importance sampling

Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems AISTATS 2021 Dual Active Learning for Both Model and Data Selection IJCAI 2021 Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling AISTATS 2021 Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm ICML 2021 Gradient Descent in RKHS with Importance Labeling AISTATS 2021 Improving black-box optimization in VAE latent space using decoder uncertainty NeurIPS 2021 Independence-aware Advantage Estimation IJCAI 2021 Learning from eXtreme Bandit Feedback AAAI 2021 Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories ICCV 2021 L-SVRG and L-Katyusha with Arbitrary Sampling JMLR 2021 Monte Carlo Filtering Objectives IJCAI 2021 Monte Carlo Variational Auto-Encoders ICML 2021 NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform NeurIPS 2021 Nested Variational Inference NeurIPS 2021 On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models AISTATS 2021 On Sampling-Based Training Criteria for Neural Language Modeling Interspeech 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021 Optimized auxiliary particle filters: adapting mixture proposals via convex optimization UAI 2021 Policy Optimization as Online Learning with Mediator Feedback AAAI 2021 Query complexity of least absolute deviation regression via robust uniform convergence COLT 2021 Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning NeurIPS 2021 Shadow Manifold Hamiltonian Monte Carlo AISTATS 2021 SOPE: Spectrum of Off-Policy Estimators NeurIPS 2021 State Relevance for Off-Policy Evaluation ICML 2021 Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning NeurIPS 2021