no code implementations • 28 May 2024 • Haipeng Luo, Spandan Senapati, Vatsal Sharan
We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over $K$ classes with low U-calibration error, that is, low regret with respect to all bounded proper losses simultaneously.
no code implementations • 18 Oct 2023 • Spandan Senapati, Rahul Vaze
In addition, we show that the competitive ratio of any online algorithm is $\max\{\Omega(L), \Omega(\frac{L}{\sqrt{\mu}})\}$ in the limited information setting when the switching cost is quadratic.
1 code implementation • 26 May 2023 • Aakash Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel
Specifically, they exhibit a superlinear rate with $O(d^2)$ cost in contrast to the linear rate of first-order methods with $O(d)$ cost and the quadratic rate of second-order methods with $O(d^3)$ cost.