The Elements of Statistical Learning: Data Mining, Inference, and Prediction

By David Ruppert. 2004

18,671 citations recorded by the source corpus.

Subfields: Information Systems, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Spectral Networks and Locally Connected Networks on Graphs (2013, background): “For a larger account of the problem, we refer the reader to [10].”
Membership Inference Attacks Against Machine Learning Models (2016, background): “Many regularization techniques have been proposed to prevent models from becoming overfitted to their training datasets while minimizing their prediction error [19].”
Weakly Supervised Action Labeling in Videos under Ordering Constraints (2014, background): “Minimizing this cost with respect toW and b for fixed Z can be done in closed form [3,12].”
A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “According to the learning theory for supervised learning [153], the performance of an old task is improved with replaying more old training samples that approximate”
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning (2018, background): “Linear regression is at the basis of machine learning [25].”
Causal Regularization (2019, neutral): “where λ is a regularization parameter [13].”
Transductive Multi-View Zero-Shot Learning (2015, methodology): “A straightforward solution is to decompose the multi-label classification problem into multiple independent binary classification problems which is equivalent [14] to directly solving Eq (1) by: L̂T = [ [v(WT )] v(WT ) ]† [v(WT )] · ŶT (2)”
Non-stochastic Best Arm Identification and Hyperparameter Optimization (2015, methodology): “We adopt the train-validate-test framework for choosing hyperparameters (Hastie et al., 2005): 1.”
Successor Features for Transfer in Reinforcement Learning (2016, methodology): “Note that approximating r(s, a, s′) ≈ φ(s, a, s′)>w̃ is a supervised learning problem, so we can use well-understood techniques from the field to learn w̃ (and potentially φ̃, too) [9].”
Preserving Statistical Validity in Adaptive Data Analysis (2014, neutral): “7 in [27] for examples and references).”
Finding Density Functionals with Machine Learning (2011, methodology): “We use a linear combination of 3 Gaussian dips with different depths, widths, and centers: v(x) = − 3∑ i=1 ai exp(−(x− bi)2/(2c2i )).”
Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization (2018, methodology): “In this section, we apply Algorithm 1 for solving linear regression problems, the most popular technique for data ﬁtting [10, 31] in statistical machine learning, over a network of processors under random quantization.”
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption (2018, background): “Problem (2) arises frequently in supervised learning applications (Hastie et al., 2009).”
Bypassing the Kohn-Sham equations with machine learning (2016, background): “Kohn-Sham density functional theory[1] is now enor-mously popular as an electronic structure method in a wide variety of ﬁelds[2].”