62 citations · 424 across the 139 of their papers we have counts for
15 papers · 1 filter
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland, Ali Kavis, Alex Immer +2
We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this pro…
Iterative Classroom Teaching
Teresa Yeo, Parameswaran Kamalaruban, Adish Singla +5
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from…
Adversarially Robust Optimization with Gaussian Processes
Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka +1
In this paper, we consider the problem of Gaussian process (GP) optimization with an added robustness requirement: The returned point may be perturbed by an adversary, and we requi…
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Ya-Ping Hsieh, Chen Liu, Volkan Cevher
We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a…
Online Adaptive Methods, Universality and Acceleration
Kfir Y. Levy, Alp Yurtsever, Volkan Cevher
We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard conv…
An Adaptive Primal-Dual Framework for Nonsmooth Convex Minimization
Quoc Tran-Dinh, Ahmet Alacaoglu, Olivier Fercoq +1
We propose a new self-adaptive, double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov's sm…