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- affiliation: University of British Columbia, Canada
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2020 – today
- 2024
[j38]Kevin Leyton-Brown, Mausam, Yatin Nandwani
, Hedayat Zarkoob, Chris Cameron, Neil Newman, Dinesh Raghu:
Matching papers and reviewers at large conferences. Artif. Intell. 331: 104119 (2024)- [j37]Neil Newman, Kevin Leyton-Brown, Paul Milgrom, Ilya Segal:
Incentive Auction Design Alternatives: A Simulation Study. Manag. Sci. 70(11): 8187-8215 (2024) - [c109]Greg d'Eon, Sophie Greenwood, Kevin Leyton-Brown, James R. Wright:
How to Evaluate Behavioral Models. AAAI 2024: 9636-9644 - [c108]Taylor Lundy, Narun K. Raman, Hu Fu, Kevin Leyton-Brown:
Pay to (Not) Play: Monetizing Impatience in Mobile Games. AAAI 2024: 9856-9864 - [c107]Chris Cameron, Jason S. Hartford, Taylor Lundy, Tuan Truong, Alan Milligan, Rex Chen, Kevin Leyton-Brown:
UNSAT Solver Synthesis via Monte Carlo Forest Search. CPAIOR (1) 2024: 170-189 - [c106]Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham:
Generating Benchmarks for Factuality Evaluation of Language Models. EACL (1) 2024: 49-66 - [c105]Narun Krishnamurthi Raman, Taylor Lundy, Samuel Joseph Amouyal, Yoav Levine, Kevin Leyton-Brown, Moshe Tennenholtz:
STEER: Assessing the Economic Rationality of Large Language Models. ICML 2024 - [c104]Hedayat Zarkoob, Siddharth Nand, Kevin Leyton-Brown, Giulia Toti:
Agora: Motivating and Measuring Engagement in Large-Class Discussions. ITiCSE (1) 2024 - [c103]Hedayat Zarkoob, Kevin Leyton-Brown:
Mechanical TA 2: Peer Grading with TA and Algorithmic Support. SIGCSE (1) 2024: 1470-1476 - [i56]Narun K. Raman, Taylor Lundy, Samuel Joseph Amouyal, Yoav Levine, Kevin Leyton-Brown, Moshe Tennenholtz:
Rationality Report Cards: Assessing the Economic Rationality of Large Language Models. CoRR abs/2402.09552 (2024) - [i55]Greg d'Eon, Neil Newman, Kevin Leyton-Brown:
Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning. CoRR abs/2402.19420 (2024) - [i54]Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen, Nils Dycke, Alexander Goldberg, Tom Hope, Dirk Hovy, Jonathan K. Kummerfeld, Anne Lauscher, Kevin Leyton-Brown, Sheng Lu, Mausam, Margot Mieskes, Aurélie Névéol, Danish Pruthi, Lizhen Qu, Roy Schwartz, Noah A. Smith, Thamar Solorio, Jingyan Wang, Xiaodan Zhu, Anna Rogers, Nihar B. Shah, Iryna Gurevych:
What Can Natural Language Processing Do for Peer Review? CoRR abs/2405.06563 (2024) - [i53]Devon R. Graham, Kevin Leyton-Brown:
Utilitarian Algorithm Configuration for Infinite Parameter Spaces. CoRR abs/2405.18246 (2024) - [i52]Kevin Leyton-Brown, Yoav Shoham:
Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models. CoRR abs/2406.10937 (2024) - 2023
- [j36]Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham:
In-Context Retrieval-Augmented Language Models. Trans. Assoc. Comput. Linguistics 11: 1316-1331 (2023) - [c102]Hedayat Zarkoob, Greg d'Eon, Lena Podina, Kevin Leyton-Brown:
Better Peer Grading through Bayesian Inference. AAAI 2023: 6137-6144 - [c101]Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham:
Parallel Context Windows for Large Language Models. ACL (1) 2023: 6383-6402 - [c100]Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Formalizing Preferences Over Runtime Distributions. ICML 2023: 11659-11682 - [c99]Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Utilitarian Algorithm Configuration. NeurIPS 2023 - [e2]Kevin Leyton-Brown, Jason D. Hartline, Larry Samuelson:
Proceedings of the 24th ACM Conference on Economics and Computation, EC 2023, London, United Kingdom, July 9-12, 2023. ACM 2023 [contents] - [i51]Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham:
In-Context Retrieval-Augmented Language Models. CoRR abs/2302.00083 (2023) - [i50]Greg d'Eon, Sophie Greenwood, Kevin Leyton-Brown, James R. Wright:
Loss Functions for Behavioral Game Theory. CoRR abs/2306.04778 (2023) - [i49]Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham:
Generating Benchmarks for Factuality Evaluation of Language Models. CoRR abs/2307.06908 (2023) - [i48]Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Utilitarian Algorithm Configuration. CoRR abs/2310.20401 (2023) - [i47]Taylor Lundy, Narun K. Raman, Hu Fu, Kevin Leyton-Brown:
Pay to (Not) Play: Monetizing Impatience in Mobile Games. CoRR abs/2312.10205 (2023) - 2022
- [j35]Kevin Leyton-Brown, Mausam, Qiang Yang:
The New Faculty Highlights Program at AAAI-21. AI Mag. 43(4): 343 (2022) - [c98]Chris Cameron, Jason S. Hartford, Taylor Lundy, Kevin Leyton-Brown:
The Perils of Learning Before Optimizing. AAAI 2022: 3708-3715 - [c97]Greg d'Eon, Jason d'Eon, James R. Wright, Kevin Leyton-Brown:
