Information Velocity of Cascaded Gaussian Channels With Feedback
Long-Term Fairness in Sequential Multi-Agent Selection with Positive Reinforcement
While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing sequential decision-making to positively impact long-term social fairness. In selection processes such as college admissions or hiring, biasing slightly towards applicants from under-represented groups is hypothesized to provide positive feedback that increases the pool of under-represented applicants in future selection rounds, thus enhancing fairness in the long term.
Controlled privacy leakage propagation throughout overlapping grouped learning
Neural Distributed Source Coding
Exploring the Symbiotic Relationship Between Information Theory and Machine Learning
Title: Exploring the Symbiotic Relationship Between Information Theory and Machine Learning
In the vast realm of artificial intelligence, two pillars stand prominently: Information Theory and Machine Learning. At first glance, they might seem like distinct fields with little in common, but upon closer inspection, their connection runs deep, forming a symbiotic relationship that underpins many modern AI advancements.