A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified framework for decentralized optimization combining gradient tracking and variance reduction.
problem Solving finite-sum minimization problems in distributed systems with privacy and resource constraints.
method Unified algorithmic framework combining variance-reduction and gradient tracking.
result Unified methods achieve robust performance and fast convergence for smooth and strongly-convex objectives, and are applicable to non-convex problems.
Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartp…
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locatio…
Decentralized solutions to finite-sum minimization are of significant importance in many signal processing, control, and machine learning applications. In such settings, the data is distributed over a network of arbitrarily-connected nodes and raw data sharing is prohibitive often due to communication or privacy constr…
problem Efficiently learning posterior distributions in a decentralized setting.
method Decentralized Langevin dynamics in a non-convex setting.
result The algorithm converges to the target posterior distribution with exponential decrease in KL-divergence and polynomial decrease in error contributions.
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, …
Improves decentralized learning by optimizing graph mixing for data heterogeneity.
problem Data heterogeneity impacts convergence in decentralized learning, but existing methods ignore this.
method Characterized and quantified the relationship between graph mixing and data heterogeneity. Proposed an optimization approach to improve convergence.
result Our approach leads to improved test performance across various tasks.
Paper proposes a transfer learning approach for decentralized QoE estimation.
problem Challenges in QoE model development due to small datasets, user diversity, and IPR/privacy concerns.
method A transfer learning-based ML model training approach that allows decentralized local models to share generic indicators and customize them further.
result The approach shows advantages of stacking various generic and specific models with corresponding weight factors.
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In r…
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems. This paper studies the decentralized Markov chain gradient descent (DMGD) algorithm - a variant of the decentralized stochastic gradient methods where the random samples are taken along the trajec…
Decentralized optimization algorithms have attracted intensive interests recently, as it has a balanced communication pattern, especially when solving large-scale machine learning problems. Stochastic Path Integrated Differential Estimator Stochastic First-Order method (SPIDER-SFO) nearly achieves the algorithmic lower…
Decentralized mechanism for collective predictions without sharing data or models.
problem Making predictions jointly among multiple parties without sharing data or models.
method Inspired by social science consensus-making, a decentralized mechanism for test-time collective predictions.
result Our mechanism converges to inverse meansquared-error weighting in the large-sample limit and achieves significant gains over classical model averaging.
AdaSDBO solves decentralized bilevel optimization without problem parameters, achieving competitive performance.
problem Decentralized bilevel optimization problems without known parameters.
method AdaSDBO, a fully problem-parameter-free algorithm with adaptive stepsizes.
result AdaSDBO achieves a convergence rate of $\widetilde{\mathcal{O}}\left(\frac{1}{T}
ight)$, matching state-of-the-art methods up to polylogarithmic factors.
DE-SGD shows heavy-tailed behavior in decentralized settings.
problem Heavy-tailed behavior in decentralized SGD.
method Analyzes the emergence of heavy-tails in DE-SGD, considering both quadratic and twice continuously differentiable strongly convex loss functions.
result DE-SGD exhibits heavier tails than centralized SGD, and tail behavior depends on network parameters.
While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals. As sensitive information may be contained in the individual's dataset, sharing training data may lead to severe privacy conc…
Many applications of machine learning, such as human health research, involve processing private or sensitive information. Privacy concerns may impose significant hurdles to collaboration in scenarios where there are multiple sites holding data and the goal is to estimate properties jointly across all datasets. Differe…
Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are built in a centralized fashion. One bottleneck of centralized algorithms lies on high communication cost on the central node. Motivated by this, we ask, can decentralized algorithms be faster than its centralized counterpart? Althoug…