Papers
Approachability, fast and slow
Vianney Perchet, Shie Mannor
Appropriately Incorporating Statistical Significance in PMI
Om P. Damani, Shweta Ghonge
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
Vikash K Mansinghka, Tejas D Kulkarni, Yura N Perov et al.
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris
Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
Approximate Gaussian process inference for the drift function in stochastic differential equations
Andreas Ruttor, Philipp Batz, Manfred Opper
Approximate Inference in Collective Graphical Models
Daniel Sheldon, Tao Sun, Akshat Kumar et al.
Approximate Inference in Continuous Determinantal Processes
Raja Hafiz Affandi, Emily B. Fox, Ben Taskar
Approximate inference in latent Gaussian-Markov models from continuous time observations
Botond Cseke, Manfred Opper, Guido Sanguinetti
Approximate PCFG Parsing Using Tensor Decomposition
Shay B. Cohen, Giorgio Satta, Michael Collins
Approximate Representations for Multi-Robot Control Policies that Maximize Mutual Information
Benjamin Charrow, Vijay Kumar, Nathan Michael
Approximating the Permanent with Fractional Belief Propagation
Michael Chertkov, Adam B. Yedidia
Approximation Algorithms for Max-Sum-Product Problems
Denis Deratani Mauá
Approximation properties of DBNs with binary hidden units and real-valued visible units
Oswin Krause, Asja Fischer, Tobias Glasmachers et al.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Sanjeev Arora, Rong Ge, Yonatan Halpern et al.
A Practical Rank-Constrained Eight-Point Algorithm for Fundamental Matrix Estimation
Yinqiang Zheng, Shigeki Sugimoto, Masatoshi Okutomi
A Practical Transfer Learning Algorithm for Face Verification
Xudong Cao, David Wipf, Fang Wen et al.
A Principled Deep Random Field Model for Image Segmentation
Pushmeet Kohli, Anton Osokin, Stefanie Jegelka
A Probabilistic Approach to Latent Cluster Analysis
Zhipeng Xie, Rui Dong, Zhengheng Deng et al.
A Proof-Theoretical View of Collective Rationality
Daniele Porello
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions
Quoc Tran Dinh, Anastasios Kyrillidis, Volkan Cevher
A Quantum-Theoretic Approach to Distributional Semantics
William Blacoe, Elham Kashefi, Mirella Lapata
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning
Arash Afkanpour, András György, Csaba Szepesvari et al.
A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation
Zhenhua Tian, Hengheng Xiang, Ziqi Liu et al.