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Found 6 papers, 1 papers with code
Distribution-Free Guarantees for Systems with Decision-Dependent Noise
Our approach finds the open-loop control law that minimizes the worst-case loss, given that the noise induced by this control lies in its $(1 - p)$-confidence set for a predetermined $p$.
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting
Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains.
Approximate Regions of Attraction in Learning with Decision-Dependent Distributions
As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner.
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks
We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
