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2020 – today
- 2024
[i22]Ali Arabzadeh, James A. Grant, David S. Leslie:
Federated X-armed Bandit with Flexible Personalisation. CoRR abs/2409.07251 (2024)- 2023
- [j22]Matthew Darlington
, Kevin D. Glazebrook, David S. Leslie, Rob Shone, Roberto Szechtman:
A stochastic game framework for patrolling a border. Eur. J. Oper. Res. 311(3): 1146-1158 (2023) - [j21]James A. Grant, David S. Leslie:
Learning to Rank under Multinomial Logit Choice. J. Mach. Learn. Res. 24: 260:1-260:49 (2023) - 2022
- [j20]Simen Eide, David S. Leslie, Arnoldo Frigessi:
Dynamic slate recommendation with gated recurrent units and Thompson sampling. Data Min. Knowl. Discov. 36(5): 1756-1786 (2022) - [i21]Matthew Darlington, Kevin D. Glazebrook, David S. Leslie, Rob Shone, Roberto Szechtman:
A stochastic game framework for patrolling a border. CoRR abs/2205.10017 (2022) - 2021
- [j19]Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson:
GIBBON: General-purpose Information-Based Bayesian Optimisation. J. Mach. Learn. Res. 22: 235:1-235:49 (2021) - [j18]Harjit Hullait, David S. Leslie, Nicos G. Pavlidis, Steve King:
Robust Function-on-Function Regression. Technometrics 63(3): 396-409 (2021) - [c18]Muhammed O. Sayin, Kaiqing Zhang, David S. Leslie, Tamer Basar, Asuman E. Ozdaglar:
Decentralized Q-learning in Zero-sum Markov Games. NeurIPS 2021: 18320-18334 - [c17]Simen Eide, David S. Leslie, Arnoldo Frigessi, Joakim Rishaug, Helge Jenssen, Sofie Verrewaere:
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, all Viewed Items and Click Responses/No-Click for Recommender Systems Research. RecSys 2021: 556-558 - [i20]Henry B. Moss, David S. Leslie, Javier Gonzalez, Paul Rayson:
GIBBON: General-purpose Information-Based Bayesian OptimisatioN. CoRR abs/2102.03324 (2021) - [i19]Simen Eide, David S. Leslie, Arnoldo Frigessi:
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling. CoRR abs/2104.15046 (2021) - [i18]Muhammed O. Sayin, Kaiqing Zhang, David S. Leslie, Tamer Basar, Asuman E. Ozdaglar:
Decentralized Q-Learning in Zero-sum Markov Games. CoRR abs/2106.02748 (2021) - [i17]James A. Grant, David S. Leslie:
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification. CoRR abs/2109.14412 (2021) - [i16]Simen Eide, Arnoldo Frigessi, Helge Jenssen, David S. Leslie, Joakim Rishaug, Sofie Verrewaere:
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research. CoRR abs/2111.03340 (2021) - 2020
- [j17]James A. Grant, David S. Leslie, Kevin Glazebrook, Roberto Szechtman, Adam N. Letchford:
Adaptive policies for perimeter surveillance problems. Eur. J. Oper. Res. 283(1): 265-278 (2020) - [j16]David S. Leslie, Steven Perkins, Zibo Xu:
Best-response dynamics in zero-sum stochastic games. J. Econ. Theory 189: 105095 (2020) - [j15]James A. Edwards, David S. Leslie:
Selecting multiple web adverts: A contextual multi-armed bandit with state uncertainty. J. Oper. Res. Soc. 71(1): 100-116 (2020) - [c16]James A. Grant, David S. Leslie:
On Thompson Sampling for Smoother-than-Lipschitz Bandits. AISTATS 2020: 2612-2622 - [c15]Henry B. Moss, David S. Leslie, Daniel Beck, Javier Gonzalez, Paul Rayson:
BOSS: Bayesian Optimization over String Spaces. NeurIPS 2020 - [c14]Henry B. Moss, David S. Leslie, Paul Rayson:
