Stochastic Processes

Approximate Message Passing with Spectral Initialization for Generalized Linear Models AISTATS 2021 A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning JMLR 2021 Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections ICML 2021 A Technical Note on Non-Stationary Parametric Bandits: Existing Mistakes and Preliminary Solutions ALT 2021 Automatic structured variational inference AISTATS 2021 Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games AAAI 2021 Bandits with partially observable confounded data UAI 2021 Batched Thompson Sampling NeurIPS 2021 Best Model Identification: A Rested Bandit Formulation ICML 2021 Better Training using Weight-Constrained Stochastic Dynamics ICML 2021 Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization ICML 2021 Bias-Free Scalable Gaussian Processes via Randomized Truncations ICML 2021 Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning ICML 2021 Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data ICML 2021 Cardinality-Regularized Hawkes-Granger Model NeurIPS 2021 Change Matters: Medication Change Prediction with Recurrent Residual Networks IJCAI 2021 Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning ICML 2021 Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms AISTATS 2021 Communication-efficient SGD: From Local SGD to One-Shot Averaging NeurIPS 2021 Conditioning Sparse Variational Gaussian Processes for Online Decision-making NeurIPS 2021 Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning JMLR 2021 Continuous Integration over SO(3) for IMU Preintegration RSS 2021 Continuous Mean-Covariance Bandits NeurIPS 2021 Continuous Time Analysis of Momentum Methods JMLR 2021 Contrastive learning of strong-mixing continuous-time stochastic processes AISTATS 2021