Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Publication
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This work proposes a universal and adaptive second-order method for minimizing second-order smooth, convex functions. Our algorithm achieves O(σ/T‾‾√) convergence when the oracle feedback is stochastic with variance σ2, and improves its convergence to O(1/T3) with deterministic oracles, where T is the number of iterations. Our method also interpolates these rates without knowing the nature of the oracle apriori, which is enabled by a parameter-free adaptive step-size that is oblivious to the knowledge of smoothness modulus, variance bounds and the diameter of the constrained set. To our knowledge, this is the first universal algorithm with such global guarantees within the second-order optimization literature.