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Found 10 papers, 6 papers with code
Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling
Load balancing and auto scaling are at the core of scalable, contemporary systems, addressing dynamic resource allocation and service rate adjustments in response to workload changes.
A Practical Approach to Novel Class Discovery in Tabular Data
In particular, the number of novel classes is usually assumed to be known in advance, and their labels are sometimes used to tune hyperparameters.
An Interactive Interface for Novel Class Discovery in Tabular Data
This task is difficult and can often only be performed by a domain expert.
Online Learning with Adversaries: A Differential-Inclusion Analysis
Our main result is that the proposed algorithm almost surely converges to the desired mean $\mu.$ This makes ours the first asynchronous FL method to have an a. s. convergence guarantee in the presence of adversaries.
Novel Class Discovery: an Introduction and Key Concepts
We then give an overview of the different families of approaches, organized by the way they transfer knowledge from the labeled set to the unlabeled set.
Découvrir de nouvelles classes dans des données tabulaires
In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes.
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices
The application of Euclidean methods to integrate differential equations lying on such objects does not respect the geometry of the manifold, which can cause many numerical issues.
Online Multi-Agent Decentralized Byzantine-robust Gradient Estimation
In this paper, we propose an iterative scheme for distributed Byzantineresilient estimation of a gradient associated with a black-box model.
A Method for Discovering Novel Classes in Tabular Data
In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data.
Opinion shaping in social networks using reinforcement learning
One leads to a convex optimization problem and the other to a non-convex one.
