
On Certifying Nonuniform Bound against Adversarial Attacks
This work studies the robustness certification problem of neural network...
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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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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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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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Sketchy Decisions: Convex LowRank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization pr...
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Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and LevelSet Estimation
We present a new algorithm, truncated variance reduction (TruVaR), that ...
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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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Randomized singleview algorithms for lowrank matrix approximation
This paper develops a suite of algorithms for constructing lowrank appr...
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Lower Bounds on Active Learning for Graphical Model Selection
We consider the problem of estimating the underlying graph associated wi...
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Convex blocksparse linear regression with expanders  provably
Sparse matrices are favorable objects in machine learning and optimizati...
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A singlephase, proximal pathfollowing framework
We propose a new proximal, pathfollowing framework for a class of const...
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On the Difficulty of Selecting Ising Models with Approximate Recovery
In this paper, we consider the problem of estimating the underlying grap...
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Partial Recovery Bounds for the Sparse Stochastic Block Model
In this paper, we study the informationtheoretic limits of community de...
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Learning Data Triage: Linear Decoding Works for Compressive MRI
The standard approach to compressive sampling considers recovering an un...
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TimeVarying Gaussian Process Bandit Optimization
We consider the sequential Bayesian optimization problem with bandit fee...
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Learningbased Compressive Subsampling
The problem of recovering a structured signal x∈C^p from a set of dimens...
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Composite convex minimization involving selfconcordantlike cost functions
The selfconcordantlike property of a smooth convex function is a new a...
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Limits on Support Recovery with Probabilistic Models: An InformationTheoretic Framework
The support recovery problem consists of determining a sparse subset of ...
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A totally unimodular view of structured sparsity
This paper describes a simple framework for structured sparse recovery b...
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Convex Optimization for Big Data
This article reviews recent advances in convex optimization algorithms f...
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A PrimalDual Algorithmic Framework for Constrained Convex Minimization
We present a primaldual algorithmic framework to obtain approximate sol...
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Scalable sparse covariance estimation via selfconcordance
We consider the class of convex minimization problems, composed of a sel...
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Learning NonParametric Basis Independent Models from Point Queries via LowRank Methods
We consider the problem of learning multiridge functions of the form f(...
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Composite SelfConcordant Minimization
We propose a variable metric framework for minimizing the sum of a self...
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GroupSparse Model Selection: Hardness and Relaxations
Groupbased sparsity models are proven instrumental in linear regression...
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A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions
We propose an algorithmic framework for convex minimization problems of ...
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AdaptiveRate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
We propose and analyze an online algorithm for reconstructing a sequence...
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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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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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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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HighDimensional Bayesian Optimization via Additive Models with Overlapping Groups
Bayesian optimization (BO) is a popular technique for sequential blackb...
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Mirrored Langevin Dynamics
We generalize the Langevin Dynamics through the mirror descent framework...
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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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Optimal Rates of Sketchedregularized Algorithms for LeastSquares Regression over Hilbert Spaces
We investigate regularized algorithms combining with projection for leas...
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Dimensionfree Information Concentration via ExpConcavity
Information concentration of probability measures have important implica...
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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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LearningBased Compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of n...
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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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Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimiza...
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Iterative Classroom Teaching
We consider the machine teaching problem in a classroomlike setting whe...
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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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Kernel Conjugate Gradient Methods with Random Projections
We propose and study kernel conjugate gradient methods (KCGM) with rando...
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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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Finding Mixed Nash Equilibria of Generative Adversarial Networks
We reconsider the training objective of Generative Adversarial Networks ...
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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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Volkan Cevher
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Associate Professor at Ecole Polytechnique Federale de Lausanne