Sampling from Probabilistic Submodular Models
暂无分享,去创建一个
[1] Martin E. Dyer,et al. Path coupling: A technique for proving rapid mixing in Markov chains , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.
[2] Éva Tardos,et al. Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..
[3] Eric Vigoda,et al. A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries , 2001, STOC '01.
[4] V. Climenhaga. Markov chains and mixing times , 2013 .
[5] 藤重 悟. Submodular functions and optimization , 1991 .
[6] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[7] Hui Lin,et al. A Class of Submodular Functions for Document Summarization , 2011, ACL.
[8] Ove Granstrand,et al. Innovation and Intellectual Property Rights , 2006 .
[9] Martin E. Dyer,et al. On Markov Chains for Independent Sets , 2000, J. Algorithms.
[10] Andreas Krause,et al. Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization , 2010, J. Artif. Intell. Res..
[11] Sunil Kanwar,et al. Innovation and Intellectual Property Rights , 2006 .
[12] Gérard Cornuéjols,et al. Submodular set functions, matroids and the greedy algorithm: Tight worst-case bounds and some generalizations of the Rado-Edmonds theorem , 1984, Discret. Appl. Math..
[13] Andreas Krause,et al. From MAP to Marginals: Variational Inference in Bayesian Submodular Models , 2014, NIPS.
[14] Andreas Krause,et al. Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks , 2008 .
[15] Martin E. Dyer,et al. Matrix norms and rapid mixing for spin systems , 2007, ArXiv.
[16] C. Guestrin,et al. Near-optimal sensor placements: maximizing information while minimizing communication cost , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.
[17] Bimal Kumar Roy,et al. Counting, sampling and integrating: Algorithms and complexity , 2013 .
[18] Alistair Sinclair,et al. Improved Bounds for Mixing Rates of Marked Chains and Multicommodity Flow , 1992, LATIN.
[19] Mark Jerrum,et al. Polynomial-Time Approximation Algorithms for the Ising Model , 1990, SIAM J. Comput..
[20] Vahab Mirrokni,et al. Maximizing Non-Monotone Submodular Functions , 2007, FOCS 2007.
[21] Rishabh K. Iyer,et al. Submodular Point Processes with Applications to Machine learning , 2015, AISTATS.
[22] M. L. Fisher,et al. An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..
[23] D. Aldous. Random walks on finite groups and rapidly mixing markov chains , 1983 .
[24] P. Diaconis,et al. Geometric Bounds for Eigenvalues of Markov Chains , 1991 .
[25] Vahab S. Mirrokni,et al. Maximizing Non-Monotone Submodular Functions , 2011, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).
[26] Alistair Sinclair,et al. Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow , 1992, Combinatorics, Probability and Computing.
[27] Amin Karbasi,et al. Fast Mixing for Discrete Point Processes , 2015, COLT.
[28] Martin E. Dyer,et al. Beating the 2Δ bound for approximately counting colourings: a computer-assisted proof of rapid mixing , 1998, SODA '98.
[29] Mark Jerrum,et al. A Very Simple Algorithm for Estimating the Number of k-Colorings of a Low-Degree Graph , 1995, Random Struct. Algorithms.
[30] Ben Taskar,et al. Determinantal Point Processes for Machine Learning , 2012, Found. Trends Mach. Learn..
[31] Eric Vigoda,et al. A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries , 2004, JACM.
[32] Mark Jerrum,et al. Approximating the Permanent , 1989, SIAM J. Comput..