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Found 11 papers, 1 papers with code
Venture Capital Portfolio Construction and the Main Factors Impacting the Optimal Strategy
The optimal portfolio size for a venture capital (VC) fund is a topic often debated, but there is no consensus on the best strategy.
Intrinsic uncertainties and where to find them
We introduce a framework for uncertainty estimation that both describes and extends many existing methods.
Data efficiency in graph networks through equivariance
We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes.
Symmetry-driven graph neural networks
Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more efficiency.
Beyond permutation equivariance in graph networks
In this draft paper, we introduce a novel architecture for graph networks which is equivariant to the Euclidean group in $n$-dimensions.
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization
This paper deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i. e., by means of local computation and communication, without any central coordinator.
Distributed Personalized Gradient Tracking with Convex Parametric Models
We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user.
Collective Learning
In this paper, we introduce the concept of collective learning (CL) which exploits the notion of collective intelligence in the field of distributed semi-supervised learning.
Asynchronous Distributed Learning from Constraints
In this paper, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied.
Distributed Submodular Minimization via Block-Wise Updates and Communications
In this \emph{distributed} set-up, in order to preserve their own privacy, agents communicate with neighbors but do not share their local cost functions.
Asynchronous Distributed Method of Multipliers for Constrained Nonconvex Optimization
We show that this distributed algorithm is equivalent to a block coordinate descent algorithm for the minimization of the Augmented Lagrangian followed by an update of the whole multiplier vector.
Optimization and Control
