1.3k citations · 3.8k across the 222 of their papers we have counts for
19 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…
Kernel Conjugate Gradient Methods with Random Projections
Junhong Lin, Volkan Cevher
We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space. Considering two types of random p…
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…
On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher +2
While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on t…
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…
A General Convergence Result for Mirror Descent with Armijo Line Search
Yen-Huan Li, Carlos A. Riofrio, Volkan Cevher
Existing convergence guarantees for the mirror descent algorithm require the objective function to have a bounded gradient or be smooth relative to a Legendre function. The bounded…
Learning-Based Compressive MRI
Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li +4
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling pat…
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Alp Yurtsever, Olivier Fercoq, Francesco Locatello +1
We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines smoothing and homotopy techniques under the…
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Paul Rolland, Jonathan Scarlett, Ilija Bogunovic +1
Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring,…
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Junhong Lin, Volkan Cevher
We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kern…
Dimension-free Information Concentration via Exp-Concavity
Ya-Ping Hsieh, Volkan Cevher
Information concentration of probability measures have important implications in learning theory. Recently, it is discovered that the information content of a log-concave distribut…
Mirrored Langevin Dynamics
Ya-Ping Hsieh, Ali Kavis, Paul Rolland +1
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unif…
Robust Maximization of Non-Submodular Objectives
Ilija Bogunovic, Junyao Zhao, Volkan Cevher
We study the problem of maximizing a monotone set function subject to a cardinality constraint in the setting where some number of elements is deleted from the returned set…
Let's be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin +1
We revisit the problem of solving two-player zero-sum games in the decentralized setting. We propose a simple algorithmic framework that simultaneously achieves the best rates for…
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Junhong Lin, Volkan Cevher
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distrib…
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
Junhong Lin, Alessandro Rudi, Lorenzo Rosasco +1
In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We inve…