7 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
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 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…