24 citations · 75 across the 7 of their papers we have counts for
7 papers
Tight Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
Blair Bilodeau, Dylan J. Foster, Daniel M. Roy
We consider the classical problem of sequential probability assignment under logarithmic loss while competing against an arbitrary, potentially nonparametric class of experts. We o…
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
Yossi Arjevani, Yair Carmon, John C. Duchi +3
We design an algorithm which finds an -approximate stationary point (with ) using stochastic gradient and Hessian-vector products, matching gua…
Open Problem: Model Selection for Contextual Bandits
Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees a…
Learning nonlinear dynamical systems from a single trajectory
Dylan J. Foster, Alexander Rakhlin, Tuhin Sarkar
We introduce algorithms for learning nonlinear dynamical systems of the form , where is a weight matrix, is a nonlinear link…
Vector Contraction for Rademacher Complexity
Dylan J. Foster, Alexander Rakhlin
We show that the Rademacher complexity of any -valued function class composed with an -Lipschitz function is bounded by the maximum Rademacher comple…
Distributed Learning with Sublinear Communication
Jayadev Acharya, Christopher De Sa, Dylan J. Foster +1
In distributed statistical learning, samples are split across machines and a learner wishes to use minimal communication to learn as well as if the examples were on a singl…