Publications

2016

Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, and Sriraam Natarajan,
Learning through Advice-Seeking via Transfer,
International Conference on Inductive Logic Programming (ILP), 2016.

Phillip Odom, and Sriraam Natarajan,
Actively Interacting with Experts: A Probabilistic Logic Approach ,
European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD) 2016. (Acceptance rate 28%)

Phillip Odom, and Sriraam Natarajan,
Active Advice Seeking for Inverse Reinforcement Learning,
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016. (Acceptance rate ~24.9%)

2015

Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, and Sriraam Natarajan,
Transfer Learning via Relational Type Matching,
International Conference on Data Mining (ICDM), 2015. (Acceptance rate 18.2%)

Phillip Odom, Vishal Bangera, Tushar Khot, David Page, and Sriraam Natarajan,
Extracting Adverse Drug Events from Text using Human Advice,
Artificial Intelligence in Medicine (AIME), 2015. (Acceptance rate 25% [long])

Phillip Odom, Tushar Khot, Reid Porter, and Sriraam Natarajan,
Knowledge-Based Probabilistic Logic Learning,
Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2015. (Acceptance rate 26.67%)

Phillip Odom, Tushar Khot, and Sriraam Natarajan,
Learning Probabilistic Logic Models with Human Advice,
AAAI Spring Symposium on Knowledge Representation and Reasoning, 2015.

Phillip Odom and Sriraam Natarajan,
Active Advice Seeking for Inverse Reinforcement Learning,
AAAI Student Abstract and Poster Program, 2015.

2013

Gautam Kunapuli, Phillip Odom, Jude Shavlik and Sriraam Natarajan,
Guiding Autonomous Agents to Better Behaviors through Human Advice,
IEEE International Conference on Data Mining (ICDM) 2013. (Acceptance rate 19.65%)

Sriraam Natarajan, Phillip Odom, Saket Joshi, Tushar Khot, Kristian Kersting and Prasad Tadepalli,
Accelarating Imitation Learning in Relational Domains via Transfer by Initialization,
International Conference on Inductive Logic Programming (ILP), 2013. (Acceptance rate 44.44% [long])