Pages that link to "Item:Q5943102"

The following pages link to Improved bounds on the sample complexity of learning (Q5943102):

Displaying 46 items.

• Range minima queries with respect to a random permutation, and approximate range counting (Q629829) (← links)
• Relative \((p,\varepsilon )\)-approximations in geometry (Q633202) (← links)
• Two proofs for shallow packings (Q728497) (← links)
• Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension (Q1314506) (← links)
• Microchoice bounds and self bounding learning algorithms (Q1394783) (← links)
• Fast approximation of betweenness centrality through sampling (Q1741154) (← links)
• A general lower bound on the number of examples needed for learning (Q1823011) (← links)
• On the sample complexity of weak learning (Q1892883) (← links)
• General bounds on the number of examples needed for learning probabilistic concepts (Q1916527) (← links)
• Shape matching under rigid motion (Q1947970) (← links)
• The true sample complexity of active learning (Q1959593) (← links)
• Optimal approximations made easy (Q2122796) (← links)
• The \(\varepsilon\)-\(t\)-net problem (Q2167320) (← links)
• Near-optimal coresets of kernel density estimates (Q2189735) (← links)
• Dynamic planar Voronoi diagrams for general distance functions and their algorithmic applications (Q2207601) (← links)
• Sampling-based algorithm for link prediction in temporal networks (Q2282277) (← links)
• Shallow packings, semialgebraic set systems, macbeath regions, and polynomial partitioning (Q2415378) (← links)
• Estimation of the hardness of the learning with errors problem with a restricted number of samples (Q2415419) (← links)
• Learning big (image) data via coresets for dictionaries (Q2513397) (← links)
• The VC dimension of metric balls under Fréchet and Hausdorff distances (Q2665263) (← links)
• The optimal sample complexity of PAC learning (Q2810825) (← links)
• Refined error bounds for several learning algorithms (Q2834450) (← links)
• Subsampling in Smoothed Range Spaces (Q2835631) (← links)
• Characterizing the sample complexity of private learners (Q2986862) (← links)
• Core-Sets: Updated Survey (Q3297370) (← links)
• Turning Big Data Into Tiny Data: Constant-Size Coresets for $k$-Means, PCA, and Projective Clustering (Q3304733) (← links)
• Theory of Classification: a Survey of Some Recent Advances (Q3373749) (← links)
• (Q4202912) (← links)
• Submodular Functions: Learnability, Structure, and Optimization (Q4564777) (← links)
• The Communication Complexity of Distributed epsilon-Approximations (Q4978194) (← links)
• (Q5009629) (← links)
• Greedy Strategy Works for k-Center Clustering with Outliers and Coreset Construction (Q5075781) (← links)
• (Q5075824) (← links)
• Journey to the Center of the Point Set (Q5088971) (← links)
• (Q5088989) (← links)
• (Q5091023) (← links)
• Approximating the distribution of the median and other robust estimators on uncertain data (Q5115783) (← links)
• (Q5214238) (← links)
• (Q5214265) (← links)
• The Learning Rate of lp -coefficient Regularized Shannon Sampling Algorithm (Q5259986) (← links)
• (Q5743377) (← links)
• A Size-Sensitive Discrepancy Bound for Set Systems of Bounded Primal Shatter Dimension (Q5743609) (← links)
• Safe Multi-Agent Pathfinding with Time Uncertainty (Q5856485) (← links)
• On coresets for support vector machines (Q5919115) (← links)
• Estimating the clustering coefficient using sample complexity analysis (Q6109016) (← links)
• Realizable learning is all you need (Q6566462) (← links)