The Interplay Between Stability and Regret in Online Learning

  title={The Interplay Between Stability and Regret in Online Learning},
  author={Ankan Saha and Prateek Jain and Ambuj Tewari},
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 that the existence of an algorithm with bounded regret implies the existence of a stable algorithm with… CONTINUE READING
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  • Alexander Rakhlin
  • Lecture notes on online learning,
  • 2009
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