BACKGROUND & OBJECTIVES It is not known if optimism influences regret following major reconstructive breast surgery. We examined the relationship between dispositional optimism, major complications and decision regret in patients undergoing microsurgical breast reconstruction. METHODS A consecutive series of 290 patients were surveyed. Independent variables were: (1) dispositional optimism an...

We present a general framework for analyzing regret in the online prediction problem. We develop this from sets of linear transformations of strategies. We establish relationships among the varieties of regret and present a class of regret-matching algorithms. Finally we consider algorithms that exhibit the asymptotic no-regret property. Our main results are an analysis of observed regret in ex...

Sensitive error correcting output codes are a reduction from cost sensitive classi cation to binary classi cation. They are a modi cation of error correcting output codes [3] which satisfy an additional property: regret for binary classi cation implies at most 2 l2 regret for cost-estimation. This has several implications: 1) Any 0/1 regret minimizing online algorithm is (via the reduction) a r...

This paper considers the stability of online learning algorithms and its implications for learnability (bounded regret). We introduce a novel quantity called forward regret that intuitively measures how good an online learning algorithm is if it is allowed a one-step look-ahead into the future. We show that given stability, bounded forward regret is equivalent to bounded regret. We also show th...

A general class of no-regret learning algorithms, called no-Φ-regret learning algorithms, is defined which spans the spectrum from no-external-regret learning to no-internal-regret learning and beyond. The set Φ describes the set of strategies to which the play of a given learning algorithm is compared. A learning algorithm satisfies no-Φ-regret if no regret is experienced for playing as the al...

We consider online content recommendation with implicit feedback through pairwise comparisons, formalized as the so-called dueling bandit problem. We study the dueling bandit problem in the Condorcet winner setting, and consider two notions of regret: the more well-studied strong regret, which is 0 only when both arms pulled are the Condorcet winner; and the less well-studied weak regret, which...