.
method Asynchronous incremental aggregated gradient algorithm in a parameter server framework.
result Established linear convergence rate and step-size choices for strong convex data loss.
AD-PSGD is an asynchronous decentralized parallel SGD that converges as fast as AllReduce-SGD but is much faster in a heterogeneous environment.
problem Designing an efficient and robust asynchronous decentralized parallel SGD algorithm.
method Proposes AD-PSGD, an asynchronous decentralized parallel SGD algorithm.
result AD-PSGD converges at the optimal O ( 1 / K ) O(1/\sqrt{K}) O ( 1/ K ) rate and has linear speedup w.r.t. number of workers. Zeno++ improves robustness of asynchronous SGD in fully asynchronous settings.
problem Byzantine failures in fully asynchronous SGD.
method Estimates descent of loss after applying candidate gradient.
result Proves convergence for non-convex problems under Byzantine failures.
Novel optimizer mitigates instability in asynchronous data parallel optimization.
problem Instability in asynchronous data parallel optimization.
method Pushes a normalized sequence of first-order gradients to a parameter server.
result Achieves better convergence rate compared to other optimizers.
Asynchronous algorithms reduce privacy costs in distributed machine learning.
problem Privacy concerns in training machine learning models on scattered private data.
method Differentially-private asynchronous algorithms for collaborative training.
result Cost of privacy is inversely proportional to dataset size and privacy budgets.
The paper proposes a method to model financial data asynchronously using copulas.
problem Modeling intraday financial returns of multiple assets due to asynchronous data.
method Proposes a consistent estimator of the correlation coefficient for Elliptical copulas and an improved estimator for non-elliptical copulas.
result The proposed estimator reduces bias in estimating copula parameters for a general class of copulas.
A new asynchronous method for vertical federated learning improves privacy and efficiency.
problem Solving vertical federated learning in an asynchronous manner with privacy and efficiency.
method A simple FL method that allows clients to run stochastic gradient algorithms asynchronously with a new perturbed local embedding technique.
result The method improves privacy and communication efficiency compared to centralized and synchronous FL methods.
Freya PAGE optimizes nonconvex optimization with heterogeneous, asynchronous workers.
problem Optimizing nonconvex finite-sum problems with varying worker processing times.
method Freya PAGE, a parallel method robust to stragglers and adaptive to slow computations.
result Freya PAGE offers improved time complexity guarantees compared to previous methods.
New framework simplifies analysis of asynchronous parallel optimization algorithms.
problem Difficulty in analyzing asynchronous parallel optimization algorithms due to delay and inconsistency.
method Revisit and clarify technical issue in convergence rate proofs, propose simplified 'perturbed iterate' framework.
result Proves linear speedup for ASAGA and KROMAGNON on multi-core systems without sparsity assumptions.
New method stabilizes asynchronous SGD by maintaining energy below that of synchronous SGD.
problem Stability issues in distributed asynchronous gradient descent.
method Gradient Energy Matching (GEM) using Lagrangian mechanics.
result GEM ensures stability and speedup in asynchronous SGD, even with many workers.
AEGiS optimizes expensive function evaluations asynchronously.
problem Optimizing expensive black-box functions efficiently.
method Asynchronous ε-Greedy Bayesian Optimisation combining greedy search and Thompson sampling.
result AEGiS outperforms existing asynchronous BO methods.
Secure aggregation for buffered asynchronous federated learning without TEEs.
problem Privacy and convergence in buffered asynchronous federated learning.
method Developed a new protocol (BASecAgg) that ensures privacy without TEEs by carefully designing masks.
result BASecAgg achieves similar convergence guarantees as FedBuff without TEEs.
AE-DDPG improves DDPG for continuous control in complex environments with less data and time.
problem Data insufficiency and training inefficiency in DDPG for complex environments.
method Asynchronous episodic data collection, modified experience replay, and new action noise.
result AE-DDPG achieves higher rewards and less training time in complex environments.
Standard acquisition functions are sufficient for asynchronous Bayesian optimization.
problem Redundant and repeated queries in asynchronous Bayesian optimization.
method Conceptual analysis and theoretical guarantees of standard acquisitions.
result Standard acquisition functions achieve theoretical guarantees equivalent to Thompson sampling in asynchronous settings.
Paper analyzes convergence and speedup of asynchronous parallel SGD.
problem Achieving good convergence and linear speedup in asynchronous parallel SGD.
method Second-order convergence analysis of APSGD with consistent read near strictly saddle points.
result Theoretical guarantee for using at most O ( K 1 / 3 M − 1 / 3 ) O(K^{1/3}M^{-1/3}) O ( K 1/3 M − 1/3 ) workers for good convergence and linear speedup. Asynchronous framework improves distributed learning performance.
problem Heterogeneous computing machines hinder synchronous learning strategies.
method Asynchronous distributed framework with parameter exchanges.
result Convergence of consistency in distributed asynchronous methods for gradient iterations.
This work tackles resource allocation in asynchronous and stochastic systems.
problem Distributed resource allocation in asynchronous and stochastic settings.
method Approximate stochastic primal-dual approach with asynchronous updates.
result The Asynchronous stochastic Primal-Dual (Asyn-PD) algorithm converges to the saddle point solution at a rate of O ( 1 / t ) O(1/t) O ( 1/ t ) . Advances in asynchronous optimization methods for machine learning.
problem Efficiently solving large-scale optimization problems in machine learning.
method Asynchronous parallel and distributed optimization methods, accounting for information delays.
result Degree of asynchrony impacts convergence rates in stochastic optimization methods.
DANA mitigates gradient staleness in asynchronous distributed SGD with momentum.
problem Gradient staleness in asynchronous distributed SGD with momentum.
method DANA: a novel technique for asynchronous distributed SGD with momentum that computes the gradient on an estimated future position of the model's parameters.
result DANA fully incorporates momentum in asynchronous training with almost no ramifications to final accuracy.
POAP and pySOT improve surrogate optimization of expensive functions.
problem Optimizing expensive functions with concurrent evaluations.
method Event-driven asynchronous framework for optimization strategies.
result Asynchronous computation offers significant speed-up advantages.
DSCOVR improves distributed optimization for big data with less communication and synchronization.
problem Efficiently optimizing large linear models with convex loss functions over distributed systems.
method Randomized primal-dual block coordinate algorithms with doubly stochastic coordinate optimization and variance reduction.
result DSCOVR algorithms require less overall computation and communication compared to other first-order distributed algorithms.
New algorithm reduces regret in asynchronous multiplayer bandits to constant or logarithmic levels.
problem Asynchronous multiplayer bandits in cognitive radio networks.
method Cautious Greedy algorithm with O ( T log ( T ) ) \mathcal{O}(\sqrt{T\log(T)}) O ( T log ( T ) ) minimax regret. result Cautious Greedy yields constant instance-dependent regret under certain conditions.
Sparsification improves convergence in asynchronous distributed SGD, even in the presence of staleness.
problem Staleness in asynchronous distributed SGD.
method Applied sparsification to reduce communication overheads in distributed asynchronous settings.
result The ergodic convergence rate of sparsified asynchronous SGD matches that of vanilla SGD, $\mathcal{O} \left( 1/\sqrt{T}
ight)$ , even in the presence of staleness.
The asymptotic pseudo-trajectory approach to stochastic approximation of Benaim, Hofbauer and Sorin is extended for asynchronous stochastic approximations with a set-valued mean field. The asynchronicity of the process is incorporated into the mean field to produce convergence results which remain similar to those of a…