Amin Karbasi

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EPFL ∙ ETH Zurich ∙ Yale University

Assistant Professor at Yale University

Simultaneous Greedys: A Swiss Army Knife for Constrained Submodular Maximization

In this paper, we present SimultaneousGreedys, a deterministic algorithm...
09/29/2020 ∙ by Moran Feldman, et al. ∙ 0 ∙ share

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Multiple Descent: Design Your Own Generalization Curve

This paper explores the generalization loss of linear regression in vari...
08/03/2020 ∙ by Lin Chen, et al. ∙ 2 ∙ share

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Continuous Submodular Maximization: Beyond DR-Submodularity

In this paper, we propose the first continuous optimization algorithms t...
06/21/2020 ∙ by Moran Feldman, et al. ∙ 0 ∙ share

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Meta Learning in the Continuous Time Limit

In this paper, we establish the ordinary differential equation (ODE) tha...
06/19/2020 ∙ by Ruitu Xu, et al. ∙ 30 ∙ share

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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-...
02/25/2020 ∙ by Yifei Min, et al. ∙ 10 ∙ share

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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...
02/11/2020 ∙ by Lin Chen, et al. ∙ 38 ∙ share

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Submodular Maximization Through Barrier Functions

In this paper, we introduce a novel technique for constrained submodular...
02/10/2020 ∙ by Ashwinkumar Badanidiyuru, et al. ∙ 9 ∙ share

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Regularized Submodular Maximization at Scale

In this paper, we propose scalable methods for maximizing a regularized ...
02/10/2020 ∙ by Ehsan Kazemi, et al. ∙ 11 ∙ share

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Streaming Submodular Maximization under a k-Set System Constraint

In this paper, we propose a novel framework that converts streaming algo...
02/09/2020 ∙ by Ran Haba, et al. ∙ 0 ∙ share

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Adaptivity in Adaptive Submodularity

Adaptive sequential decision making is one of the central challenges in ...
11/09/2019 ∙ by Hossein Esfandiari, et al. ∙ 12 ∙ share

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Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback

In this paper, we propose three online algorithms for submodular maximis...
10/28/2019 ∙ by Mingrui Zhang, et al. ∙ 16 ∙ share

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Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition

We study the problem of switching-constrained online convex optimization...
10/24/2019 ∙ by Lin Chen, et al. ∙ 11 ∙ share

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Batched Multi-Armed Bandits with Optimal Regret

We present a simple and efficient algorithm for the batched stochastic m...
10/11/2019 ∙ by Hossein Esfandiari, et al. ∙ 0 ∙ share

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One Sample Stochastic Frank-Wolfe

One of the beauties of the projected gradient descent method lies in its...
10/10/2019 ∙ by Mingrui Zhang, et al. ∙ 7 ∙ share

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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...
05/02/2019 ∙ by Ehsan Kazemi, et al. ∙ 4 ∙ share

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Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications

It is generally believed that submodular functions -- and the more gener...
04/19/2019 ∙ by Christopher Harshaw, et al. ∙ 0 ∙ share

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Stochastic Conditional Gradient++

In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
02/19/2019 ∙ by Hamed Hassani, et al. ∙ 0 ∙ share

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Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization

How can we efficiently mitigate the overhead of gradient communications ...
02/17/2019 ∙ by Mingrui Zhang, et al. ∙ 0 ∙ share

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Adaptive Sequence Submodularity

In many machine learning applications, one needs to interactively select...
02/15/2019 ∙ by Marko Mitrovic, et al. ∙ 8 ∙ share

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Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...
01/28/2019 ∙ by Lin Chen, et al. ∙ 6 ∙ share

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Unconstrained Submodular Maximization with Constant Adaptive Complexity

In this paper, we consider the unconstrained submodular maximization pro...
11/15/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Eliminating Latent Discrimination: Train Then Mask

How can we control for latent discrimination in predictive models? How c...
11/12/2018 ∙ by Soheil Ghili, et al. ∙ 2 ∙ share

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Data Summarization at Scale: A Two-Stage Submodular Approach

The sheer scale of modern datasets has resulted in a dire need for summa...
06/07/2018 ∙ by Marko Mitrovic, et al. ∙ 0 ∙ share

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Projection-Free Bandit Convex Optimization

In this paper, we propose the first computationally efficient projection...
05/18/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

This paper considers stochastic optimization problems for a large class ...
04/24/2018 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Submodularity on Hypergraphs: From Sets to Sequences

In a nutshell, submodular functions encode an intuitive notion of dimini...
02/26/2018 ∙ by Marko Mitrovic, et al. ∙ 0 ∙ share

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Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity

Online optimization has been a successful framework for solving large-sc...
02/22/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Do Less, Get More: Streaming Submodular Maximization with Subsampling

In this paper, we develop the first one-pass streaming algorithm for sub...
02/20/2018 ∙ by Moran Feldman, et al. ∙ 0 ∙ share

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Comparison Based Learning from Weak Oracles

There is increasing interest in learning algorithms that involve interac...
02/20/2018 ∙ by Ehsan Kazemi, et al. ∙ 0 ∙ share

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Online Continuous Submodular Maximization

In this paper, we consider an online optimization process, where the obj...
02/16/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Deletion-Robust Submodular Maximization at Scale

Can we efficiently extract useful information from a large user-generate...
11/20/2017 ∙ by Ehsan Kazemi, et al. ∙ 0 ∙ share

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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...
07/13/2017 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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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...
03/08/2017 ∙ by Ethan R. Elenberg, et al. ∙ 0 ∙ share

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Estimating the Size of a Large Network and its Communities from a Random Sample

Most real-world networks are too large to be measured or studied directl...
10/26/2016 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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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,...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Submodular Variational Inference for Network Reconstruction

In real-world and online social networks, individuals receive and transm...
03/29/2016 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting

In this paper, we consider the problem of actively learning a linear cla...
03/11/2016 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling

Learning about the social structure of hidden and hard-to-reach populati...
11/13/2015 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Fast Mixing for Discrete Point Processes

We investigate the systematic mechanism for designing fast mixing Markov...
06/06/2015 ∙ by Patrick Rebeschini, et al. ∙ 0 ∙ share

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Distributed Submodular Maximization

Many large-scale machine learning problems--clustering, non-parametric l...
11/03/2014 ∙ by Baharan Mirzasoleiman, et al. ∙ 0 ∙ share

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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...
07/24/2014 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Noise Facilitation in Associative Memories of Exponential Capacity

Recent advances in associative memory design through structured pattern ...
03/13/2014 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Near Optimal Bayesian Active Learning for Decision Making

How should we gather information to make effective decisions? We address...
02/24/2014 ∙ by Shervin Javdani, et al. ∙ 0 ∙ share

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Neural Networks Built from Unreliable Components

Recent advances in associative memory design through strutured pattern s...
01/26/2013 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Coupled Neural Associative Memories

We propose a novel architecture to design a neural associative memory th...
01/08/2013 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Comparison-Based Learning with Rank Nets

We consider the problem of search through comparisons, where a user is p...
06/18/2012 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Multi-Level Error-Resilient Neural Networks with Learning

The problem of neural network association is to retrieve a previously me...
02/13/2012 ∙ by Amir Hesam Salavati, et al. ∙ 0 ∙ share

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From Small-World Networks to Comparison-Based Search

The problem of content search through comparisons has recently received ...
07/15/2011 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Amin Karbasi

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