8 citations · 21 across the 8 of their papers we have counts for
8 papers
How many classifiers do we need?
Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen +1
As performance gains through scaling data and/or model size experience diminishing returns, it is becoming increasingly popular to turn to ensembling, where the predictions of mult…
When are ensembles really effective?
Ryan Theisen, Hyunsuk Kim, Yaoqing Yang +2
Ensembling has a long history in statistical data analysis, with many impactful applications. However, in many modern machine learning settings, the benefits of ensembling are less…
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
Yaoqing Yang, Ryan Theisen, Liam Hodgkinson +4
Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct…
Taxonomizing local versus global structure in neural network loss landscapes
Yaoqing Yang, Liam Hodgkinson, Ryan Theisen +4
Viewing neural network models in terms of their loss landscapes has a long history in the statistical mechanics approach to learning, and in recent years it has received attention…
Evaluating State-of-the-Art Classification Models Against Bayes Optimality
Ryan Theisen, Huan Wang, Lav R. Varshney +2
Evaluating the inherent difficulty of a given data-driven classification problem is important for establishing absolute benchmarks and evaluating progress in the field. To this end…
Good Classifiers are Abundant in the Interpolating Regime
Ryan Theisen, Jason M. Klusowski, Michael W. Mahoney
Within the machine learning community, the widely-used uniform convergence framework has been used to answer the question of how complex, over-parameterized models can generalize w…
Global Capacity Measures for Deep ReLU Networks via Path Sampling
Ryan Theisen, Jason M. Klusowski, Huan Wang +3
Classical results on the statistical complexity of linear models have commonly identified the norm of the weights as a fundamental capacity measure. Generalizations of this…
Stochastic versus deterministic consensus dynamics on graphs
Dylan Weber, Ryan Theisen, Sebastien Motsch
We study two agent based models of opinion formation - one stochastic in nature and one deterministic. Both models are defined in terms of an underlying graph; we study how the str…