Algorithmic game theoryStackelberg equilibriaCongestion gamesComputational complexityBilevel programmingWe study the problem of computing Stackelberg equilibria in Stackelberg games whose underlying structure is a congestion game, focusing on singleton congestion games, i.e., on congestion games where each ...
Artificial Intelligence (AI) has emerged through the emulation of human intellectual processes by designing and implementing algorithms. This algorithmic approach has been pivotal in advancing AI technologies. It is essential to recognize that the journey of AI has been a collaborative endeavor, and it...
For example, Canada’s Algorithmic Impact Assessment provides a score based on qualitative questions such as “Are clients in this line of business particularly vulnerable? (yes or no).” What matters is the potential for harm, regardless of whether we're discussing an algebraic formula or a ...
such that the win-probability is a martingale diffusion, which of these processes has maximum entropy and hence gives the most excitement for the spectators?
Computer Science Algorithm Machine Learning (ML) Big Data Analytics Computational Neuroscience Augmented Intelligence AI Guardrail Related Reading “The Impact of Instant Translation is Profound”: Skrivanek’s Arturs Peha on How AI is Changing Language ...
Though, if you're reasonably worried about an attacker, there are many other algorithmic attack vectors to worry about besides amortization and average-case.) Both average-case and amortization are incredibly useful tools for thinking about and designing with scaling in mind. (See Difference bet...
Because of a well known link between algorithmic undecidability and logical undecidability (also known as logical independence), the main theorem also implies the existence of an (in principle explicitly describable) dimension , periodic subset of , and a finite subset of , such that the assertion...
techniques from Machine Learning to Algorithmic Game Theory, which has been a major area of research at the intersection of Computer Science and Economics... MF Balcan - Carnegie Mellon University. 被引量: 7发表: 2008年 Machine Learning and Behavioral Economics: Evaluating Models of Choice Under...
AI was revived in the 1980’s with the expansion of the algorithmic toolkit and more dedicated funds. John Hopefield and David Rumelhart introduced “deep learning” techniques that allowed computers to learn through experience. Edward Feigenbaum introduced “expert systems” that mimicked human decisio...
AI is extensively used in the finance industry for fraud detection, algorithmic trading, credit scoring, and risk assessment. Machine learning models can analyze vast amounts of financial data to identify patterns and make predictions. Healthcare AI applications in healthcare include disease diagnosis...