In this work we consider the problem of regret minimization for logistic bandits. The main challenge bandits is reducing dependence on a potentially large dependent constant that can at worst scale exponentially with norm unknown parameter vector. Previous works have applied self-concordance function to remove worst-case providing guarantees move reduce case lower order terms only polylogarithm...

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both inherent drawbacks, i.e., the user either has provide specific utility function or faces many results. The k-regret minimization query proposed, which integrates merits of queri...

Now-a-days, billboard advertisement has emerged as an effective outdoor technique. In this case, a commercial house approaches influence provider for specific number of views their content on payment basis. If the can satisfy then they will receive full else partial payment. provides more or less than demand certainly is loss to them. This formalized ‘Regret’ and goal be minimize ‘Regret’. pape...

We consider decision-making problems in Markov decision processes where both the rewards and the transition probabilities vary in an arbitrary (e.g., nonstationary) fashion to some extent. We propose online learning algorithms and provide guarantees on their performance evaluated in retrospect against stationary policies. Unlike previous works, the guarantees depend critically on the variabilit...

The counterfactual regret minimization (CFR) algorithm is state-of-the-art for computing strategies in large games and other sequential decisionmaking problems. Little is known, however, about CFR in games with more than 2 players. This extended abstract outlines research towards a better understanding of CFR in multiplayer games and new procedures for computing even stronger multiplayer strate...

Regret minimization is widely used in determining strategies for imperfect-information games and in online learning. In large games, computing the regrets associated with a single iteration can be slow. For this reason, pruning – in which parts of the decision tree are not traversed in every iteration – has emerged as an essential method for speeding up iterations in large games. The ability to...

Many situations involve repeatedly making decisions in an uncertain environment: for instance, deciding what route to drive to work each day, or repeated play of a game against an opponent with an unknown strategy. In this chapter we describe learning algorithms with strong guarantees for settings of this type, along with connections to game-theoretic equilibria when all players in a system are...

Many real world security problems can be modelled as finite zero-sum games with structured sequential strategies and limited interactions between the players. An abstract class of games unifying these models are the normal-form games with sequential strategies (NFGSS). We show that all games from this class can be modelled as well-formed imperfect-recall extensiveform games and consequently can...

Extensive games are a powerful model of multiagent decision-making scenarioswith incomplete information. Finding a Nash equilibrium for very large instancesof these games has received a great deal of recent attention. In this paper, wedescribe a new technique for solving large games based on regret minimization.In particular, we introduce the notion of counterfactual regret, whi...

Approachability theory, introduced by Blackwell (1956), provides fundamental results on repeated games with vector-valued payoffs, and has been usefully applied since in the theory of learning in games and to learning algorithms in the online adversarial setup. Given a repeated game with vector payoffs, a target set S is approachable by a certain player (the agent) if he can ensure that the ave...