Mathematics & Optimization

Online Learning in Kernelized Markov Decision Processes AISTATS 2019 On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters AAAI 2019 Online Learning via the Differential Privacy Lens NeurIPS 2019 Online learning with feedback graphs and switching costs AISTATS 2019 Online learning with kernel losses ICML 2019 Online Learning with Sleeping Experts and Feedback Graphs ICML 2019 Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms NeurIPS 2019 Online Meta-Learning ICML 2019 Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis NeurIPS 2019 Online Pandora’s Boxes and Bandits AAAI 2019 Online Prediction of Switching Graph Labelings with Cluster Specialists NeurIPS 2019 Online sampling from log-concave distributions NeurIPS 2019 Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function NeurIPS 2019 On Mean Estimation for General Norms with Statistical Queries COLT 2019 On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons NeurIPS 2019 On Sampling Complexity of the Semidefinite Affine Rank Feasibility Problem AAAI 2019 On Scalable and Efficient Computation of Large Scale Optimal Transport ICML 2019 On Testing of Uniform Samplers AAAI 2019 On the Complexity of Approximating Wasserstein Barycenters ICML 2019 On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games AAAI 2019 On the Computational Power of Online Gradient Descent COLT 2019 On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition AISTATS 2019 On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization ICLR 2019 On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 On the convergence of single-call stochastic extra-gradient methods NeurIPS 2019