
On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in...
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SelfSupervised Neural Architecture Search for Imbalanced Datasets
Neural Architecture Search (NAS) provides stateoftheart results when ...
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Regret minimization in stochastic nonconvex learning via a proximalgradient approach
Motivated by applications in machine learning and operations research, w...
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Random extrapolation for primaldual coordinate descent
We introduce a randomly extrapolated primaldual coordinate descent meth...
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Conditional gradient methods for stochastically constrained convex minimization
We propose two novel conditional gradientbased methods for solving stru...
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Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
We study the inverse reinforcement learning (IRL) problem under the tran...
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DoubleLoop Unadjusted Langevin Algorithm
A wellknown firstorder method for sampling from logconcave probabilit...
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Efficient Proximal Mapping of the 1pathnorm of Shallow Networks
We demonstrate two new important properties of the 1pathnorm of shallo...
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Interactionlimited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to...
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Environment Shaping in Reinforcement Learning using State Abstraction
One of the central challenges faced by a reinforcement learning (RL) age...
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On the Almost Sure Convergence of Stochastic Gradient Descent in NonConvex Problems
This paper analyzes the trajectories of stochastic gradient descent (SGD...
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The limits of minmax optimization algorithms: convergence to spurious noncritical sets
Compared to minimization problems, the minmax landscape in machine lear...
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Convergence of adaptive algorithms for weakly convex constrained optimization
We analyze the adaptive first order algorithm AMSGrad, for solving a con...
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Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
We introduce LiPopt, a polynomial optimization framework for computing i...
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A new regret analysis for Adamtype algorithms
In this paper, we focus on a theorypractice gap for Adam and its varian...
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A Newton FrankWolfe Method for Constrained SelfConcordant Minimization
We demonstrate how to scalably solve a class of constrained selfconcord...
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Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of tr...
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Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multiagent reinforcement learni...
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UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
We propose a novel adaptive, accelerated algorithm for the stochastic co...
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Nearly Minimal OverParametrization of Shallow Neural Networks
A recent line of work has shown that an overparametrized neural network ...
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Fast and Provable ADMM for Learning with Generative Priors
In this work, we propose a (linearized) Alternating Direction Methodof...
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Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the ad...
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On Certifying Nonuniform Bound against Adversarial Attacks
This work studies the robustness certification problem of neural network...
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Streaming LowRank Matrix Approximation with an Application to Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for...
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An OptimalStorage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storageoptimal algorithm that provably solves...
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Stochastic Conditional Gradient Method for Composite Convex Minimization
In this paper, we propose the first practical algorithm to minimize stoc...
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An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
Information theory plays an indispensable role in the development of alg...
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Efficient learning of smooth probability functions from Bernoulli tests with guarantees
We study the fundamental problem of learning an unknown, smooth probabil...
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Iterative Classroom Teaching
We consider the machine teaching problem in a classroomlike setting whe...
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Kernel Conjugate Gradient Methods with Random Projections
We propose and study kernel conjugate gradient methods (KCGM) with rando...
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Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimiza...
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Finding Mixed Nash Equilibria of Generative Adversarial Networks
We reconsider the training objective of Generative Adversarial Networks ...
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Online Adaptive Methods, Universality and Acceleration
We present a novel method for convex unconstrained optimization that, wi...
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LearningBased Compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of n...
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Optimal Rates of Sketchedregularized Algorithms for LeastSquares Regression over Hilbert Spaces
We investigate regularized algorithms combining with projection for leas...
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Mirrored Langevin Dynamics
We generalize the Langevin Dynamics through the mirror descent framework...
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Dimensionfree Information Concentration via ExpConcavity
Information concentration of probability measures have important implica...
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Robust Maximization of NonSubmodular Objectives
We study the problem of maximizing a monotone set function subject to a ...
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HighDimensional Bayesian Optimization via Additive Models with Overlapping Groups
Bayesian optimization (BO) is a popular technique for sequential blackb...
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Let's be honest: An optimal noregret framework for zerosum games
We revisit the problem of solving twoplayer zerosum games in the decen...
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Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and SpectralRegularization Algorithms
We study generalization properties of distributed algorithms in the sett...
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Optimal Rates for Spectralregularized Algorithms with LeastSquares Regression over Hilbert Spaces
In this paper, we study regression problems over a separable Hilbert spa...
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Smooth PrimalDual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
We propose a new randomized coordinate descent method for a convex optim...
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Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
We study the classical problem of maximizing a monotone submodular funct...
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Phase Transitions in the Pooled Data Problem
In this paper, we study the pooled data problem of identifying the label...
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Combinatorial Penalties: Which structures are preserved by convex relaxations?
We consider the homogeneous and the nonhomogeneous convex relaxations f...
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FixedRank Approximation of a PositiveSemidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite p...
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Robust Submodular Maximization: A NonUniform Partitioning Approach
We study the problem of maximizing a monotone submodular function subjec...
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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
In this paper, we consider the problem of sequentially optimizing a blac...
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Faster Coordinate Descent via Adaptive Importance Sampling
Coordinate descent methods employ random partial updates of decision var...
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Volkan Cevher
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Associate Professor at Ecole Polytechnique Federale de Lausanne