monte carlo

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monte carlo

Tag #monte carlo

183 papers:

AIIDE-2019-YangO #game studies #realtime

Guiding Monte Carlo Tree Search by Scripts in Real-Time Strategy Games (ZY, SO), pp. 100–107.

CoG-2019-ChoeK #game studies

Enhancing Monte Carlo Tree Search for Playing Hearthstone (JSBC, JKK), pp. 1–7.

CoG-2019-GreenwoodA #game studies

Monte Carlo Strategies for Exploiting Fairness in N-player Ultimatum Games (GWG, DA), pp. 1–7.

CoG-2019-KantharajuOG #recognition #scalability

Scaling up CCG-Based Plan Recognition via Monte-Carlo Tree Search (PK, SO, CWG), pp. 1–8.

CoG-2019-KiarostamiDMRG #multi

Multi-Agent non-Overlapping Pathfinding with Monte-Carlo Tree Search (MSK, MRD, SKM, DR, SG), pp. 1–4.

CoG-2019-SironiW #parametricity

Comparing Randomization Strategies for Search-Control Parameters in Monte-Carlo Tree Search (CFS, MHMW), pp. 1–8.

ICML-2019-ChenBBGGMO #markov

Stein Point Markov Chain Monte Carlo (WYC, AB, FXB, JG, MAG, LWM, CJO), pp. 1011–1021.

ICML-2019-ChoromanskiRCW #orthogonal

Unifying Orthogonal Monte Carlo Methods (KC, MR, WC, AW), pp. 1203–1212.

ICML-2019-GolinskiWR #integration

Amortized Monte Carlo Integration (AG, FW, TR), pp. 2309–2318.

ICML-2019-NguyenSR #analysis #optimisation

Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization (THN, US, GR), pp. 4810–4819.

ICML-2019-PolianskiiP #approach #bound #classification #geometry #integration

Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration (VP, FTP), pp. 5162–5170.

ICML-2019-ShestopaloffD

Replica Conditional Sequential Monte Carlo (AS, AD), pp. 5749–5757.

ICML-2019-ZhangZT #adaptation #multi #testing

Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits (MJZ, JZ, DT), pp. 7512–7522.

ASPLOS-2019-BanerjeeKI #algorithm #markov #modelling #probability

AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms for Probabilistic Models (SSB, ZTK, RKI), pp. 515–528.

SEFM-2018-LuckowPV #source code

Monte Carlo Tree Search for Finding Costly Paths in Programs (KSL, CSP, WV), pp. 123–138.

AIIDE-2018-HornMSC #approach #automation

A Monte Carlo Approach to Skill-Based Automated Playtesting (BH, JAM, GS, SC), pp. 166–172.

CIG-2018-GeddaLB #game studies

Monte Carlo Methods for the Game Kingdomino (MG, MZL, MB), pp. 1–8.

CIG-2018-IshiharaIIHT #behaviour #game studies #implementation

Monte-Carlo Tree Search for Implementation of Dynamic Difficulty Adjustment Fighting Game AIs Having Believable Behaviors (MI, SI, RI, TH, RT), pp. 1–8.

CIG-2018-IshiiIIHT #game studies #implementation

Monte-Carlo Tree Search Implementation of Fighting Game AIs Having Personas (RI, SI, MI, TH, RT), pp. 1–8.

CIG-2018-SironiW #adaptation #analysis #game studies #self #video

Analysis of Self-Adaptive Monte Carlo Tree Search in General Video Game Playing (CFS, MHMW), pp. 1–4.

ICML-2018-BuchholzWM

Quasi-Monte Carlo Variational Inference (AB, FW, SM), pp. 667–676.

ICML-2018-ChatterjiFMBJ #formal method #on the #probability #reduction

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo (NSC, NF, YAM, PLB, MIJ), pp. 763–772.

