
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
It is well known that modern deep neural networks are powerful enough to...
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Submodular + Concave
It has been well established that first order optimization methods can c...
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Parallelizing Thompson Sampling
How can we make use of information parallelism in online decision making...
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Learning and Certification under Instancetargeted Poisoning
In this paper, we study PAC learnability and certification under instanc...
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The Power of Subsampling in Submodular Maximization
We propose subsampling as a unified algorithmic technique for submodular...
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Batched Neural Bandits
In many sequential decisionmaking problems, the individuals are split i...
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Simultaneous Greedys: A Swiss Army Knife for Constrained Submodular Maximization
In this paper, we present SimultaneousGreedys, a deterministic algorithm...
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Multiple Descent: Design Your Own Generalization Curve
This paper explores the generalization loss of linear regression in vari...
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Continuous Submodular Maximization: Beyond DRSubmodularity
In this paper, we propose the first continuous optimization algorithms t...
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Meta Learning in the Continuous Time Limit
In this paper, we establish the ordinary differential equation (ODE) tha...
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The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Despite remarkable success, deep neural networks are sensitive to human...
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More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Despite remarkable success in practice, modern machine learning models h...
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Submodular Maximization Through Barrier Functions
In this paper, we introduce a novel technique for constrained submodular...
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Regularized Submodular Maximization at Scale
In this paper, we propose scalable methods for maximizing a regularized ...
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Streaming Submodular Maximization under a kSet System Constraint
In this paper, we propose a novel framework that converts streaming algo...
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Adaptivity in Adaptive Submodularity
Adaptive sequential decision making is one of the central challenges in ...
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Online Continuous Submodular Maximization: From FullInformation to Bandit Feedback
In this paper, we propose three online algorithms for submodular maximis...
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Minimax Regret of SwitchingConstrained Online Convex Optimization: No Phase Transition
We study the problem of switchingconstrained online convex optimization...
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Batched MultiArmed Bandits with Optimal Regret
We present a simple and efficient algorithm for the batched stochastic m...
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One Sample Stochastic FrankWolfe
One of the beauties of the projected gradient descent method lies in its...
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Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Streaming algorithms are generally judged by the quality of their soluti...
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Submodular Maximization Beyond Nonnegativity: Guarantees, Fast Algorithms, and Applications
It is generally believed that submodular functions  and the more gener...
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Stochastic Conditional Gradient++
In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
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Quantized FrankWolfe: CommunicationEfficient Distributed Optimization
How can we efficiently mitigate the overhead of gradient communications ...
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Adaptive Sequence Submodularity
In many machine learning applications, one needs to interactively select...
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Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodula...
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Unconstrained Submodular Maximization with Constant Adaptive Complexity
In this paper, we consider the unconstrained submodular maximization pro...
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Eliminating Latent Discrimination: Train Then Mask
How can we control for latent discrimination in predictive models? How c...
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Data Summarization at Scale: A TwoStage Submodular Approach
The sheer scale of modern datasets has resulted in a dire need for summa...
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ProjectionFree Bandit Convex Optimization
In this paper, we propose the first computationally efficient projection...
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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class ...
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Submodularity on Hypergraphs: From Sets to Sequences
In a nutshell, submodular functions encode an intuitive notion of dimini...
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ProjectionFree Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Online optimization has been a successful framework for solving largesc...
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Do Less, Get More: Streaming Submodular Maximization with Subsampling
In this paper, we develop the first onepass streaming algorithm for sub...
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Comparison Based Learning from Weak Oracles
There is increasing interest in learning algorithms that involve interac...
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Online Continuous Submodular Maximization
In this paper, we consider an online optimization process, where the obj...
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DeletionRobust Submodular Maximization at Scale
Can we efficiently extract useful information from a large usergenerate...
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Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Submodular functions are a broad class of set functions, which naturally...
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Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
In many machine learning applications, it is important to explain the pr...
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Estimating the Size of a Large Network and its Communities from a Random Sample
Most realworld networks are too large to be measured or studied directl...
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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Faced with massive data, is it possible to trade off (statistical) risk,...
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Submodular Variational Inference for Network Reconstruction
In realworld and online social networks, individuals receive and transm...
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NearOptimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting
In this paper, we consider the problem of actively learning a linear cla...
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Seeing the Unseen Network: Inferring Hidden Social Ties from RespondentDriven Sampling
Learning about the social structure of hidden and hardtoreach populati...
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Fast Mixing for Discrete Point Processes
We investigate the systematic mechanism for designing fast mixing Markov...
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Distributed Submodular Maximization
Many largescale machine learning problemsclustering, nonparametric l...
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Convolutional Neural Associative Memories: Massive Capacity with Noise Tolerance
The task of a neural associative memory is to retrieve a set of previous...
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Noise Facilitation in Associative Memories of Exponential Capacity
Recent advances in associative memory design through structured pattern ...
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Near Optimal Bayesian Active Learning for Decision Making
How should we gather information to make effective decisions? We address...
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Neural Networks Built from Unreliable Components
Recent advances in associative memory design through strutured pattern s...
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