MUMBO: MUlti-task Max-Value Bayesian Optimization. ECML/PKDD (3) 2020: 447-462 - [i15]James A. Grant, David S. Leslie:
On Thompson Sampling for Smoother-than-Lipschitz Bandits. CoRR abs/2001.02323 (2020) - [i14]Henry B. Moss, David S. Leslie, Paul Rayson:
MUMBO: MUlti-task Max-value Bayesian Optimization. CoRR abs/2006.12093 (2020) - [i13]Henry B. Moss, David S. Leslie, Paul Rayson:
BOSH: Bayesian Optimization by Sampling Hierarchically. CoRR abs/2007.00939 (2020) - [i12]James A. Grant, David S. Leslie:
Learning to Rank under Multinomial Logit Choice. CoRR abs/2009.03207 (2020) - [i11]Henry B. Moss, Daniel Beck, Javier Gonzalez, David S. Leslie, Paul Rayson:
BOSS: Bayesian Optimization over String Spaces. CoRR abs/2010.00979 (2020)
2010 – 2019
- 2019
- [j14]Kevin Lloyd, Adam Sanborn, David S. Leslie, Stephan Lewandowsky:
Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cogn. Sci. 43(12) (2019) - [c13]Henry B. Moss, Andrew Moore, David S. Leslie, Paul Rayson:
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms. ACL (1) 2019: 2920-2930 - [c12]James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote:
Adaptive Sensor Placement for Continuous Spaces. ICML 2019: 2385-2393 - [c11]Harjit Hullait, David S. Leslie, Nicos G. Pavlidis, Steve King:
Robust Functional Regression for Outlier Detection. AALTD@PKDD/ECML 2019: 3-13 - [i10]James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote:
Adaptive Sensor Placement for Continuous Spaces. CoRR abs/1905.06821 (2019) - [i9]Henry B. Moss, Andrew Moore, David S. Leslie, Paul Rayson:
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms. CoRR abs/1906.12230 (2019) - 2018
- [c10]Henry B. Moss, David S. Leslie, Paul Rayson:
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models. COLING 2018: 2978-2989 - [c9]Mario Bravo, David S. Leslie, Panayotis Mertikopoulos:
Bandit Learning in Concave N-Person Games. NeurIPS 2018: 5666-5676 - [i8]Henry B. Moss, David S. Leslie, Paul Rayson:
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models. CoRR abs/1806.07139 (2018) - [i7]Mario Bravo, David S. Leslie, Panayotis Mertikopoulos:
Bandit learning in concave N-person games. CoRR abs/1810.01925 (2018) - [i6]James A. Grant, David S. Leslie, Kevin Glazebrook, Roberto Szechtman, Adam N. Letchford:
Adaptive Policies for Perimeter Surveillance Problems. CoRR abs/1810.02176 (2018) - 2017
- [j13]Brian Swenson, Soummya Kar, João Xavier, David S. Leslie:
Robustness Properties in Fictitious-Play-Type Algorithms. SIAM J. Control. Optim. 55(5): 3295-3318 (2017) - [j12]Steven Perkins, Panayotis Mertikopoulos, David S. Leslie:
Mixed-Strategy Learning With Continuous Action Sets. IEEE Trans. Autom. Control. 62(1): 379-384 (2017) - [c8]Kevin Lloyd, Adam Sanborn, David S. Leslie, Stephan Lewandowsky:
Why Does Higher Working Memory Capacity Help You Learn? CogSci 2017 - [c7]David S. Leslie, Chris Sherfield, Nigel P. Smart:
Multi-rate Threshold FlipThem. ESORICS (2) 2017: 174-190 - [i5]James A. Grant, David S. Leslie, Kevin Glazebrook, Roberto Szechtman:
Combinatorial Multi-Armed Bandits with Filtered Feedback. CoRR abs/1705.09605 (2017) - [i4]David S. Leslie, Chris Sherfield, Nigel P. Smart:
