IDEAS

Zoom link: https://upenn.zoom.us/j/98220304722   Abstract I will present recent work by my research group on the design and analysis of stochastic-gradient-based algorithms for solving nonconvex constrained optimization problems, which may arise, for example, in informed supervised learning.  I will focus in particular on algorithmic strategies that have consistently been shown to exhibit the best practical […]

We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along the sequence converges to zero. In general, this condition is not implied by, nor implies, sublinear regret. It turns out that gradient equilibrium is achievable […]

Zoom link: https://upenn.zoom.us/j/98220304722

Zoom link: https://upenn.zoom.us/j/98220304722