BibSLEIGH corpus
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against (8)
game (6)
adapt (5)
model (5)
learn (5)

Stem adversari$ (all stems)

58 papers:

Broadcast from Minicast Secure Against General Adversaries (PR), pp. 701–712.
ICMLICML-2015-GajaneUC #algorithm #exponential
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits (PG, TU, FC), pp. 218–227.
SACSAC-2015-CruzPQSSOBO #algorithm #game studies #named #probability #using
Amê: an environment to learn and analyze adversarial search algorithms using stochastic card games (ABC, LP, JQ, US, SS, AO, EB, ESO), pp. 208–213.
SACSAC-2015-TambeN #behaviour #game studies #modelling #resource management #robust #security
Robust resource allocation in security games and ensemble modeling of adversary behavior (AT, TN), pp. 277–282.
ICALPICALP-v2-2014-Chatterjee0 #game studies
Games with a Weak Adversary (KC, LD), pp. 110–121.
CIKMCIKM-2014-AlabdulmohsinGZ #reverse engineering #robust
Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering (IMA, XG, XZ), pp. 231–240.
Tracking Adversarial Targets (YAY, PLB, VK), pp. 369–377.
ICMLICML-c2-2014-SeldinS #algorithm #probability
One Practical Algorithm for Both Stochastic and Adversarial Bandits (YS, AS), pp. 1287–1295.
SIGIRSIGIR-2014-RichardsonC #statistics
Estimating global statistics for unstructured P2P search in the presence of adversarial peers (SR, IJC), pp. 203–212.
VLDBVLDB-2013-RekatsinasDM #clustering #multi
A SPARSI: Partitioning Sensitive Data amongst Multiple Adversaries (TR, AD, AM), pp. 1594–1605.
ICMLICML-c1-2013-TorkamaniL #classification
Convex Adversarial Collective Classification (MT, DL), pp. 642–650.
ICMLICML-c3-2013-ChenCM #robust
Robust Sparse Regression under Adversarial Corruption (YC, CC, SM), pp. 774–782.
Better Rates for Any Adversarial Deterministic MDP (OD, EH), pp. 675–683.
ICMLICML-c3-2013-GrosshansSBS #game studies #problem
Bayesian Games for Adversarial Regression Problems (MG, CS, MB, TS), pp. 55–63.
KDDKDD-2013-Gesher #adaptation
Adaptive adversaries: building systems to fight fraud and cyber intruders (AG), p. 1136.
STOCSTOC-2012-VaziraniV #generative #quantum #random
Certifiable quantum dice: or, true random number generation secure against quantum adversaries (UVV, TV), pp. 61–76.
ICALPICALP-v1-2012-Kimmel #bound #quantum
Quantum Adversary (Upper) Bound (SK), pp. 557–568.
Byzantine Agreement with a Rational Adversary (AG, JK, AT, VZ), pp. 561–572.
CIKMCIKM-2012-ShirazipourazadBVSH #contest #how
Influence propagation in adversarial setting: how to defeat competition with least amount of investment (SS, BB, HV, AS, PH), pp. 585–594.
ICMLICML-2012-DekelTA #adaptation #learning #online #policy
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret (OD, AT, RA), p. 227.
KDDKDD-2012-ZhouKTX #machine learning
Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
Adversarial content manipulation effects (FR), p. 993.
SACSAC-2012-OliveiraCS #bound #on the #using
On using adversary simulators to obtain tight lower bounds for response times (RSdO, AC, RAS), pp. 1573–1579.
Secure computation with information leaking to an adversary (MA), pp. 715–724.
ICMLICML-2011-GuilloryB #learning
Simultaneous Learning and Covering with Adversarial Noise (AG, JAB), pp. 369–376.
KDDKDD-2011-BrucknerS #game studies #predict #problem
Stackelberg games for adversarial prediction problems (MB, TS), pp. 547–555.
KDDKDD-2011-SculleyOPSHZ #detection
Detecting adversarial advertisements in the wild (DS, MEO, MP, BS, JH, YZ), pp. 274–282.
FoSSaCSFoSSaCS-2010-AsarinCV #game studies
Fair Adversaries and Randomization in Two-Player Games (EA, RCYF, DV), pp. 64–78.
PLDIPLDI-2010-FlanaganF #detection #memory management
