Jeff Schneider

Offline Reinforcement Learning for Rotation Profile Control in Tokamaks L4DC 2026 TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint L4DC 2026 Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks ICML 2025 Training a Generally Curious Agent ICML 2025 Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving CVPR 2024 Genetic Algorithm for Curriculum Design in Multi-Agent Reinforcement Learning CoRL 2024 Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data ICML 2024 Reasoning with Latent Diffusion in Offline Reinforcement Learning ICLR 2024 Sampling-based Multi-dimensional Recalibration ICML 2024 Learning Temporally AbstractWorld Models without Online Experimentation ICML 2023 Near-optimal Policy Identification in Active Reinforcement Learning ICLR 2023 Offline Model-Based Reinforcement Learning for Tokamak Control L4DC 2023 Stealthy Terrain-Aware Multi-Agent Active Search CoRL 2023 Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning ICML 2022 An Experimental Design Perspective on Model-Based Reinforcement Learning ICLR 2022 SBEVNet: End-to-End Deep Stereo Layout Estimation WACV 2022 Decentralized multi-agent active search for sparse signals UAI 2021 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations AISTATS 2020 Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly JMLR 2020 Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving WACV 2020 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments ICML 2019 Offline Contextual Bayesian Optimization NeurIPS 2019 Neural Architecture Search with Bayesian Optimisation and Optimal Transport NeurIPS 2018 Parallelised Bayesian Optimisation via Thompson Sampling AISTATS 2018 Transformation Autoregressive Networks ICML 2018 Equivariance Through Parameter-Sharing ICML 2017 Multi-fidelity Bayesian Optimisation with Continuous Approximations ICML 2017 Scaling Active Search using Linear Similarity Functions IJCAI 2017 The Statistical Recurrent Unit ICML 2017 Bayesian Nonparametric Kernel-Learning AISTATS 2016 Estimating Cosmological Parameters from the Dark Matter Distribution ICML 2016 Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations NeurIPS 2016 High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models AISTATS 2016 Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems IJCAI 2016 Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016 The Multi-fidelity Multi-armed Bandit NeurIPS 2016 Active Pointillistic Pattern Search AISTATS 2015 Fast Function to Function Regression AISTATS 2015 Finding Galaxies in the Shadows of Quasars with Gaussian Processes ICML 2015 High Dimensional Bayesian Optimisation and Bandits via Additive Models ICML 2015 Active Area Search via Bayesian Quadrature AISTATS 2014 Active Transfer Learning under Model Shift ICML 2014 Fast Distribution To Real Regression AISTATS 2014 Flexible Transfer Learning under Support and Model Shift NeurIPS 2014 FuSSO: Functional Shrinkage and Selection Operator AISTATS 2014 Distribution to Distribution Regression ICML 2013 Expensive Function Optimization with Stochastic Binary Outcomes ICML 2013 Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition NeurIPS 2013 Spectral Learning of Hidden Markov Models from Dynamic and Static Data ICML 2013 Σ-Optimality for Active Learning on Gaussian Random Fields NeurIPS 2013 A Composite Likelihood View for Multi-Label Classification AISTATS 2012 Nonparametric Estimation of Conditional Information and Divergences AISTATS 2012 Hierarchical Probabilistic Models for Group Anomaly Detection AISTATS 2011 Multi-Label Output Codes using Canonical Correlation Analysis AISTATS 2011 On the Estimation of α-Divergences AISTATS 2011 Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010