Multi-Rate Threshold FlipThem. IACR Cryptol. ePrint Arch. 2017: 611 (2017) - 2015
- [c6]David S. Leslie, Chris Sherfield, Nigel P. Smart:
Threshold FlipThem: When the Winner Does Not Need to Take All. GameSec 2015: 74-92 - [c5]Adnane Ez-Zizi, Simon Farrell, David S. Leslie:
Bayesian Reinforcement Learning in Markovian and non-Markovian Tasks. SSCI 2015: 579-586 - [i3]David S. Leslie, Chris Sherfield, Nigel P. Smart:
Threshold FlipThem: When the winner does not need to take all. IACR Cryptol. ePrint Arch. 2015: 784 (2015) - 2014
- [j11]Archie C. Chapman, David S. Leslie, Alex Rogers, Nicholas R. Jennings:
Learning in Unknown Reward Games: Application to Sensor Networks. Comput. J. 57(6): 875-892 (2014) - [j10]Steven Perkins, David S. Leslie:
Stochastic fictitious play with continuous action sets. J. Econ. Theory 152: 179-213 (2014) - [i2]Steven Perkins, Panayotis Mertikopoulos, David S. Leslie:
Game-theoretical control with continuous action sets. CoRR abs/1412.0543 (2014) - 2013
- [j9]Archie C. Chapman, David S. Leslie, Alex Rogers, Nicholas R. Jennings:
Convergent Learning Algorithms for Unknown Reward Games. SIAM J. Control. Optim. 51(4): 3154-3180 (2013) - [c4]Holly Borowski, Jason R. Marden, David S. Leslie, Eric W. Frew:
Coarse resistance tree methods for stochastic stability analysis. CDC 2013: 1860-1865 - 2012
- [j8]Benedict C. May, Nathan Korda, Anthony Lee, David S. Leslie:
Optimistic Bayesian Sampling in Contextual-Bandit Problems. J. Mach. Learn. Res. 13: 2069-2106 (2012) - 2011
- [j7]Archie C. Chapman, Alex Rogers, Nicholas R. Jennings, David S. Leslie:
A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems. Knowl. Eng. Rev. 26(4): 411-444 (2011) - [c3]David S. Leslie, Jason R. Marden:
Equilibrium selection in potential games with noisy rewards. NetGCoop 2011: 1-4 - [i1]Michalis Smyrnakis, David S. Leslie:
Adaptive Forgetting Factor Fictitious Play. CoRR abs/1112.2315 (2011) - 2010
- [j6]Michalis Smyrnakis, David S. Leslie:
Dynamic Opponent Modelling in Fictitious Play. Comput. J. 53(9): 1344-1359 (2010) - [j5]Tobias Larsen, David S. Leslie, Edmund J. Collins, Rafal Bogacz:
Posterior Weighted Reinforcement Learning with State Uncertainty. Neural Comput. 22(5): 1149-1179 (2010) - [c2]Michalis Smyrnakis, David S. Leslie:
Convergence of Probability Collectives with Adaptive Choice of Temperature Parameters. LION 2010: 200-203
2000 – 2009
- 2009
- [c1]Michalis Smyrnakis, David S. Leslie:
Sequentially updated Probability Collectives. CDC 2009: 5774-5779 - 2008
- [j4]Iead Rezek, David S. Leslie, Steven Reece, Stephen J. Roberts, Alex Rogers, Rajdeep K. Dash, Nicholas R. Jennings:
On Similarities between Inference in Game Theory and Machine Learning. J. Artif. Intell. Res. 33: 259-283 (2008) - 2007
- [j3]David S. Leslie, Robert Kohn, David J. Nott:
A general approach to heteroscedastic linear regression. Stat. Comput. 17(2): 131-146 (2007) - 2006
- [j2]David S. Leslie, Edmund J. Collins:
Generalised weakened fictitious play. Games Econ. Behav. 56(2): 285-298 (2006) - 2005
- [j1]David S. Leslie, Edmund J. Collins:
Individual Q-Learning in Normal Form Games. SIAM J. Control. Optim. 44(2): 495-514 (2005)
Coauthor Index
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