ICML-2018-FoersterFARXW #infinity #named

DiCE: The Infinitely Differentiable Monte Carlo Estimator (JNF, GF, MAS, TR, EPX, SW), pp. 1524–1533.

ICML-2018-RainforthCYW #on the

On Nesting Monte Carlo Estimators (TR, RC, HY, AW), pp. 4264–4273.

ICML-2018-ZouXG #probability

Stochastic Variance-Reduced Hamilton Monte Carlo Methods (DZ, PX0, QG), pp. 6023–6032.

AIIDE-2017-PowleyCW #bound #memory management

Memory Bounded Monte Carlo Tree Search (EJP, PIC, DW), pp. 94–100.

CIG-2017-DemediukTRZLM #algorithm

Monte Carlo tree search based algorithms for dynamic difficulty adjustment (SD, MT, WLR, FZ, XL0, F'M), pp. 53–59.

CIG-2017-IlhanE #game studies #learning #video

Monte Carlo tree search with temporal-difference learning for general video game playing (EI, ASEU), pp. 317–324.

CIG-2017-SantosSM

Monte Carlo tree search experiments in hearthstone (AS, PAS, FSM), pp. 272–279.

CIG-2017-SilvaVT #game studies #using

Using Monte Carlo tree search and google maps to improve game balancing in location-based games (LFMS, WV, FT), pp. 215–222.

ICML-2017-DinhBZM #probability

Probabilistic Path Hamiltonian Monte Carlo (VD, AB, CZ, FAMI), pp. 1009–1018.

ICML-2017-Hoffman #learning #markov #modelling

Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo (MDH), pp. 1510–1519.

ICML-2017-KattOA #learning

Learning in POMDPs with Monte Carlo Tree Search (SK, FAO, CA), pp. 1819–1827.

ICML-2017-Simsekli #difference #equation #markov #probability

Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo (US), pp. 3200–3209.

ICML-2017-TripuraneniRGT

Magnetic Hamiltonian Monte Carlo (NT, MR, ZG, RET), pp. 3453–3461.

PLDI-2017-HuangTM #algorithm #compilation #markov #modelling #probability

Compiling Markov chain Monte Carlo algorithms for probabilistic modeling (DH0, JBT, GM), pp. 111–125.

CADE-2017-FarberKU #proving

Monte Carlo Tableau Proof Search (MF0, CK, JU), pp. 563–579.

AIIDE-2016-DevlinASCR #game studies

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play (SD, AA, NS, PIC, JR), pp. 16–22.

AIIDE-2016-KartalSG #data-driven #generative

Data Driven Sokoban Puzzle Generation with Monte Carlo Tree Search (BK, NS, SJG), pp. 58–64.

AIIDE-2016-SooLC #using

Generate Believable Causal Plots with User Preferences Using Constrained Monte Carlo Tree Search (VWS, CML, THC), pp. 218–224.

AIIDE-2016-UriarteO #modelling #policy #probability

Improving Monte Carlo Tree Search Policies in StarCraft via Probabilistic Models Learned from Replay Data (AU, SO), pp. 100–106.

CIG-2016-GrafP #revisited #simulation

Monte-Carlo simulation balancing revisited (TG, MP), pp. 1–7.

CIG-2016-Ontanon #game studies #realtime

Informed Monte Carlo Tree Search for Real-Time Strategy games (SO), pp. 1–8.

CIG-2016-SoemersSSW #game studies #realtime #video

Enhancements for real-time Monte-Carlo Tree Search in General Video Game Playing (DJNJS, CFS, TS, MHMW), pp. 1–8.

CIG-2016-WaardRB #game studies #video

Monte Carlo Tree Search with options for general video game playing (MdW, DMR, SCJB), pp. 1–8.

ICML-2016-KhandelwalLNS #analysis #on the

On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search (PK, EL, SN, PS), pp. 1319–1328.

ICML-2016-MnihR

Variational Inference for Monte Carlo Objectives (AM, DJR), pp. 2188–2196.

