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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 851–900 of 2050 papers

TitleStatusHype
Estimating Optimal Policy Value in General Linear Contextual Bandits—0
Linear Bandits with Memory: from Rotting to Rising—0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Best Arm Identification for Stochastic Rising BanditsCode0
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnessCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
What are the mechanisms underlying metacognitive learning?—0
Fast Linear Model Trees by PILOT—0
On the Limitation and Experience Replay for GNNs in Continual Learning—0
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection—0
A Strong Baseline for Batch Imitation Learning—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect EstimationCode0
Empirical analysis in limit order book modeling for Nikkei 225 Stocks with Cox-type intensities—0
MLOps with enhanced performance control and observability—0
How to select predictive models for causal inference?—0
Dirichlet process mixture of Gaussian process functional regressions and its variational EM algorithm—0
Straight-Through meets Sparse Recovery: the Support Exploration Algorithm—0
Revisiting Bellman Errors for Offline Model SelectionCode0
A Deep Learning Method for Comparing Bayesian Hierarchical ModelsCode0
Warlock: an automated computational workflow for simulating spatially structured tumour evolutionCode0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks—0
Understanding Best Subset Selection: A Tale of Two C(omplex)ities—0
Guided Recommendation for Model Fine-Tuning—0
BiasBed - Rigorous Texture Bias EvaluationCode0
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countriesCode0
Bayesian Interpolation with Deep Linear Networks—0
Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection CriteriaCode0
Fast and fully-automated histograms for large-scale data sets—0
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks—0
An Information-Theoretic Approach to Transferability in Task Transfer Learning—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Dominant Drivers of National Inflation—0
Optimal Model Selection in RDD and Related Settings Using Placebo Zones—0
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation—0
Stochastic Rising BanditsCode0
Designing Ecosystems of Intelligence from First Principles—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance—0
The smooth output assumption, and why deep networks are better than wide ones—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
BiasBed -- Rigorous Texture Bias EvaluationCode0
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks—0
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
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