Amin Karbasi

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EPFL ∙ ETH Zurich ∙ Yale University

Assistant Professor at Yale University

An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks

It is well known that modern deep neural networks are powerful enough to...
06/14/2021 ∙ by Shashank Rajput, et al. ∙ 0 ∙ share

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Submodular + Concave

It has been well established that first order optimization methods can c...
06/09/2021 ∙ by Siddharth Mitra, et al. ∙ 0 ∙ share

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Parallelizing Thompson Sampling

How can we make use of information parallelism in online decision making...
06/02/2021 ∙ by Amin Karbasi, et al. ∙ 0 ∙ share

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Learning and Certification under Instance-targeted Poisoning

In this paper, we study PAC learnability and certification under instanc...
05/18/2021 ∙ by Ji Gao, et al. ∙ 0 ∙ share

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The Power of Subsampling in Submodular Maximization

We propose subsampling as a unified algorithmic technique for submodular...
04/06/2021 ∙ by Christopher Harshaw, et al. ∙ 0 ∙ share

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Batched Neural Bandits

In many sequential decision-making problems, the individuals are split i...
02/25/2021 ∙ by Quanquan Gu, et al. ∙ 6 ∙ share

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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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Amin Karbasi

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