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Found 8 papers, 3 papers with code
Learning the Topology and Behavior of Discrete Dynamical Systems
On the positive side, we present efficient learning methods under the PAC model when the underlying graph of the dynamical system belongs to some classes.
Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems
Fixed points of such dynamical systems represent configurations to which the system converges.
Resource Sharing Through Multi-Round Matchings
For a general class of benefit functions satisfying certain properties (including diminishing returns), we show that this multi-round matching problem is efficiently solvable.
Explainable Clustering via Exemplars: Complexity and Efficient Approximation Algorithms
Explainable AI (XAI) is an important developing area but remains relatively understudied for clustering.
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness
Existing work on fairness typically focuses on making known machine learning algorithms fairer.
Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise
Our focus is on resource allocation problems where agents may have restrictions that make them incompatible with some resources.
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability
Improving the explainability of the results from machine learning methods has become an important research goal.
A Graph-Based Approach for Active Learning in Regression
Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query.
