28 citations · 64 across the 10 of their papers we have counts for
10 papers
The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning
Nikhil Ghosh, Spencer Frei, Wooseok Ha +1
In this work, we investigate the dynamics of stochastic gradient descent (SGD) when training a single-neuron autoencoder with linear or ReLU activation on orthogonal data. We show…
Adaptive wavelet distillation from neural networks through interpretations
Wooseok Ha, Chandan Singh, Francois Lanusse +2
Recent deep-learning models have achieved impressive prediction performance, but often sacrifice interpretability and computational efficiency. Interpretability is crucial in many…
Interpreting and improving deep-learning models with reality checks
Chandan Singh, Wooseok Ha, Bin Yu
Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter cov…
Transformation Importance with Applications to Cosmology
Chandan Singh, Wooseok Ha, Francois Lanusse +3
Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence. Its potential benefits to these fields requires…
Statistical guarantees for local graph clustering
Wooseok Ha, Kimon Fountoulakis, Michael W. Mahoney
Local graph clustering methods aim to find small clusters in very large graphs. These methods take as input a graph and a seed node, and they return as output a good cluster in a r…
An equivalence between critical points for rank constraints versus low-rank factorizations
Wooseok Ha, Haoyang Liu, Rina Foygel Barber
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so th…
Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization
Wooseok Ha, Emil Y. Sidky, Rina Foygel Barber +2
We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission…
Alternating minimization and alternating descent over nonconvex sets
Wooseok Ha, Rina Foygel Barber
We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex c…
Gradient descent with nonconvex constraints: local concavity determines convergence
Rina Foygel Barber, Wooseok Ha
Many problems in high-dimensional statistics and optimization involve minimization over nonconvex constraints-for instance, a rank constraint for a matrix estimation problem-but li…
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…