ICML-2016-PaigeW #modelling #network #visual notation

Inference Networks for Sequential Monte Carlo in Graphical Models (BP, FDW), pp. 3040–3049.

ICML-2016-RainforthNLPMDW #markov

Interacting Particle Markov Chain Monte Carlo (TR, CAN, FL, BP, JWvdM, AD, FDW), pp. 2616–2625.

ICML-2016-RoychowdhuryKP #robust #using

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics (AR, BK, SP0), pp. 2673–2681.

ICML-2016-SimsekliBCR #probability

Stochastic Quasi-Newton Langevin Monte Carlo (US, RB, ATC, GR), pp. 642–651.

CIG-2015-ChangHH #algorithm #analysis #case study #convergence #correctness

Convergence and correctness analysis of Monte-Carlo tree search algorithms: A case study of 2 by 4 Chinese dark chess (HJC, CWH, TsH), pp. 260–266.

CIG-2015-ChuHGHT #algorithm #game studies #knowledge-based #video

Combining pathfmding algorithm with Knowledge-based Monte-Carlo tree search in general video game playing (CYC, HH, ZG, TH, RT), pp. 523–529.

CIG-2015-CowlingWP

Emergent bluffing and inference with Monte Carlo Tree Search (PIC, DW, EJP), pp. 114–121.

CIG-2015-FujikiIV #algorithm #comparison #framework #game studies #platform

A platform for turn-based strategy games, with a comparison of Monte-Carlo algorithms (TF, KI, SV), pp. 407–414.

CIG-2015-ImagawaK #algorithm #game studies

Enhancements in Monte Carlo tree search algorithms for biased game trees (TI, TK), pp. 43–50.

CIG-2015-IvanovoRZL #learning

Combining Monte Carlo tree search and apprenticeship learning for capture the flag (JI, WLR, FZ, XL0), pp. 154–161.

CIG-2015-LiuT

Regulation of exploration for simple regret minimization in Monte-Carlo tree search (YCL, YT), pp. 35–42.

CIG-2015-MizukamiT #modelling #simulation

Building a computer Mahjong player based on Monte Carlo simulation and opponent models (NM, YT), pp. 275–283.

CIG-2015-SatoIW #estimation #multi #using

Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method (NS, KI, TW), pp. 51–59.

CIG-2015-WangZLHW #game studies

Belief-state Monte-Carlo tree search for Phantom games (JW, TZ, HL, CHH, ICW), pp. 267–274.

CIG-2015-ZhuangLPZ #network #novel #representation

Improving Monte-Carlo tree search for dots-and-boxes with a novel board representation and artificial neural networks (YZ, SL, TVP, CZ), pp. 314–321.

FDG-2015-FischerFVTR

Monte-Carlo Tree Search for Simulated Car Racing (JF, NF, MV, JT, SR).

FDG-2015-ZookHR #analysis #game studies

Monte-Carlo Tree Search for Simulation-based Play Strategy Analysis (AZ, BH, MR).

ICML-2015-Betancourt #scalability

The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling (MB), pp. 533–540.

ICML-2015-ChaoSMS #exponential #integration

Exponential Integration for Hamiltonian Monte Carlo (WLC, JS, DM, FS), pp. 1142–1151.

ICML-2015-NaessethLS

Nested Sequential Monte Carlo Methods (CAN, FL, TBS), pp. 1292–1301.

ICML-2015-SalimansKW #markov

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap (TS, DPK, MW), pp. 1218–1226.

ICML-2015-WangFS #for free #privacy #probability

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (YXW, SEF, AJS), pp. 2493–2502.

SAC-2015-Rubio-MonteroPG #adaptation #evaluation #framework

Evaluation of an adaptive framework for resilient Monte Carlo executions (AJRM, MARP, RMG), pp. 448–455.

DATE-2015-AfacanBPDB #hybrid

A hybrid Quasi Monte Carlo method for yield aware analog circuit sizing tool (EA, GB, AEP, GD, IFB), pp. 1225–1228.

