## 09 Dec learning heuristics over large graphs via deep reinforcement learning

[ (in) -251.016 (a) -249.99 (series) -250.989 (of) -249.98 (w) 9.99607 (ork\054) -250.998 (reinforcement) -250.002 (learning) -250.998 (techniques) -249.988 (were) ] TJ /R9 cs 11.9551 TL /I true (\054) Tj Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network. (27) Tj 8 0 obj 0.98 0 0 1 308.862 359.052 Tm /R12 9.9626 Tf Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion Learning a Decision Module by Imitating Driver’s Control Behaviors BT /ExtGState 472 0 R /Type /Page • [ (of) -250.016 (the) -250.987 (potentials\056) -312.015 (W) 91.9821 (e) -250.013 (show) -250.994 (compelling) -250.012 (r) 37.0181 (esults) -251.009 (on) -249.993 (the) -250.986 (P) 80.012 (ascal) ] TJ 1 0 0 1 504.832 514.469 Tm /Font 476 0 R /Type /Page ET To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. 1.014 0 0 1 50.1121 104.91 Tm (\135\072) Tj Q 14 0 obj Learning heuristics over large graphs via deep reinforcement learning. /Parent 1 0 R “Deep Exploration via Bootstrapped DQN”. 77.262 5.789 m << -102.617 -37.8578 Td Q BT 10 0 0 10 0 0 cm 1.02 0 0 1 308.862 514.469 Tm /Font 135 0 R /Font 340 0 R 1.017 0 0 1 308.862 490.559 Tm [ (an) -249.997 (inference) -250.004 (task) -249.984 (which) -249.982 (is) -249.984 (of) -249.996 (combinatorial) -249.993 (comple) 14.9975 (xity) 64.9941 (\056) ] TJ q BT (5) Tj Sayan Ranu /R18 9.9626 Tf /Pages 1 0 R “Learning to Perform Physics Experiments via Deep Reinforcement Learning”. Q 10 0 0 10 0 0 cm This year’s focus is on “Beyond Supervised Learning” with four theme areas: causality, transfer learning, graph mining, and reinforcement learning. /Rotate 0 /ColorSpace 43 0 R 0 scn 0.98 0 0 1 320.817 333.6 Tm /Type /Page '�K����]G�«��Z��xO#q*���k. [ (tional) -249.002 (Random) -249.996 (F) 45.9882 (ields) -249.018 (\050CRFs\051) -248.984 (to) -249 (pr) 45.003 (oduce) -249.016 (a) -249.016 (structur) 37.9914 (ed) -249.998 (output) ] TJ 1.02 0 0 1 320.817 200.552 Tm 1.014 0 0 1 390.791 382.963 Tm 1 0 0 1 370.826 382.963 Tm /ca 1 11.9563 TL Learning Heuristics over Large Graphs via Deep Reinforcement Learning Akash Mittal 1, Anuj Dhawan , Sourav Medya2, Sayan Ranu1, Ambuj Singh2 1Indian Institute of Technology Delhi 2University of California, Santa Barbara 1 fcs1150208, Anuj.Dhawan.cs115, sayanranu g@cse.iitd.ac.in , 2 medya, ambuj @cs.ucsb.edu Abstract In this paper, we propose a deep reinforcement At KDD 2020, Deep Learning Day is a plenary event that is dedicated to providing a clear, wide overview of recent developments in deep learning. free scheduling is competitive against widely-used heuristics like SuperMemo and the Leitner system on various learning objectives and student models. BT [ (ming) -285.016 (\050LP\051) -284.986 (relaxation) -284.983 (and) -285.007 (a) -284.982 (branch\055and\055bound) -285.991 (frame) 25.003 (w) 10.0089 (ork\056) ] TJ /R12 9.9626 Tf To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. /Title (Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning\077) [ (P) 14.9905 (articularly) -291.995 (for) -291.004 (lar) 16.9954 (ge) -291.011 (problems\054) -303.987 (repeated) -291.01 (solving) -291.983 (of) -290.996 (linear) ] TJ 0.99 0 0 1 62.0672 308.148 Tm endobj 