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We study the problem of learning a good search policy for combinatorial search spaces.
On the shortest spanning subtree of a graph and the traveling salesman problem
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Lawler, E. L. and Wood, D. E · 1966
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A formal basis for the heuristic determination of minimum cost paths
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Learning evaluation functions for global optimization and boolean satisfiability
Boyan, J. A. and Moore, A. W · 1998
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 1998
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The use of discrete moment bounds in probabilistic constrained stochastic programming models
Prékopa, A · 1999
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Optimal solutions for multi-unit combinatorial auctions: Branch and bound heuristics
Gonen, R. and Lehmann, D · 2000
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A lagrangian heuristic based branch-and-bound approach for the capacitated network design problem
Holmberg, K. and Yuan, D · 2000
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Towards a universal test suite for combinatorial auction algorithms
Leyton-Brown, K., Pearson, M., and Shoham, Y · 2000
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Mixed integer programming for multi-vehicle path planning
Schouwenaars, T., DeMoor, B., Feron, E., and How, J · 2001
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Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Hullender, G · 2005
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Self-optimizing memory controllers: A reinforcement learning approach
Ipek, E., Mutlu, O., Martínez, J. F., and Caruana, R · 2008
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SCIP: solving constraint integer programs
Achterberg, T · 2009
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Search-based structured prediction
Daumé III, H., Langford, J., and Marcu, D · 2009
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Automated configuration of mixed integer programming solvers
Hutter, F., Hoos, H., and Leyton-Brown, K · 2010
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Learning policies for contextual submodular prediction
Ross, S., Zhou, J., Yue, Y., Dey, D., and Bagnell, J. A · 2013
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A supervised machine learning approach to variable branching in branch-and-bound
Alvarez, R. M., Louveaux, Q., and Wehenkel, L · 2014
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Learning to search in branch and bound algorithms
He, H., Daume III, H., and Eisner, J. M · 2014
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A human computation framework for boosting combinatorial solvers
Le Bras, R., Xue, Y., Bernstein, R., Gomes, C. P., and Selman, B · 2014
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Learning to search better than your teacher
Chang, K.-W., Krishnamurthy, A., Agarwal, A., Daume, H., and Langford, J · 2015
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LETOR : A Benchmark Collection for Research on Learning to Rank for Information Retrieval
Qin, T., Jun, T.-y. L., and Hang, X · 2010
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Efficient reductions for imitation learning
Ross, S. and Bagnell, D · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, J. A · 2011
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Rectifier nonlinearities improve neural network acoustic models
Maas, A. L., Hannun, A. Y., and Ng, A. Y · 2013
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Probabilistic planning for continuous dynamic systems under bounded risk
Ono, M., Williams, B. C., and Blackmore, L · 2013
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Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
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Learning to branch in mixed integer programming
Khalil, E. B., Le Bodic, P., Song, L., Nemhauser, G. L., and Dilkina, B. N · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Device placement optimization with reinforcement learning
Mirhoseini, A., Pham, H., Le, Q. V., Steiner, B., Larsen, R., Zhou, Y., Kumar, N., Norouzi, M., Bengio, S., and Dean, J · 2017
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Learning to solve smt formulas
Balunovic, M., Bielik, P., and Vechev, M · 2018
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