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small-gradient-skipping

topic1 events
papersSEP 10 04:00 UTC

Constant-regret algorithm for online inverse integer linear optimization proposed

Researchers address online inverse linear optimization, where a learner predicts weights, observes the agent's optimal action, and updates its estimate each round. Their new small-gradient skipping technique achieves constant regret and only a finite number of mistakes for integer linear optimization problems. This improves on prior bounds that left a logarithmic gap between upper and lower regret limits.

research/optimization/online-learning/theory