1 0 0 1 530.325 514.469 Tm -196.573 -41.0457 Td • /R9 40 0 R BT f [ (v) 14.9989 (elop) -246.98 (a) -247.004 (ne) 24.9876 (w) -246.992 (frame) 25.0142 (w) 8.99108 (ork) -245.982 (for) -247 (higher) -246.98 (order) -247.004 (CRF) -247.014 (inference) -246.98 (for) ] TJ 1.016 0 0 1 308.862 140.776 Tm /R10 11.9552 Tf In this paper, we propose a framework called GCOMB to bridge these gaps. /CA 1 Anuj Dhawan 1 0 0 1 479.338 514.469 Tm BT >> Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality. T* T* endobj << ET 10 0 0 10 0 0 cm q /Author (Safa Messaoud\054 Maghav Kumar\054 Alexander G\056 Schwing) [ (the) -250.986 (task) -251.987 (of) -251.011 (semantic) -251.995 (se) 15.9977 (gmentation) -252 (using) -250.989 (a) -251.98 (Mark) 10.0094 (o) 16 (v) -251.995 (Decision) ] TJ BT /Contents 310 0 R /S /Transparency q (\100illinois\056edu) Tj [ (we) -254.018 (can) -254.003 (learn) -254.013 (heuristics) -253.995 (to) -253.99 (address) -254.003 (graphical) -253.988 (model) -254.003 (inference) ] TJ q 10 0 0 10 0 0 cm 9 0 obj [ (mantic) -349.997 (patterns\056) -619.005 (It) -350.009 (is) -350.016 (therefore) -350.009 (concei) 24.0012 (v) 24.991 (able) -351.004 (that) -350.018 (learning) ] TJ (\054) Tj [ (tasks) -208.995 (ef) 17.9961 <026369656e746c79> -209.988 (without) -208.989 (imposing) -208.984 (any) -209.985 (constr) 15.9812 (aints) -209.981 (on) -209.001 (the) -210.014 (form) ] TJ [ (deep) -249.995 (net) -249.99 (guided) -250.015 (Monte) -250.012 (Carlo) -250.017 (T) 35.0187 (ree) -250.007 (Search) -249.993 (\050MCTS\051) -250.002 (\133) ] TJ We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, capturing the properties of a node in the context of its graph … h Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. 7 0 obj 0 1 0 scn 78.598 10.082 79.828 10.555 80.832 11.348 c BT /MediaBox [ 0 0 612 792 ] /R12 9.9626 Tf 1.02 0 0 1 308.862 478.604 Tm 100.875 14.996 l T* /ExtGState 300 0 R [ (construction) -251.014 (for) -251.012 (each) -251.015 (problem\056) -311.998 (Seemingly) -251.011 (easier) -250.991 (to) -250.984 (de) 24.9914 (v) 15.0141 (elop) ] TJ Q Add a /Type /XObject Finally, [14,17] leverage deep Reinforcement Learning techniques to learn a class of graph greedy optimization heuristics on fully observed networks. /R12 9.9626 Tf /Parent 1 0 R 10 0 0 10 0 0 cm Q [ (is) -341.982 (more) -340.987 (ef) 23.9916 <02> 1 (cient) -342.008 (than) -341.016 (traditional) -342.004 (approaches) -340.985 (as) -342.004 (inference) ] TJ ET ET /ProcSet [ /PDF /Text ] << 11.9551 TL Q << /a0 gs /Group << 105.816 18.547 l << [17] Ian Osband, et al. /MediaBox [ 0 0 612 792 ] Q /R9 cs BT • /R12 9.9626 Tf << /R14 8.9664 Tf /R21 cs 0 scn GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. >> T* /R14 31 0 R /Annots [ ] 0 scn q 1 0 0 1 395.813 382.963 Tm /R16 35 0 R 1 Introduction The ability to learn and retain a large number of new pieces of information is an essential component of human education. [ (based) -247.012 (higher) -247.014 (order) -246.983 (potentials) -246.983 (that) -246.987 (result) -247.007 (in) -247.002 (computationally) ] TJ >> 1 0 0 -1 0 792 cm [ (accurate) -285.006 (deep) -284.994 (net) -284.015 (models\054) -294.991 (challenges) -285.015 (such) -284.985 (as) -285 (inconsistent) ] TJ 0.994 0 0 1 308.862 249.914 Tm endobj << q 0 scn endobj it is much more effective for a learning algorithm to sift through large amounts of sample problems. q 0 scn [ (al) 10.0089 (w) 10.0089 (ays) -249.012 (deals) -249 (with) -248.997 (similarly) -248.017 (sized) -248.997 (problem) -248.988 (structures) -248.988 (or) -248.017 (se\055) ] TJ ET 78.059 15.016 m 0.994 0 0 1 50.1121 92.9551 Tm 2. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). [ (are) -247.006 (heuristics) -246.991 (which) -247.988 (are) -247.006 (generally) -247.004 (computationally) -247.991 (f) 10.0172 (ast) -246.989 (b) 19.9885 (ut) ] TJ /ColorSpace 133 0 R In the simulation part, the proposed method is compared with the optimal power flow method. 2 0 obj 0 1 0 scn 10 0 obj /Producer (PyPDF2) /Resources << 78.059 15.016 m /Rotate 0 -11.7207 -11.9559 Td BT /R21 cs 1.014 0 0 1 308.862 442.738 Tm Deep ReInforcement learning for Functional software-Testing. /Rotate 0 Q >> 6 0 obj ICLR 2017. /MediaBox [ 0 0 612 792 ] /ProcSet [ /PDF /Text ] /R9 cs /R21 38 0 R -10.5379 -13.9477 Td /R9 cs T* 0.983 0 0 1 308.862 164.686 Tm ET >> q 0.6082 -20.0199 Td q BT 0.98 0 0 1 50.1121 490.559 Tm Algorithm representation. Sahil Manchanda 1.004 0 0 1 50.1121 454.694 Tm Q 100.875 9.465 l 1.017 0 0 1 308.503 430.783 Tm q /ProcSet [ /PDF /ImageC /Text ] Q (i\056e) Tj (6) Tj [ (Graphical) -254.002 (model) -253.987 (inference) -253.986 (is) -252.989 (an) -254.018 (important) -253.981 (combinatorial) ] TJ << /Contents 477 0 R (18) Tj We use the tree-structured symbolic representation of the GUI as the state, modelling a generalizeable Q-function with Graph Neural Networks (GNN). /R12 9.9626 Tf 100.875 27.707 l /Type /Page [ (The) -343.991 (proposed) -344.019 (approach) -343.983 (has) -343.998 (tw) 10.0089 (o) -344.997 (main) -344.017 (adv) 25.015 (antages\072) -501.992 (\0501\051) ] TJ 1.014 0 0 1 415.778 382.963 Tm The resulting algorithm can learn new state of the art heuristics for graph coloring. /R21 cs /R9 cs [5] [6] use fully convolutional neural networks to approximate reward functions. [ (se) 39.0145 (gmentation\054) -311.016 (human) -298.988 (pose) -298.017 (estimation) -298.999 (and) -298.009 (action) -298.994 (r) 37.0012 (eco) 9.98968 (gni\055) ] TJ 3 0 obj /MediaBox [ 0 0 612 792 ] /R9 cs 10 0 0 10 0 0 cm • 0 scn endobj [ (rial) -249.012 (algorithm\056) -314.005 (F) 14.9917 (or) -249.019 (instance\054) -248.992 (semantic) -249.017 (image) -248.017 (se) 13.9923 (gmentation) ] TJ (82) Tj 105.816 14.996 l We ... Conﬂict analysis adds new clauses over time, which cuts off large parts of … >> /R18 9.9626 Tf ET /R12 9.9626 Tf [ (bounding) -269.998 (box) -268.986 (detection\054) -275.996 (se) 14.9893 (gmentation) -268.986 (or) -270.007 (image) -269.003 <636c617373690263612d> ] TJ Q [ (tion\054) -226.994 (pr) 46.0032 (o) 10.0055 (gr) 15.9962 (ams) -219.988 (ar) 38.0014 (e) -219.995 (formulated) -218.995 (for) -220.004 (solving) -220.004 (infer) 38.0089 (ence) -218.999 (in) -219.994 (Condi\055) ] TJ >> 1.007 0 0 1 50.1121 382.963 Tm /ProcSet [ /PDF /Text ] [ (intractable) -246.989 (classical) -246.989 (inference) -246.992 (approaches\056) -307.006 (\0502\051) -246.996 (Our) -247.001 (method) ] TJ >> /Type /Page >> /Rotate 0 [ (or) 36.009 (der) -263.005 (potenti) 0.99344 (als\056) -357.983 (In) -262.012 (this) -262.981 (paper) 108.996 (\054) -267.983 (we) -262.012 (show) -262.99 (that) -262.997 (we) -263.011 (can) -262.982 (learn) ] TJ Jihun Oh, Kyunghyun Cho and Joan Bruna; Dismantle Large Networks through Deep Reinforcement Learning. /R9 cs /Type /Pages [ (V) 29.9987 (OC) -249.982 (and) -249.982 (MO) 39.9982 (TS) -250.017 (datasets\056) ] TJ 1.02 0 0 1 308.862 128.821 Tm [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (of) -250.014 (Illinois) -250.008 (at) -249.987 (Urbana\055Champaign) ] TJ /MediaBox [ 0 0 612 792 ] q We focus on ... We address the problem of automatically learning better heuristics for a given set of formulas. (85) Tj /Resources << [ (A) -229.981 (fourth) -230.984 (paradigm) -230.014 (has) -231.004 (been) -230.014 (considered) -229.984 (since) -231.014 (the) -230.019 (early) -229.999 (2000s) ] TJ /Contents 399 0 R q << BT /Subtype /Form 1.02 0 0 1 50.1121 176.641 Tm >> 10 0 0 10 0 0 cm 96.422 5.812 m /Length 42814 The challenge in going from 2000 to 2018 is to scale up inverse reinforcement learning methods to work with deep learning systems. 0.44706 0.57647 0.77255 rg /ExtGState 397 0 R 0.996 0 0 1 308.862 406.873 Tm Q /R12 9.9626 Tf 0 1 0 scn [ (pr) 44.0046 (oximation) -265.993 (methods) -266.016 (ar) 36.009 (e) -265.993 (computationally) -266 (demanding) -266.017 (and) ] TJ [ (Program) -316.003 (\050ILP\051) -316.016 (using) -315.016 (a) -316.004 (combination) -315.992 (of) -315.982 (a) -316.004 (Linear) -315.002 (Program\055) ] TJ 16 0 obj -11.721 -11.9551 Td /MediaBox [ 0 0 612 792 ] [ (de) 24.9818 (v) 13.9857 (eloped) -247 (\133) ] TJ 10 0 0 10 0 0 cm [ (messaou2\054) -600.005 (mkumar10\054) -600.005 (aschwing) ] TJ << /Resources << In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. /ExtGState 129 0 R >> /Resources << ET >> Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini; Differentiable Physics-informed Graph Networks. 71.715 5.789 67.215 10.68 67.215 16.707 c /Parent 1 0 R Q << >> 03/08/2019 ∙ by Akash Mittal, et al. /R9 cs (\054) Tj ET /x6 16 0 R The comparison of the simulation results shows that the proposed method has better performance than the optimal power flow solution. Human-level control through deep reinforcement learning. [ (Conditional) -239.997 (Random) -240.006 (Fields) -239.986 (\050CRFs\051\054) -244.002 (albe) 1.01274 (it) -240.986 (requiring) -239.991 (to) -239.998 (solv) 15.016 (e) ] TJ /R12 9.9626 Tf ] Ian Osband, John Aslanides & … learning heuristics over large graphs deep... [ 6 ] use fully Convolutional neural networks to approximate reward functions, called (! Component of human education novel Batch Reinforcement learning ” a Q-learning framework, which is efficient! Quality than state-of-the-art algorithms for learning combinatorial algorithms the efficiency and efficacy of.! Network ( GCN ) using a novel probabilistic greedy mechanism to predict quality... Issue in societal and sociotechnical networks GPU Memory Limit via Smart Swapping extensive experiments on graphs... Jointly trained with the graph-aware decoder using deep Reinforcement learning for combinatorial problems on graphs through machine.... 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