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Found 12 papers, 2 papers with code
Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection
We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter using a held-out set, we train the final model using {\em all} of the training data -- since this may or may not improve future generalization error, should one do this?
Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance
The tradeoffs in the excess risk incurred from data-driven learning of a single model has been studied by decomposing the excess risk into approximation, estimation and optimization errors.
Solving Constrained CASH Problems with ADMM
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens
The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits.
Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
We show theoretically that minimax MTL tends to avoid worst case outcomes on newly drawn test tasks in the learning to learn (LTL) test setting.
Fast Nonparametric Conditional Density Estimation
Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx).
Maximum Inner-Product Search using Tree Data-structures
Finally we present a new data structure for increasing the efficiency of the dual-tree algorithm.
Linear-time Algorithms for Pairwise Statistical Problems
Several key computational bottlenecks in machine learning involve pairwise distance computations, including all-nearest-neighbors (finding the nearest neighbor(s) for each point, e. g. in manifold learning) and kernel summations (e. g. in kernel density estimation or kernel machines).
Submanifold density estimation
Kernel density estimation is the most widely-used practical method for accurate nonparametric density estimation.
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
The long-standing problem of efficient nearest-neighbor (NN) search has ubiquitous applications ranging from astrophysics to MP3 fingerprinting to bioinformatics to movie recommendations.
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
We propose a new fast Gaussian summation algorithm for high-dimensional datasets with high accuracy.
QUIC-SVD: Fast SVD Using Cosine Trees
The Singular Value Decomposition is a key operation in many machine learning methods.
