Learning Theory

Is Q-Learning Provably Efficient? NeurIPS 2018 Jointly Learning Network Connections and Link Weights in Spiking Neural Networks IJCAI 2018 Learners that Use Little Information ALT 2018 Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels NeurIPS 2018 Learning convex polytopes with margin NeurIPS 2018 Learning Deep ResNet Blocks Sequentially using Boosting Theory ICML 2018 Learning latent variable structured prediction models with Gaussian perturbations NeurIPS 2018 Learning linear structural equation models in polynomial time and sample complexity AISTATS 2018 Learning Loop Invariants for Program Verification NeurIPS 2018 Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time ICML 2018 Learning One Convolutional Layer with Overlapping Patches ICML 2018 Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data NeurIPS 2018 Learning Single-Index Models in Gaussian Space COLT 2018 Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity AISTATS 2018 Learning to Branch ICML 2018 Learning To Learn Around A Common Mean NeurIPS 2018 Learning under p-Tampering Attacks ALT 2018 Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification COLT 2018 Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator ICML 2018 Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games ICML 2018 Limitations in learning an interpreted language with recurrent models EMNLP 2018 Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability COLT 2018 Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning JMLR 2018 LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better ACL 2018 LSTMs Exploit Linguistic Attributes of Data ACL 2018