HPDC-2015-XiaoCHZ #cpu #gpu

Monte Carlo Based Ray Tracing in CPU-GPU Heterogeneous Systems and Applications in Radiation Therapy (KX, DZC, XSH, BZ), pp. 247–258.

CIG-2014-GrafP #graph

Common fate graph patterns in Monte Carlo Tree Search for computer go (TG, MP), pp. 1–8.

CIG-2014-KimYK #geometry #graph #representation #using

Solving Geometry Friends using Monte-Carlo Tree Search with directed graph representation (HTK, DMY, KJK), pp. 1–2.

CIG-2014-LanctotWPS #heuristic #using

Monte Carlo Tree Search with heuristic evaluations using implicit minimax backups (ML, MHMW, TP, NRS), pp. 1–8.

CIG-2014-SanseloneSSPD #game studies #using

Constrained control of non-playing characters using Monte Carlo Tree Search (MS, SS, CS, DP, YD), pp. 1–8.

CIG-2014-SarrattPJ #convergence

Converging to a player model in Monte-Carlo Tree Search (TS, DVP, AJ), pp. 1–7.

CIG-2014-SephtonCPS #game studies #heuristic

Heuristic move pruning in Monte Carlo Tree Search for the strategic card game Lords of War (NS, PIC, EJP, NHS), pp. 1–7.

CIG-2014-TakLW #game studies

Monte Carlo Tree Search variants for simultaneous move games (MJWT, ML, MHMW), pp. 1–8.

ICML-c1-2014-BardenetDH #adaptation #approach #markov #scalability #towards

Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach (RB, AD, CCH), pp. 405–413.

ICML-c1-2014-LanZS

Spherical Hamiltonian Monte Carlo for Constrained Target Distributions (SL, BZ, BS), pp. 629–637.

ICML-c1-2014-Sohl-DicksteinMD

Hamiltonian Monte Carlo Without Detailed Balance (JSD, MM, MRD), pp. 719–726.

ICML-c1-2014-YangSAM #invariant #kernel

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels (JY, VS, HA, MWM), pp. 485–493.

ICML-c2-2014-ChenFG #probability

Stochastic Gradient Hamiltonian Monte Carlo (TC, EBF, CG), pp. 1683–1691.

ICML-c2-2014-JunB #memory management #performance

Memory (and Time) Efficient Sequential Monte Carlo (SHJ, ABC), pp. 514–522.

ICML-c2-2014-NeufeldGSS #adaptation

Adaptive Monte Carlo via Bandit Allocation (JN, AG, CS, DS), pp. 1944–1952.

ICML-c2-2014-SuttonMPH #equivalence

A new Q(λ) with interim forward view and Monte Carlo equivalence (RSS, ARM, DP, HvH), pp. 568–576.

AIIDE-2013-WhitehouseCPR #game studies #knowledge-based #mobile

Integrating Monte Carlo Tree Search with Knowledge-Based Methods to Create Engaging Play in a Commercial Mobile Game (DW, PIC, EJP, JR).

CIG-2013-AlhejaliL #heuristic #programming #search-based #using

Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent (AMA, SML), pp. 1–8.

CIG-2013-BaierW

Monte-Carlo Tree Search and minimax hybrids (HB, MHMW), pp. 1–8.

CIG-2013-FurtakB #game studies #recursion

Recursive Monte Carlo search for imperfect information games (TF, MB), pp. 1–8.

CIG-2013-IkedaV

Production of various strategies and position control for Monte-Carlo Go - Entertaining human players (KI, SV), pp. 1–8.

CIG-2013-PerezSL #learning #multi #online

Online and offline learning in multi-objective Monte Carlo Tree Search (DPL, SS, SML), pp. 1–8.

CIG-2013-PowleyWC #policy #simulation

Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search (EJP, DW, PIC), pp. 1–8.