Adversarial memory for detecting destructive races (CF, SNF), pp. 244–254.
ICMLICML-2010-Busa-FeketeK #performance #using
Fast boosting using adversarial bandits (RBF, BK), pp. 143–150.
SACSAC-2010-McDonaldTKG #component #reduction #using
Using logic-based reduction for adversarial component recovery (JTM, EDT, YCK, MRG), pp. 1993–2000.
CSLCSL-2010-BasinC #protocol #security
Degrees of Security: Protocol Guarantees in the Face of Compromising Adversaries (DAB, CJFC), pp. 1–18.
Data Publishing against Realistic Adversaries (AM, JG, MG), pp. 790–801.
CIKMCIKM-2009-ChinavleKOF #classification
Ensembles in adversarial classification for spam (DC, PK, TO, TF), pp. 2015–2018.
SACSAC-2009-KingLS #network #taxonomy
A taxonomy and adversarial model for attacks against network log anonymization (JK, KL, AJS), pp. 1286–1293.
STOCSTOC-2008-MironovNS #sketching
Sketching in adversarial environments (IM, MN, GS), pp. 651–660.
ICALPICALP-A-2008-ChildsL #bound #order #quantum
Optimal Quantum Adversary Lower Bounds for Ordered Search (AMC, TL), pp. 869–880.
SACSAC-2008-BenedettiLV #modelling #scheduling
Modeling adversary scheduling with QCSP+ (MB, AL, JV), pp. 151–155.
VLDBVLDB-2007-ChenRL #multi #privacy
Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge (BCC, RR, KL), pp. 770–781.
Negative weights make adversaries stronger (PH, TL, RS), pp. 526–535.
ICEISICEIS-EIS-2007-MurzekK #model transformation #process
Business Process Model Transformation Issues — The Top 7 Adversaries Encountered at Defining Model Transformations (MM, GK), pp. 144–151.
SOSPSOSP-2007-ChunMSK #memory management #word
Attested append-only memory: making adversaries stick to their word (BGC, PM, SS, JK), pp. 189–204.
WCREWCRE-2006-CoxS #case study #experience #reverse engineering
Reverse Engineering in Support of Litigation: Experiences in an Adversarial Environment (AC, TJS), pp. 288–292.
KDDKDD-2006-AggarwalPZ #data mining #mining #on the #privacy
On privacy preservation against adversarial data mining (CCA, JP, BZ), pp. 510–516.
ESOPESOP-2005-JanvierLM #encryption
Completing the Picture: Soundness of Formal Encryption in the Presence of Active Adversaries (RJ, YL, LM), pp. 172–185.
STOCSTOC-2005-Scheideler #exclamation #how
How to spread adversarial nodes?: rotate! (CS), pp. 704–713.
ICALPICALP-2005-BaudetCK #equation #implementation
Computationally Sound Implementations of Equational Theories Against Passive Adversaries (MB, VC, SK), pp. 652–663.
ICALPICALP-2005-SpalekS #quantum
All Quantum Adversary Methods Are Equivalent (RS, MS), pp. 1299–1311.
KDDKDD-2005-LowdM #learning
Adversarial learning (DL, CM), pp. 641–647.
KDDKDD-2004-DalviDMSV #classification
Adversarial classification (NND, PMD, M, SKS, DV), pp. 99–108.
Anycasting in Adversarial Systems: Routing and Admission Control (BA, AB, CS), pp. 1153–1168.
ICMLICML-2003-McMahanGB #cost analysis
Planning in the Presence of Cost Functions Controlled by an Adversary (HBM, GJG, AB), pp. 536–543.
STOCSTOC-2002-AnshelevichKK #algorithm
Stability of load balancing algorithms in dynamic adversarial systems (EA, DK, JMK), pp. 399–406.
CSCWCSCW-2000-CohenCM #collaboration #design
Designing to support adversarial collaboration (ALC, DC, MJM), pp. 31–39.
STOCSTOC-1999-Gamarnik #adaptation #network #policy
Stability of Adaptive and Non-Adaptive Packet Routing Policies in Adversarial Queueing Networks (DG), pp. 206–214.
STOCSTOC-1998-AielloKOR #adaptation
Adaptive Packet Routing for Bursty Adversarial Traffic (WA, EK, RO, AR), pp. 359–368.
Adversarial Queueing Theory (AB, JMK, PR, MS, DPW), pp. 376–385.
ICALPICALP-1996-AumannB #performance
Efficient Asynchronous Consensus with the Value-Oblivious Adversary Scheduler (YA, MAB), pp. 622–633.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.