Papers
Blending Autonomous Exploration and Apprenticeship Learning
Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison
Block-sparse Solutions using Kernel Block RIP and its Application to Group Lasso
Rahul Garg, Rohit Khandekar
Boosting with Maximum Adaptive Sampling
Charles Dubout, Francois Fleuret
Bounds on Individual Risk for Log-loss Predictors
Peter D. Grünwald, Wojciech Kotłowski
Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality
Shuang–Hong Yang, Steven P. Crain, Hongyuan Zha
Budgeted Optimization with Concurrent Stochastic-Duration Experiments
Javad Azimi, Alan Fern, Xiaoli Z. Fern
CAKE: Convex Adaptive Kernel Density Estimation
Ravi Sastry Ganti Mahapatruni, Alexander Gray
Can matrix coherence be efficiently and accurately estimated?
Mehryar Mohri, Ameet Talwalkar
CARP: Software for Fishing Out Good Clustering Algorithms
Volodymyr Melnykov, Ranjan Maitra
Clustered Multi-Task Learning Via Alternating Structure Optimization
Jiayu Zhou, Jianhui Chen, Jieping Ye
Clustering Algorithms for Chains
Antti Ukkonen
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery
Scott Niekum, Andrew G. Barto
Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing
Ohad Shamir, Shai Shalev-Shwartz
Collective Graphical Models
Daniel R. Sheldon, Thomas G. Dietterich
Collision-Free and Curvature-Continuous Path Smoothing In Cluttered Environments
Jia Pan, Liangjun Zhang, Dinesh Manocha
Committing Bandits
Loc X. Bui, Ramesh Johari, Shie Mannor
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
Armen Allahverdyan, Aram Galstyan
Comparing Heads-up, Hands-free Operation of Ground Robots to Teleoperation
Matthew Marge, Aaron Powers, Jonathan Brookshire et al.
Competitive Closeness Testing
Jayadev Acharya, Hirakendu Das, Ashkan Jafarpour et al.
Complexity-Based Approach to Calibration with Checking Rules
Dean P. Foster, Alexander Rakhlin, Karthik Sridharan et al.
Complexity of Inference in Latent Dirichlet Allocation
David Sontag, Dan Roy
Composite Multiclass Losses
Elodie Vernet, Mark D. Reid, Robert C. Williamson
Computationally Efficient Convolved Multiple Output Gaussian Processes
Mauricio A. Álvarez, Neil D. Lawrence
Computationally Efficient Sufficient Dimension Reduction via Squared-Loss Mutual Information
Makoto Yamada, Gang Niu, Jun Takagi et al.
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Edward Challis, David Barber