CIG-2013-PowleyWC13a #heuristic #multi #physics #problem

Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem (EJP, DW, PIC), pp. 1–8.

ICML-c1-2013-WilliamsonDX #markov #modelling #parallel #parametricity

Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models (SW, AD, EPX), pp. 98–106.

ICML-c2-2013-CarpentierM #integration #towards

Toward Optimal Stratification for Stratified Monte-Carlo Integration (AC, RM), pp. 28–36.

ICML-c3-2013-WangMF #adaptation

Adaptive Hamiltonian and Riemann Manifold Monte Carlo (ZW, SM, NdF), pp. 1462–1470.

CASE-2013-Geldmann #image #simulation #using

Fine registration of SEM and AFM images using Monte Carlo simulations (CG), pp. 813–818.

DATE-2013-SchryverTW #multi

A multi-level Monte Carlo FPGA accelerator for option pricing in the Heston model (CdS, PT, NW), pp. 248–253.

CIG-2012-BaierW

Beam Monte-Carlo Tree Search (HB, MHMW), pp. 227–233.

CIG-2012-NaveedKCCG

A Monte-Carlo path planner for dynamic and partially observable environments (MN0, DEK, AC, LC, PG), pp. 211–218.

CIG-2012-PepelsW

Enhancements for Monte-Carlo Tree Search in Ms Pac-Man (TP, MHMW), pp. 265–272.

CIG-2012-PerezRL

Monte Carlo Tree Search: Long-term versus short-term planning (DPL, PR, SML), pp. 219–226.

CIG-2012-PerickSME #comparison #game studies

Comparison of different selection strategies in Monte-Carlo Tree Search for the game of Tron (PP, DLSP, FM, DE), pp. 242–249.

CIG-2012-PowleyWC #heuristic #physics #problem

Monte Carlo Tree Search with macro-actions and heuristic route planning for the Physical Travelling Salesman Problem (EJP, DW, PIC), pp. 234–241.

CIKM-2012-EmrichKNRSZ #graph #nondeterminism #probability #query

Exploration of monte-carlo based probabilistic query processing in uncertain graphs (TE, HPK, JN, MR, AS, AZ), pp. 2728–2730.

ICML-2012-WangWHL #learning

Monte Carlo Bayesian Reinforcement Learning (YW, KSW, DH, WSL), p. 105.

ICPR-2012-WangL12a

Hamiltonian Monte Carlo estimator for abrupt motion tracking (FW, ML), pp. 3066–3069.

SIGIR-2012-Cummins #modelling #performance #predict #simulation #using

Investigating performance predictors using monte carlo simulation and score distribution models (RC), pp. 1097–1098.

DAC-2012-KuoHCKC #design #performance

Efficient trimmed-sample Monte Carlo methodology and yield-aware design flow for analog circuits (CCK, WYH, YHC, JFK, YKC), pp. 1113–1118.

CIG-2011-IkehataI

Monte-Carlo tree search in Ms. Pac-Man (NI, TI), pp. 39–46.

CIG-2011-NijssenW #game studies

Monte-Carlo Tree Search for the game of Scotland Yard (J(AMN, MHMW), pp. 158–165.

CIG-2011-RoblesRL #game studies #learning

Learning non-random moves for playing Othello: Improving Monte Carlo Tree Search (DR, PR, SML), pp. 305–312.

CIG-2011-TongMS #approach

A Monte-Carlo approach for the endgame of Ms. Pac-Man (BKBT, CMM, CWS), pp. 9–15.

CIG-2011-WhitehousePC #game studies #set

Determinization and information set Monte Carlo Tree Search for the card game Dou Di Zhu (DW, EJP, PIC), pp. 87–94.

CIG-2011-WinandsB

αβ-based play-outs in Monte-Carlo Tree Search (MHMW, YB), pp. 110–117.

DAC-2011-GongYH #analysis #orthogonal #performance #probability

Fast non-monte-carlo transient noise analysis for high-precision analog/RF circuits by stochastic orthogonal polynomials (FG, HY, LH), pp. 298–303.

DAC-2011-IqbalSH #dependence #fault #named #power management #probability #scheduling

SEAL: soft error aware low power scheduling by Monte Carlo state space under the influence of stochastic spatial and temporal dependencies (NI, MAS, JH), pp. 134–139.

DATE-2011-MerrettAWZRMRLFA #analysis #modelling #performance #statistics #variability

Modelling circuit performance variations due to statistical variability: Monte Carlo static timing analysis (MM, PA, YW, MZ, DR, CM, SR, ZL, SBF, AA), pp. 1537–1540.

PDP-2011-PascualRMBCL #case study #performance

More Efficient Executions of Monte Carlo Fusion Codes by Means of Montera: The ISDEP Use Case (MARP, AJRM, RM, AB, FC, IML), pp. 380–384.

FASE-2011-OudinetDGLP #model checking

Uniform Monte-Carlo Model Checking (JO, AD, MCG, RL, SP), pp. 127–140.

AIIDE-2010-LaviersS #approach #game studies #generative

A Monte Carlo Approach for Football Play Generation (KRL, GS).

CIG-2010-TeytaudT #algorithm #on the

On the huge benefit of decisive moves in Monte-Carlo Tree Search algorithms (FT, OT), pp. 359–364.

ICPR-2010-KimL #markov #optimisation #random #using

Continuous Markov Random Field Optimization Using Fusion Move Driven Markov Chain Monte Carlo Technique (WK, KML), pp. 1364–1367.

ICPR-2010-LeeCL #algorithm #data-driven #graph #markov #using

A Graph Matching Algorithm Using Data-Driven Markov Chain Monte Carlo Sampling (JL, MC, KML), pp. 2816–2819.

ICPR-2010-ZhouL #approximate #optimisation #performance

Efficient Polygonal Approximation of Digital Curves via Monte Carlo Optimization (XZ, YL), pp. 3513–3516.

DAC-2010-VeetilCSB #performance #resource management

Efficient smart monte carlo based SSTA on graphics processing units with improved resource utilization (VV, YHC, DS, DB), pp. 793–798.

DATE-2010-IqbalSH10a #estimation #execution #graph #named

DAGS: Distribution agnostic sequential Monte Carlo scheme for task execution time estimation (NI, MAS, JH), pp. 1645–1648.

DATE-2010-JaffariA #estimation

Practical Monte-Carlo based timing yield estimation of digital circuits (JJ, MA), pp. 807–812.

DATE-2010-KanoriaMM #analysis #markov #statistics #using

Statistical static timing analysis using Markov chain Monte Carlo (YK, SM, AM), pp. 813–818.

CIG-2009-WardC

Monte Carlo search applied to card selection in Magic: The Gathering (CDW, PIC), pp. 9–16.

ICML-2009-SilverT #simulation

Monte-Carlo simulation balancing (DS, GT), pp. 945–952.

CAV-2009-KitchenK #constraints #integer #markov

A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints (NK, AK), pp. 446–461.

SIGMOD-2008-JampaniXWPJH #approach #named #nondeterminism

MCDB: a monte carlo approach to managing uncertain data (RJ, FX, MW, LLP, CMJ, PJH), pp. 687–700.

AIIDE-2008-ChaslotBSS #framework #game studies

Monte-Carlo Tree Search: A New Framework for Game AI (GC, SB, IS, PS).

CIG-2008-ChildsBK

Transpositions and move groups in Monte Carlo tree search (BEC, JHB, LK), pp. 389–395.

CIG-2008-ShibaharaK #simulation

Combining final score with winning percentage by sigmoid function in Monte-Carlo simulations (KS, YK), pp. 183–190.

CIG-2008-TakeuchiKY #evaluation

Evaluation of Monte Carlo tree search and the application to Go (ST, TK, KY), pp. 191–198.

ICML-2008-SalakhutdinovM08a #markov #matrix #probability #using

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (RS, AM), pp. 880–887.

ICPR-2008-Sakai #approach #classification #incremental

Monte Carlo subspace method: An incremental approach to high-dimensional data classification (TS), pp. 1–4.

PADL-2008-KellerCCSB #generative

Specialising Simulator Generators for High-Performance Monte-Carlo Methods (GK, HCM, MMTC, DS, CBK), pp. 116–132.

DAC-2008-VeetilSB #analysis #incremental #performance #statistics

Efficient Monte Carlo based incremental statistical timing analysis (VV, DS, DB), pp. 676–681.

CIG-2007-WangG #simulation

Modifications of UCT and sequence-like simulations for Monte-Carlo Go (YW, SG), pp. 175–182.

CHI-2007-StrachanWM #navigation

Show me the way to Monte Carlo: density-based trajectory navigation (SS, JW, RMS), pp. 1245–1248.

ESEC-FSE-2007-SankaranarayananCJI #constraints #feedback #generative #using

State space exploration using feedback constraint generation and Monte-Carlo sampling (SS, RMC, GJ, FI), pp. 321–330.

DAC-2007-KimJH #estimation #performance

Fast, Non-Monte-Carlo Estimation of Transient Performance Variation Due to Device Mismatch (JK, KDJ, MAH), pp. 440–443.

DATE-2007-SingheeR #novel #performance #simulation #statistics

Statistical blockade: a novel method for very fast Monte Carlo simulation of rare circuit events, and its application (AS, RAR), pp. 1379–1384.

PDP-2007-DaneseLBGNS #simulation

An Application Specific Processor for Montecarlo Simulations (GD, FL, MB, MG, NN, AS), pp. 262–269.

CIG-2006-BouzyC #learning

Monte-Carlo Go Reinforcement Learning Experiments (BB, GC), pp. 187–194.

ICPR-v1-2006-WenGL #clustering #detection #markov

Markov Chain Monte Carlo Data Association for Merge and Split Detection in Tracking Protein Clusters (QW, JG, KLP), pp. 1030–1033.

ICPR-v1-2006-XueZZ #framework #multi

An integrated Monte Carlo data association framework for multi-object tracking (JX, NZ, XZ), pp. 703–706.

ICPR-v2-2006-KimK06b #estimation #graph #modelling #multi #online

Multi-modal Sequential Monte Carlo for On-Line Hierarchical Graph Structure Estimation in Model-based Scene Interpretation (SK, ISK), pp. 251–254.

ICPR-v2-2006-YuCMW #detection #markov #multi

Boosted Markov Chain Monte Carlo Data Association for Multiple Target Detection and Tracking (QY, IC, GGM, BW), pp. 675–678.

MBT-2006-CallananGRSTZ #approach #runtime #verification

Runtime Verification for High-Confidence Systems: A Monte Carlo Approach (SC, RG, AR, SAS, MRT, EZ), pp. 41–52.

CIG-2005-CazenaveH #game studies

Combining Tactical Search and Monte-Carlo in the Game of Go (TC, BH).

CIG-2005-ChungBS #game studies

Monte Carlo Planning in RTS Games (MC, MB, JS).

ICML-2005-EspositoS #classification #comparison

Experimental comparison between bagging and Monte Carlo ensemble classification (RE, LS), pp. 209–216.

TACAS-2005-GrosuS #model checking

Monte Carlo Model Checking (RG, SAS), pp. 271–286.

ICML-2004-EspositoS #analysis #classification

A Monte Carlo analysis of ensemble classification (RE, LS).

ICPR-v3-2004-MatsuiCUM #markov #recognition #using

Bayesian Face Recognition using a Markov Chain Monte Carlo Method (AM, SC, FU, TM), pp. 918–921.

ICPR-v4-2004-LinWH

Articulate Hand Motion Capturing Based on a Monte Carlo Nelder-Mead Simplex Tracker (JL, YW, TSH), pp. 975–978.

