- [Sep 5, 2024] Transfer Learning for Bayesian Optimization on Homogeneous and Heterogeneous Search Spaces @ National University of Singapore (NUS), Singapore.
- [Jun 24, 2024] Transfer Learning for Bayesian Optimization on Homogeneous and Heterogeneous Search Spaces @ Meta Adaptive Experimentation Workshop, New York, USA.
- [Nov 15, 2023] Towards Bridging the Gap Between Human and AI Beliefs: Gaussian Process Probes and Pre-trained Gaussian Processes @ AI Centre of University College London, UK.
- [Nov 15, 2023] Towards Bridging the Gap Between Human and AI Beliefs: Gaussian Process Probes and Pre-trained Gaussian Processes @ Imperial College London, UK.
- [Jun 7, 2023] Using pre-trained models and Gaussian processes to make uncertainty-aware decisions @ Sustainable Urban Mobility: Simulation and Optimization Workshop, Mountain View, CA.
- [Mar 9, 2023] Experimental Design and Domain Expertise: The Essential Ingredients for Robot Learning @ Seminars on Experimental Design and Active Learning in the Real World (Virtual).
- [Oct 17, 2022] Pre-trained Gaussian processes for Bayesian optimization @ BayesOpt Session of INFORMS 2022, Indianapolis, IN.
- [Jul 26, 2022] Panelist of “AutoML in the age of large-pretrained models” @ AutoML Conference 2022, Baltimore, MD. video
- [May 17, 2022] Prior learning for Bayesian optimization @ HEC Montreal Optimization Days 2022, Montreal, Canada.
- [May 5, 2022] Pre-training helps Bayesian optimization too @ AutoML Seminars. video
- [Oct 26, 2021] Priors in probabilistic numerics @ Dagstuhl Seminar on ‘Probabilistic Numerical Methods - From Theory to Implementation’. slides
- [July 29, 2021] Bayesian Optimization @ Vilnius Machine Learning Workshop (Virtual). slides
- [Oct 31, 2019] Bayesian Optimization for Global Optimization of Expensive Black-box Functions @ MLFL, UMass Amherst, MA.
- [Jun 21, 2019] Human intelligence assisted robot learning @ RSS Pioneers 2019, University of Freiburg, Freiburg, Germany.
- [Jun 19, 2019] Human intelligence assisted robot learning @ Machine Learning & Robotics Lab, University Stuttgart, Stuttgart, Germany.
- [Jun 18, 2019] Human intelligence assisted robot learning @ Shift Technology, Paris, France.
- [Oct 3, 2018] Active model learning and diverse action sampling for task and motion planning (spotlight) @ IROS 2018, Madrid, Spain.
- [Sep 20, 2018] Active model learning and diverse action sampling for task and motion planning @ University of Washington, Seattle, WA. slides
- [Sep 18, 2018] Regret bound of Bayesian optimization with unknown GP priors @ Microsoft Research AI Breakthroughs Workshop, Redmond, WA.
- [Jul 3, 2018] Bayesian Optimization Guided by Max-values @ ISMP 2018, Bordeaux, France. slides
- [Mar 9, 2018] Integrating model learning and TAMP @ LIS, MIT, Cambridge, MA. [slides available upon request]
- [Nov 30, 2017] Bayesian Optimization and How to Scale It Up @ Computer Science Colloquium, University of Southern California, Los Angeles, CA. slides
- [Aug 9, 2017] Max-value Entropy Search for Efficient Bayesian Optimization @ ICML 2017, Sydney, Australia. slides video
- [Aug 7, 2017] Batched High-dimensional Bayesian Optimization via Structural Kernel Learning @ ICML 2017, Sydney, Australia. slides video
- [Jun 20, 2017] (remote talk) Scaling up Bayesian Optimization with Ensembles @ DeepMind, London, UK. slides
- [Jun 16, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ Uber ATG, Pittsburgh, PA.
- [Jun 7, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ The Manipulation Lab, CMU Robotics Institute, Pittsburgh, PA.
- [May 31, 2017] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems @ ICRA 2017, Singapore. poster slides
- [Apr 28, 2017] Challenges in Long-horizon Planning and How to Learn a “Few-shot” Precondition Generator @ LIS, MIT, Cambridge, MA.
- [May 9, 2016] Optimization as Estimation with Gaussian Processes in Bandit Settings @ AISTATS 2016, Cadiz, Spain. slides
- [May 2, 2016] Optimization as Estimation with Gaussian Processes in Bandit Settings @ Machine Learning Tea, MIT, Cambridge, MA.
