Papers
Deep Learning for Efficient Discriminative Parsing
Ronan Collobert
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Ng, Honglak Lee
Contextual Bandits with Linear Payoff Functions
Wei Chu, Lihong Li, Lev Reyzin et al.
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Edward Challis, David Barber
Switch-Reset Models : Exact and Approximate Inference
Chris Bracegirdle, David Barber
Relative Entropy Inverse Reinforcement Learning
Abdeslam Boularias, Jens Kober, Jan Peters
Domain Adaptation with Coupled Subspaces
John Blitzer, Sham Kakade, Dean Foster
Deep Learners Benefit More from Out-of-Distribution Examples
Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron et al.
Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach
Gowtham Bellala, Suresh Bhavnani, Clayton Scott
Tighter Relaxations for MAP-MRF Inference: A Local Primal-Dual Gap based Separation Algorithm
Dhruv Batra, Sebastian Nowozin, Pushmeet Kohli
Unsupervised Supervised Learning II: Margin-Based Classification without Labels
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
Statistical Optimization of Non-Negative Matrix Factorization
Anoop Korattikara Balan, Levi Boyles, Max Welling et al.
Dynamic Policy Programming with Function Approximation
Mohammad Gheshlaghi Azar, Vicenç Gómez, Bert Kappen
Polytope samplers for inference in ill-posed inverse problems
Edoardo Airoldi, Bertrand Haas
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text
Amr Ahmed, Qirong Ho, Choon Hui Teo et al.
Linear-Time Estimators for Propensity Scores
Deepak Agarwal, Lihong Li, Alexander Smola
Generative Kernels for Exponential Families
Arvind Agarwal, Hal Daumé III
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling
John Paisley, Chong Wang, David Blei
Discussion of “A conditional game for comparing approximations”
Vincent Conitzer
A conditional game for comparing approximations
Frederik Eaton
Discussion of “Spectral Dimensionality Reduction via Maximum Entropy”
Laurens van der Maaten
Spectral Dimensionality Reduction via Maximum Entropy
Neil Lawrence
Learning Scale Free Networks by Reweighted ℓ_1 regularization
Qiang Liu, Alexander Ihler