ICPR-v4-2004-MurakitaII #markov #using

Human Tracking using Floor Sensors based on the Markov Chain Monte Carlo Method (TM, TI, HI), pp. 917–920.

SIGMOD-2002-ProcopiucJAM #algorithm #clustering #performance

A Monte Carlo algorithm for fast projective clustering (CMP, MJ, PKA, TMM), pp. 418–427.

ICML-2002-FitzgibbonDA #approximate #polynomial

Univariate Polynomial Inference by Monte Carlo Message Length Approximation (LJF, DLD, LA), pp. 147–154.

ICML-2002-StrensBE #markov #optimisation #using

Markov Chain Monte Carlo Sampling using Direct Search Optimization (MJAS, MB, NE), pp. 602–609.

ICML-2002-ThamDR #classification #learning #markov #using

Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo (SST, AD, KR), pp. 634–641.

ICPR-v1-2002-KatoKS #distributed #named #smarttech

VizWear-Active: Distributed Monte Carlo Face Tracking for Wearable Active Cameras (TK, TK, KS), pp. 395–400.

ICPR-v3-2002-BaesensECV #classification #learning #markov #network #using

Learning Bayesian Network Classifiers for Credit Scoring Using Markov Chain Monte Carlo Search (BB, MEP, RC, JV), pp. 49–52.

ICPR-v4-2002-Ichimura #estimation #image #probability #sequence #using

Stochastic Filtering for Motion Trajectory in Image Sequences Using a Monte Carlo Filter with Estimation of Hyper-Parameters (NI), pp. 68–73.

POPL-2001-Monniaux #analysis #probability #source code

An abstract Monte-Carlo method for the analysis of probabilistic programs (DM), pp. 93–101.

SAC-2001-DowellB #graph #mobile #network #random #simulation #validation

Connectivity of random graphs and mobile networks: validation of Monte Carlo simulation results (LJD, MLB), pp. 77–81.

ICPR-v1-2000-GidasRA

Tracking of Moving Objects in Cluttered Environments via Monte Carlo Filter (BG, CR, MPdA), pp. 1175–1178.

ICPR-v1-2000-MossH #markov #using

Alignment and Correspondence Using Markov Chain Monte Carlo (SM, ERH), pp. 1928–1931.

ICML-1999-ThrunLF #learning #markov #modelling #parametricity #probability #process

Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes (ST, JL, DF), pp. 415–424.

PDP-1999-OdorKVR #architecture #effectiveness #parallel #simulation #string

Effective Monte Carlo simulation on System-V massively parallel associative string processing architecture (GÓ, AK, GV, FR), pp. 281–288.

EDTC-1997-SaxenaNH #approach #estimation

Monte-Carlo approach for power estimation in sequential circuits (VS, FNN, INH), pp. 416–420.

KDD-1996-Smyth #clustering #using

Clustering Using Monte Carlo Cross-Validation (PS), pp. 126–133.

KDD-1996-StolorzC #learning #markov #visual notation

Harnessing Graphical Structure in Markov Chain Monte Carlo Learning (PES, PCC), pp. 134–139.

SEKE-1996-JamoussiB #generative #performance #random testing #testing

Efficient Monte Carlo Method for Generating Random Test Data from Irregular Test Regions (AJ, FBB), pp. 17–24.

SAC-1993-HashemiCST #network #paradigm #predict

Prediction Capability of Neural Networks Trained by Monte-Carlo Paradigm (RRH, AHC, NLS, JRT), pp. 9–13.

STOC-1991-BabaiCFLS #algorithm #performance #permutation

Fast Monte Carlo Algorithms for Permutation Groups (LB, GC, LF, EML, ÁS), pp. 90–100.

DAC-1970-MitchellG #analysis #design #simulation

A simulation program for monte carlo analysis and design (EELM, DG), pp. 265–270.