Adam Tauman Kalai

TL;DR: This work model this information-theoretically using the following question: what type of compression scheme would be efiective when the encoder and decoder have (boundedly) difierent prior probability distributions and uses information theory to justify why ambiguity is necessary for the purpose of compression.

TL;DR: This paper shows that for the case of lists, one can achieve a 1 + e ratio with respect to the best static list in hindsight, by a simple efficient algorithm, and shows a (computationally inefficient) algorithm that achieves "dynamic search optimality": dynamic optimality if the online algorithm to make free rotations after each request.

TL;DR: In this article, the authors study a dynamic learning setting where individuals invest in a positive outcome based on their group's expected gain and the decision rule is updated to maximize institutional benefit.

TL;DR: A simple model of a game with additional costs for each strategy is formed, proving a counter-intuitive generalization of the classic min-max theorem to zero-sum games with the addition of strategy costs and showing that potential games with strategy costs remain potential games.

TL;DR: This work proposes a method for discouraging correlation between the predicted probability of an individual’s true occupation and a word embedding of their name, which leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes.