9:00-9:15  Opening Remarks (video)

9:15-10:00  Pedro Domingos     Keynote: Learning Tractable but Expressive Models

10:00-10:15  Rongjing Xiang*        On the Mismatch Between Learning and Inference for Single Network Domains

10:15-10:30  Rajhans Samdani     A Framework for Tuning Posterior Entropy in Unsupervised Learning

10:30-11:15  Poster Session (+ Coffee)

11:15-12:00  Tamir Hazan             Keynote: Modeling, Inference and Estimation using Random MAP Perturbations

12:00-12:15  He He                         Cost-sensitive Dynamic Feature Selection

14:00-14:45  David Sontag            Keynote: Structured Prediction using Linear Programming Relaxations

14:45-15:00  David Belanger          Speeding up MAP with Column Generation and Block Regularization

15:00-15:15  Peter Gehler              Learning Search Based Inference for Object Detection

15:15-15:30   Veselin Stoyanov**    Fast and Accurate Prediction via Evidence-Specific MRF Structure

15:30-16:15  Poster Session (+ Coffee)

16:15-17:00  Jason Eisner            Keynote: Learning Approximate Inference Policies for Fast Prediction

17:00-17:15  Filip Korc                    Approximating Marginals Using Discrete Energy Minimization

17:15-18:00  Justin Domke           Keynote: Marginalization-Based Parameter Learning in Graphical Models

18:00-18:15  Jiarong Jiang             Learned Prioritization for Trading Off Accuracy and Speed

18:15-18:30  Closing Remarks