11 citations · 15 across the 8 of their papers we have counts for
8 papers
MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
Gabriel Afriat, Xiang Meng, Shibal Ibrahim +2
Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trained model is compressed with…
OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization
Xiang Meng, Shibal Ibrahim, Kayhan Behdin +3
Structured pruning is a promising approach for reducing the inference costs of large vision and language models. By removing carefully chosen structures, e.g., neurons or attention…
End-to-end Feature Selection Approach for Learning Skinny Trees
Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder
We propose a new optimization-based approach for feature selection in tree ensembles, an important problem in statistics and machine learning. Popular tree ensemble toolkits e.g.,…
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh +3
The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a s…
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder
Decision tree ensembles are widely used and competitive learning models. Despite their success, popular toolkits for learning tree ensembles have limited modeling capabilities. For…
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance
Shibal Ibrahim, Natalia Ponomareva, Rahul Mazumder
Fine-tuning of large pre-trained image and language models on small customized datasets has become increasingly popular for improved prediction and efficient use of limited resourc…
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions
Shibal Ibrahim, Peter Radchenko, Emanuel Ben-David +1
In this paper, we consider the problem of predicting survey response rates using a family of flexible and interpretable nonparametric models. The study is motivated by the US Censu…
Nonparametric Finite Mixture Models with Possible Shape Constraints: A Cubic Newton Approach
Haoyue Wang, Shibal Ibrahim, Rahul Mazumder
We explore computational aspects of maximum likelihood estimation of the mixture proportions of a nonparametric finite mixture model -- a convex optimization problem with old roots…