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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1326–1350 of 2050 papers

TitleStatusHype
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Data—0
Tuning Large language model for End-to-end Speech Translation—0
Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Two-level histograms for dealing with outliers and heavy tail distributions—0
Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Uncertainty Profiles for LLMs: Uncertainty Source Decomposition and Adaptive Model-Metric Selection—0
Understanding and Estimating the Adaptability of Domain-Invariant Representations—0
Understanding prompt engineering may not require rethinking generalization—0
Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models—0
Understanding the double descent curve in Machine Learning—0
Understanding the Limits of Deep Tabular Methods with Temporal Shift—0
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review—0
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Universal Approximation of Edge Density in Large Graphs—0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
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