Search Results for author: Moran Feldman

Found 19 papers, 5 papers with code

Streaming Submodular Maximization under a k-Set System Constraint

no code implementations ICML 2020 Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi

Moreover, we propose the first streaming algorithms for monotone submodular maximization subject to $k$-extendible and $k$-system constraints.

Data Summarization Movie Recommendation

Submodular Minimax Optimization: Finding Effective Sets

no code implementations26 May 2023 Loay Mualem, Ethan R. Elenberg, Moran Feldman, Amin Karbasi

Despite the rich existing literature about minimax optimization in continuous settings, only very partial results of this kind have been obtained for combinatorial settings.

dialog state tracking Prompt Engineering +1

Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets

no code implementations12 Oct 2022 Loay Mualem, Moran Feldman

We also present an inapproximability result showing that our online algorithm and Du's (2022) offline algorithm are both optimal in a strong sense.

Using Partial Monotonicity in Submodular Maximization

no code implementations7 Feb 2022 Loay Mualem, Moran Feldman

Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications.

BIG-bench Machine Learning Movie Recommendation

Submodular + Concave

no code implementations NeurIPS 2021 Siddharth Mitra, Moran Feldman, Amin Karbasi

It has been well established that first order optimization methods can converge to the maximal objective value of concave functions and provide constant factor approximation guarantees for (non-convex/non-concave) continuous submodular functions.

The Power of Subsampling in Submodular Maximization

no code implementations6 Apr 2021 Christopher Harshaw, Ehsan Kazemi, Moran Feldman, Amin Karbasi

We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings.

Movie Recommendation Video Summarization

How Do You Want Your Greedy: Simultaneous or Repeated?

1 code implementation29 Sep 2020 Moran Feldman, Christopher Harshaw, Amin Karbasi

We also present SubmodularGreedy. jl, a Julia package which implements these algorithms and may be downloaded at https://github. com/crharshaw/SubmodularGreedy. jl .

Continuous Submodular Maximization: Beyond DR-Submodularity

no code implementations NeurIPS 2020 Moran Feldman, Amin Karbasi

We first prove that a simple variant of the vanilla coordinate ascent, called Coordinate-Ascent+, achieves a $(\frac{e-1}{2e-1}-\varepsilon)$-approximation guarantee while performing $O(n/\varepsilon)$ iterations, where the computational complexity of each iteration is roughly $O(n/\sqrt{\varepsilon}+n\log n)$ (here, $n$ denotes the dimension of the optimization problem).

Submodular Maximization in Clean Linear Time

no code implementations16 Jun 2020 Wenxin Li, Moran Feldman, Ehsan Kazemi, Amin Karbasi

In this paper, we provide the first deterministic algorithm that achieves the tight $1-1/e$ approximation guarantee for submodular maximization under a cardinality (size) constraint while making a number of queries that scales only linearly with the size of the ground set $n$.

Movie Recommendation Text Summarization +1

Regularized Submodular Maximization at Scale

no code implementations10 Feb 2020 Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi

In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$.

Data Summarization Point Processes +1

Streaming Submodular Maximization under a $k$-Set System Constraint

1 code implementation9 Feb 2020 Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi

In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization.

Data Summarization Movie Recommendation

Almost Optimal Semi-streaming Maximization for k-Extendible Systems

no code implementations11 Jun 2019 Moran Feldman, Ran Haba

In this paper we consider the problem of finding a maximum weight set subject to a $k$-extendible constraint in the data stream model.

Data Structures and Algorithms 68W40 (Primary) 68R05 (Secondary) F.2.2; G.1.6; G.2.1

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

1 code implementation19 Apr 2019 Christopher Harshaw, Moran Feldman, Justin Ward, Amin Karbasi

It is generally believed that submodular functions -- and the more general class of $\gamma$-weakly submodular functions -- may only be optimized under the non-negativity assumption $f(S) \geq 0$.

Experimental Design

Adaptive Sequence Submodularity

1 code implementation NeurIPS 2019 Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi

In many machine learning applications, one needs to interactively select a sequence of items (e. g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e. g., guiding an agent through a series of states).

Decision Making Link Prediction +1

Unconstrained Submodular Maximization with Constant Adaptive Complexity

no code implementations15 Nov 2018 Lin Chen, Moran Feldman, Amin Karbasi

In this paper, we consider the unconstrained submodular maximization problem.

Do Less, Get More: Streaming Submodular Maximization with Subsampling

no code implementations NeurIPS 2018 Moran Feldman, Amin Karbasi, Ehsan Kazemi

In this paper, we develop the first one-pass streaming algorithm for submodular maximization that does not evaluate the entire stream even once.

Video Summarization

Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?

no code implementations ICML 2018 Lin Chen, Moran Feldman, Amin Karbasi

In this paper, we prove that a randomized version of the greedy algorithm (previously used by Buchbinder et al. (2014) for a different problem) achieves an approximation ratio of $(1 + 1/\gamma)^{-2}$ for the maximization of a weakly submodular function subject to a general matroid constraint, where $\gamma$ is a parameter measuring the distance of the function from submodularity.

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

1 code implementation NeurIPS 2017 Ethan R. Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi

In many machine learning applications, it is important to explain the predictions of a black-box classifier.

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