• CS 159: Advanced Topics in Machine Learning, Caltech
    • Spring 2024: Large Language Models for Reasoning [www]
    • Spring 2022: Representation Learning for Science [www]
    • Spring 2021: Predictive Control & Neural Network Theory [www]
    • Spring 2020: Data-Driven Algorithm Design [www]
    • Spring 2019: Deep Probabilistic Models [www]
    • Spring 2018: Imitation Learning and Reinforcement Learning [www]
    • Spring 2017: Machine Learning for Structured Prediction [www]
    • Spring 2016: Online Learning, Interactive Machine Learning, and Learning from Human Feedback [www]
  • CS 155: Machine Learning & Data Mining, Caltech
    • Winter 2024: [www]
    • Winter 2022 (in advising capacity) [www]
    • Winter 2020 [www]
    • Winter 2019 [www]
    • Winter 2018 [www]
    • Winter 2017 [www]
    • Winter 2016 [www]
    • Winter 2015 [www]
  • CS 101: Projects in Machine Learning, Caltech
    • Fall 2019 [www]
    • Fall 2018 [www]
  • Imitation Learning, co-taught with Hoang Le, presented at ICML 2018.
  • An Introduction to Ensemble Methods: Bagging, Boosting, Random Forests and More, presented at Disney Research.
  • Practical Online Retrieval Evaluation, co-taught with Filip Radlinski, presented at SIGIR 2011.
    [slides][demo scripts]
  • Learning to Rank, co-taught with Filip Radlinski, presented at NESCAI 2008.
Other Talk Materials
  • Learning for Reliable Control in Dynamical Systems, Georgia Tech, March, 2024.
  • Controlling the Structure of Inference and Learning in Neural Networks, Johns Hopkins University, March, 2024.
  • AI for Adaptive Experiment Design, Genentech AI Seminar, Feburary, 2023.
    [slides][older video]
  • Neurosymbolic Programming, Caltech Explainable AI for Science Workshop, September, 2021.
  • Personalized Preference Learning from Spinal Cord Stimulation to Exoskeletons, July, 2021.
  • Improving Policy Learning via Programmatic Domain Knowledge, Caltech, April, 2021.
  • Competitive Algorithms for Online Control, Simons Institute Workshop for Mathematics of Online Decision Making, October, 2020.
  • Learning to Optimize as Policy Learning, Princeton University, October, 2020.
  • Learning for Safety-Critical Control in Dynamical Systems, Control Meets Learning, January, 2021.
  • New Frontiers in Imitation Learning, University of Chicago, November, 2019.
  • Policy Learning with Certifiable Guarantees, University of California Los Angeles, October, 2019.
  • Real-World Bayesian Optimization, KDD 2019 Workshop on Data Collection, Curation, and Labeling for Mining and Learning, August, 2019.
  • Two Vignettes in Robust Detection and Adversarial Analysis in Control, CVPR 2019 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems, June, 2019.
  • Structured Imitation and Reinforcement Learning, NeurIPS 2018 Workshop on Imitation Learning for Robotics, December, 2018.
  • Machine Teaching for Human Learners, IJCAI 2018 Workshop on Humanizing AI, July, 2018.
  • Inference + Imitation, ICML 2018 Workshop on Tractable Probabilistic Models, July, 2018.
  • The Dueling Bandits Problem, Massachusetts Institute of Technology, September, 2017.
  • Learning to Optimize for Structured Output Spaces, University of California Santa Barbara, April, 2017.
  • Recent Applications of Latent Factor Models, Second Spectrum, September, 2015.
  • Learning Spatial Models of Basketball Gameplay, KDD 2015 Workshop on Large-Scale Sports Analytics, August, 2015.
  • Balancing the Explore/Exploit Tradeoff in Interactive Structured Prediction, Cornell University, December, 2014.
  • Learning with Humans in the Loop, Disney Research, May, 2013.
  • Optimizing Recommender Systems as a Submodular Bandit Problem, University of Toronto, November, 2012.
  • An Introduction to Structural SVMs and its Application to Information Retrieval, University of California Berkeley, October, 2012.
  • Practical and Reliable Retrieval Evaluation Through Online Experimentation, WSDM 2012 Workshop on Web Search Click Data, February, 2012.
  • An Interactive Learning Approach to Optimizing Information Retrieval Systems, Carnegie Mellon University, September, 2010.
  • New Learning Frameworks for Information Retrieval, Microsoft Research, March, 2010.
  • Diversified Retrieval as Structured Prediction, SIGIR 2009 Workshop on Redundancy, Diversity and Interdependent Document Relevance, July, 2009.
  • Information Retrieval as Structured Prediction, University of Massachusetts Amherst, April, 2009.
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