sample complexity

Trading robust representations for sample complexity through self-supervised visual experience NeurIPS 2018 Active Learning for Top-K Rank Aggregation from Noisy Comparisons ICML 2017 Adaptive Active Hypothesis Testing under Limited Information NeurIPS 2017 Adaptive Batch Size for Safe Policy Gradients NeurIPS 2017 Adaptive Multiple-Arm Identification ICML 2017 A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control NeurIPS 2017 A Sample Complexity Measure with Applications to Learning Optimal Auctions NeurIPS 2017 Collaborative Clustering: Sample Complexity and Efficient Algorithms ALT 2017 Collaborative PAC Learning NeurIPS 2017 Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible ICML 2017 Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction ICML 2017 Effective Parallelisation for Machine Learning NeurIPS 2017 Effective Semisupervised Learning on Manifolds COLT 2017 How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices? ICML 2017 Identify the Nash Equilibrium in Static Games with Random Payoffs ICML 2017 Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications NeurIPS 2017 Iterative Machine Teaching ICML 2017 Learning a Ground Truth Ranking Using Noisy Approval Votes IJCAI 2017 Learning Determinantal Point Processes with Moments and Cycles ICML 2017 Learning from Conditional Distributions via Dual Embeddings AISTATS 2017 Learning from Networked Examples ALT 2017 Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions AISTATS 2017 Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity NeurIPS 2017 Learning Low-Dimensional Metrics NeurIPS 2017 Learning Multivariate Log-concave Distributions COLT 2017