UP TO DATE SCHEDULE is at Website: tiny.cc/mlaihci or https://sites.google.com/site/mlaihci/
(MLAIHCI – Machine Learning, Artificial Intelligence, Human-Computer Interaction)

TENTATIVE SCHEDULE (tiny.cc/mlaihci has UPDATED version)

8:50. Introductions

9:00
Michael Littman, Brown University: "Reinforcement Learning from users: New algorithms and frameworks"

10-10:30 Coffee Break

Machine Teaching
10:30
Jerry Zhu, University of Wisconsin Madison: "Machine Teaching as a Framework for Personalized Education"

Hoang M. Le, Yisong Yue, & Peter Carr. "Smooth Imitation Learning." [PDF]

11:45-1:30 Lunch.

Embedding Algorithms in User Technologies
1:30
John Langford, Microsoft Research: "An Interactive Learning Platform for Making Decisions"
Neil Heffernan, Worcester Polytechnic Institute: "Enabling real-time evaluation of crowdsourced machine learning algorithms: Experimentation and Personalization in online math problems on ASSISTments.org"

3:00-4:00 Spotlights & Posters

4-4:30 coffee break
4:30
Ambuj Tewari, Huitian Lei, & Susan Murphy. University of Michigan. "From Ads to Interventions: Contextual Bandit Algorithms for Mobile Health". (NIH application to "Heartsteps")

5:30-6:30 Conclusions & Future Directions

PRESENTATIONS

Jerry Zhu, University of Wisconsin Madison: "Machine Teaching as a Framework for Personalized Education"

Michael Littman, Brown University: "Reinforcement Learning from users: New algorithms and frameworks"

John Langford, Microsoft Research: "An Interactive Learning Platform for Making Decisions"

Neil Heffernan, Worcester Polytechnic Institute: "Enabling real-time evaluation of crowdsourced machine learning algorithms: Experimentation and Personalization in online math problems on ASSISTments.org"

Ambuj Tewari, Huitian Lei, & Susan Murphy. University of Michigan. "From Ads to Interventions: Contextual Bandit Algorithms for Mobile Health". (NIH application to "Heartsteps")

Bei Peng, James MacGlashan, Robert Loftin, Michael L. Littman, David L. Roberts, & Matthew E. Taylor. "A Need for Speed: Adapting Agent Action Speed to Improve Performance of Task Learning from Turkers."

Wei Sun, Anshul Sheopuri, Ying Li, & Thales S. Teixeira. "Cognitive Advertisement Design via Dynamic Bayesian Network."

Stefanos Poulis & Sanjoy Dasgupta. "Interactive annotation with feature feedback: from theory to practice."

Jens Schreiter, Mona Eberts, Duy Nguyen-Tuong, & Marc Toussaint. "Safe Exploration for Active Learning with Gaussian Process Models."

Hoang M. Le, Yisong Yue, & Peter Carr. "Smooth Imitation Learning."

Bo Zhang. "Machine Teaching via Simulation Optimization."

He He, Paul Mineiro, & Nikos Karampatziakis. "Active Information Acquisition."

Adish Singla, Sebastian Tschiatschek, & Anrdreas Krause. "Adaptive Sampling for Noisy Submodular Maximization with Applications to Crowdsourced Image Collection Summarization."

Theja Tulabandhula. "Learning Personalized Optimal Control for Repeatedly Operated Systems."

WORKSHOP TOPICS
How can machine learning be embedded into user technologies to actively guide sampling of data and discovery through interventions, while also automatically optimizing and personalizing for user outcomes?

For example:
> How do Massive Open Online Courses automatically improve over time to maximize student learning and enjoyment as data is collected?
> How can mobile apps minimize negative health behaviors like smoking and overeating by testing what interventions work for which people?
> What machine learning methods have been successful versus ineffective in industry applications that trade off exploration and exploitation, such as personalized search and recommendations?

A great deal of machine learning already addresses the 'big data' collected from people's use of online technologies, from well known Internet companies to novel settings like online education and health apps.

But far less research has analyzed how algorithms can learn about the world more effectively by adapting online user technologies to make decisions about what data is collected, how experiments for causal discovery are conducted, and how to negotiate tradeoffs between exploration versus exploitation in real-time and with limited computational resources.

Algorithms in real-world technologies must also optimize outcomes for users by adapting and personalizing how technology interacts with people. Ideally, algorithms instantiated in adaptive technologies appropriately trade off exploration – drawing inferences and guiding their learning in real time to do more effective learning and discovery – with exploitation – immediately deploying what is dynamically learned from resource bounded computations to optimize user outcomes through technology personalization.

Themes of the workshop will include:
> Generalizations and applications of reinforcement learning for real-time policy learning
> Contextual and multi-armed bandits for active experimentation and personalization
> Selecting and distributing interventions for causal discovery, optimal experimental design
> Active, Online, Sequential Machine Learning
> Interactive machine learning

Around the common theme of algorithms that learn from intervening and collecting data in real-world large-scale online technologies, the workshop will bring together researchers in machine learning as well as statistics, human-computer interaction, education, health, and cognitive science. This allows sharing of critical knowledge about how online technologies can be designed in a way that advances machine learning research, as well as extending the ties between applications of machine learning to online websites and services.