Search Results for author:
Found 9 papers, 4 papers with code
Recoverability Landscape of Tree Structured Markov Random Fields under Symmetric Noise
Furthermore, we present a polynomial time, sample efficient algorithm that recovers the exact tree when this is possible, or up to the unidentifiability as promised by our characterization, when full recoverability is impossible.
On Generalization of Adaptive Methods for Over-parameterized Linear Regression
In this paper, we aim to characterize the performance of adaptive methods in the over-parameterized linear regression setting.
Robust Estimation of Tree Structured Ising Models
We consider the task of learning Ising models when the signs of different random variables are flipped independently with possibly unequal, unknown probabilities.
Choosing the Sample with Lowest Loss makes SGD Robust
The presence of outliers can potentially significantly skew the parameters of machine learning models trained via stochastic gradient descent (SGD).
Negative sampling in semi-supervised learning
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL).
Minimum weight norm models do not always generalize well for over-parameterized problems
We empirically show that the minimum weight norm is not necessarily the proper gauge of good generalization in simplified scenaria, and different models found by adaptive methods could outperform plain gradient methods.
An Iterative Approach for Shadow Removal in Document Images
Uneven illumination and shadows in document images cause a challenge for digitization applications and automated workflows.
Matrix Completion via Factorizing Polynomials
While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities.
Trading-off variance and complexity in stochastic gradient descent
Stochastic gradient descent is the method of choice for large-scale machine learning problems, by virtue of its light complexity per iteration.
