47 citations · 64 across the 9 of their papers we have counts for
11 papers
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
Xiang Meng, Wenyu Chen, Riade Benbaki +1
The increasing computational demands of modern neural networks present deployment challenges on resource-constrained devices. Network pruning offers a solution to reduce model size…
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
Kayhan Behdin, Wenyu Chen, Rahul Mazumder
We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given samples from a multiv…
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…
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Riade Benbaki, Wenyu Chen, Xiang Meng +4
The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or spa…
Causal Structural Learning Via Local Graphs
Wenyu Chen, Mathias Drton, Ali Shojaie
We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well…
Definite Non-Ancestral Relations and Structure Learning
Wenyu Chen, Mathias Drton, Ali Shojaie
In causal graphical models based on directed acyclic graphs (DAGs), directed paths represent causal pathways between the corresponding variables. The variable at the beginning of s…
A new computational framework for log-concave density estimation
Wenyu Chen, Rahul Mazumder, Richard J. Samworth
In Statistics, log-concave density estimation is a central problem within the field of nonparametric inference under shape constraints. Despite great progress in recent years on th…
Subgradient Regularized Multivariate Convex Regression at Scale
Wenyu Chen, Rahul Mazumder
We present new large-scale algorithms for fitting a subgradient regularized multivariate convex regression function to samples in dimensions -- a key problem in shape const…
On Causal Discovery with Equal Variance Assumption
Wenyu Chen, Mathias Drton, Y. Samuel Wang
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances.…
Discretized conformal prediction for efficient distribution-free inference
Wenyu Chen, Kelli-Jean Chun, Rina Foygel Barber
In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the dist…
Trimmed Conformal Prediction for High-Dimensional Models
Wenyu Chen, Zhaokai Wang, Wooseok Ha +1
In regression, conformal prediction is a general methodology to construct prediction intervals in a distribution-free manner. Although conformal prediction guarantees strong statis…