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2020 – today
- 2024
[c13]Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong:
Fairness in Submodular Maximization over a Matroid Constraint. AISTATS 2024: 1027-1035- [c12]Jinuk Kim, Marwa El Halabi, Mingi Ji, Hyun Oh Song:
LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging. ICML 2024 - [i11]Jinuk Kim, Marwa El Halabi, Mingi Ji, Hyun Oh Song:
LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging. CoRR abs/2406.12837 (2024) - 2023
- [c11]Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization over a Matroid Constraint. ICML 2023: 9150-9171 - [c10]Marwa El Halabi, George Orfanides, Tim Hoheisel:
Difference of submodular minimization via DC programming. ICML 2023: 9172-9201 - [i10]Marwa El Halabi, George Orfanides, Tim Hoheisel:
Difference of Submodular Minimization via DC Programming. CoRR abs/2305.11046 (2023) - [i9]Marwa El Halabi, Federico Fusco
, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization over a Matroid Constraint. CoRR abs/2305.15118 (2023) - [i8]Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong:
Fairness in Submodular Maximization over a Matroid Constraint. CoRR abs/2312.14299 (2023) - 2022
- [c9]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. NeurIPS 2022 - [i7]Marwa El Halabi, Suraj Srinivas, Simon Lacoste-Julien:
Data-Efficient Structured Pruning via Submodular Optimization. CoRR abs/2203.04940 (2022) - 2020
- [c8]Marwa El Halabi, Stefanie Jegelka:
Optimal approximation for unconstrained non-submodular minimization. ICML 2020: 3961-3972 - [c7]Marwa El Halabi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization: Algorithms and Hardness. NeurIPS 2020 - [i6]Marwa El Halabi, Slobodan Mitrovic, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski:
Fairness in Streaming Submodular Maximization: Algorithms and Hardness. CoRR abs/2010.07431 (2020)
2010 – 2019
- 2019
- [i5]Marwa El Halabi, Stefanie Jegelka:
Minimizing approximately submodular functions. CoRR abs/1905.12145 (2019) - 2018
- [b1]Marwa El Halabi:
Learning with Structured Sparsity: From Discrete to Convex and Back. EPFL, Switzerland, 2018 - [c6]Marwa El Halabi, Francis R. Bach, Volkan Cevher:
Combinatorial Penalties: Which structures are preserved by convex relaxations? AISTATS 2018: 1551-1560 - 2017
- [i4]Marwa El Halabi, Francis R. Bach, Volkan Cevher:
Combinatorial Penalties: Which structures are preserved by convex relaxations? CoRR abs/1710.06273 (2017) - 2016
- [c5]Gergely Ódor, Yen-Huan Li, Alp Yurtsever, Ya-Ping Hsieh, Quoc Tran-Dinh, Marwa El Halabi, Volkan Cevher:
Frank-Wolfe works for non-Lipschitz continuous gradient objectives: Scalable poisson phase retrieval. ICASSP 2016: 6230-6234 - [c4]Ashkan Norouzi-Fard, Abbas Bazzi, Ilija Bogunovic, Marwa El Halabi, Ya-Ping Hsieh, Volkan Cevher:
An Efficient Streaming Algorithm for the Submodular Cover Problem. NIPS 2016: 4493-4501 - [i3]Ashkan Norouzi-Fard, Abbas Bazzi, Marwa El Halabi, Ilija Bogunovic, Ya-Ping Hsieh, Volkan Cevher:
An Efficient Streaming Algorithm for the Submodular Cover Problem. CoRR abs/1611.08574 (2016) - 2015
- [c3]Marwa El Halabi, Volkan Cevher:
A totally unimodular view of structured sparsity. AISTATS 2015 - [i2]Anastasios Kyrillidis, Luca Baldassarre, Marwa El Halabi, Quoc Tran-Dinh, Volkan Cevher:
Structured Sparsity: Discrete and Convex approaches. CoRR abs/1507.05367 (2015) - 2014
- [c2]Marwa El Halabi, Luca Baldassarre, Volkan Cevher:
Map estimation for Bayesian mixture models with submodular priors. MLSP 2014: 1-6 - [i1]Marwa El Halabi, Volkan Cevher:
A totally unimodular view of structured sparsity. CoRR abs/1411.1990 (2014) - 2013
- [c1]Marwa El Halabi, Luca Baldassarre, Volkan Cevher:
To convexify or not? Regression with clustering penalties on graphs. CAMSAP 2013: 21-24
Coauthor Index
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last updated on 2024-09-04 00:31 CEST by the dblp team
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