How can we select the best performing data-driven model and quantify its generalization error? This question has received a solid answer from the field of statistical inference since the last century and before [1, 2]
115 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.This thesis examines issues r...
We propose a novel theoretical framework for understanding learning and generalization which we will...
We propose a novel theoretical framework for understanding learning and generalization which we will...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
In this thesis, I derive generalization error bounds — bounds on the expected inaccuracy of the pred...
<p>In this thesis, I derive generalization error bounds — bounds on the expected inaccuracy of the p...
<p>After training, the model was tested on data that differ from its training distribution. (A) Disc...
Methods to avoid overfitting fall into two broad categories: data-oriented (using separate data fo...
Statistical learning theory studies the process of inferring regularities from empirical data. The f...
In this paper, we study the performance of extremum estimators from the perspective of generalizatio...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
115 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.This thesis examines issues r...
We propose a novel theoretical framework for understanding learning and generalization which we will...
We propose a novel theoretical framework for understanding learning and generalization which we will...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
How can we select the best performing data-driven model? How can we rigorously estimate its generali...
In this thesis, I derive generalization error bounds — bounds on the expected inaccuracy of the pred...
<p>In this thesis, I derive generalization error bounds — bounds on the expected inaccuracy of the p...
<p>After training, the model was tested on data that differ from its training distribution. (A) Disc...
Methods to avoid overfitting fall into two broad categories: data-oriented (using separate data fo...
Statistical learning theory studies the process of inferring regularities from empirical data. The f...
In this paper, we study the performance of extremum estimators from the perspective of generalizatio...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
The “−2 ” in the definition makes the log-likelihood loss for the Gaussian distribution match square...
115 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.This thesis examines issues r...
We propose a novel theoretical framework for understanding learning and generalization which we will...
We propose a novel theoretical framework for understanding learning and generalization which we will...
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