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models. FAccT 2022: 1962-1981 - [i46]Kevin Leyton-Brown, Mausam, Yatin Nandwani, Hedayat Zarkoob, Chris Cameron, Neil Newman, Dinesh Raghu:
Matching Papers and Reviewers at Large Conferences. CoRR abs/2202.12273 (2022) - [i45]Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham:
Standing on the Shoulders of Giant Frozen Language Models. CoRR abs/2204.10019 (2022) - [i44]Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, Moshe Tennenholtz:
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. CoRR abs/2205.00445 (2022) - [i43]Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden:
Formalizing Preferences Over Runtime Distributions. CoRR abs/2205.13028 (2022) - [i42]Hedayat Zarkoob, Greg d'Eon, Lena Podina, Kevin Leyton-Brown:
Better Peer Grading through Bayesian Inference. CoRR abs/2209.01242 (2022) - [i41]Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David C. Parkes, William H. Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, Astro Teller:
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence. CoRR abs/2211.06318 (2022) - [i40]Chris Cameron, Jason S. Hartford, Taylor Lundy, Tuan Truong, Alan Milligan, Rex Chen, Kevin Leyton-Brown:
Monte Carlo Forest Search: UNSAT Solver Synthesis via Reinforcement learning. CoRR abs/2211.12581 (2022) - [i39]Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham:
Parallel Context Windows Improve In-Context Learning of Large Language Models. CoRR abs/2212.10947 (2022) - 2021
- [c96]Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham:
PMI-Masking: Principled masking of correlated spans. ICLR 2021 - [c95]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. ICML 2021: 4096-4106 - [p4]Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Automated Configuration and Selection of SAT Solvers. Handbook of Satisfiability 2021: 481-507 - [i38]Hedayat Zarkoob, Farzad Abdolhosseini, Kevin Leyton-Brown:
Mechanical TA 2: A System for Peer Grading with TA Support. CoRR abs/2101.10078 (2021) - [i37]Chris Cameron, Jason S. Hartford, Taylor Lundy, Kevin Leyton-Brown:
The Perils of Learning Before Optimizing. CoRR abs/2106.10349 (2021) - [i36]Greg d'Eon, Jason d'Eon, James R. Wright, Kevin Leyton-Brown:
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models. CoRR abs/2107.00758 (2021) - 2020
- [c94]Chris Cameron, Rex Chen, Jason S. Hartford, Kevin Leyton-Brown:
Predicting Propositional Satisfiability via End-to-End Learning. AAAI 2020: 3324-3331 - [c93]Hedayat Zarkoob, Hu Fu, Kevin Leyton-Brown:
Report-Sensitive Spot-Checking in Peer-Grading Systems. AAMAS 2020: 1593-1601 - [c92]Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz:
Fiduciary Bandits. ICML 2020: 518-527 - [c91]Xi Alice Gao, James R. Wright, Kevin Leyton-Brown:
Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth (Extended Abstract). IJCAI 2020: 5140-5144 - [c90]Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz:
Exemplar Guided Active Learning. NeurIPS 2020 - [c89]Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier:
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. NeurIPS 2020 - [c88]James R. Wright, Kevin Leyton-Brown:
A Formal Separation Between Strategic and Nonstrategic Behavior. EC 2020: 535-536 - [c87]Neil Newman, Kevin Leyton-Brown, Paul Milgrom, Ilya Segal:
Incentive Auction Design Alternatives: A Simulation Study. EC 2020: 603-604 - [c86]Natalie Collina, Nicole Immorlica, Kevin Leyton-Brown, Brendan Lucier, Neil Newman:
Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates. WINE 2020: 17-30 - [i35]Devon R. Graham, Satish Kumar Sarraf, Taylor Lundy, Ali MohammadMehr, Sara Uppal, Tae-Yoon Lee, Hedayat Zarkoob, Scott Duke Kominers, Kevin Leyton-Brown:
Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver. CoRR abs/2003.09761 (2020) - [i34]Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz:
Learning under Invariable Bayesian Safety. CoRR abs/2006.04497 (2020) - [i33]Jason S. Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown:
Valid Causal Inference with (Some) Invalid Instruments. CoRR abs/2006.11386 (2020) - [i32]Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham:
PMI-Masking: Principled masking of correlated spans. CoRR abs/2010.01825 (2020) - [i31]Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz:
Exemplar Guided Active Learning. CoRR abs/2011.01285 (2020) - [i30]Natalie Collina, Nicole Immorlica, Kevin Leyton-Brown, Brendan Lucier, Neil Newman:
Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates. CoRR abs/2012.00689 (2020)
2010 – 2019
- 2019
- [j34]Xi Alice Gao, James R. Wright, Kevin Leyton-Brown:
Incentivizing evaluation with peer prediction and limited access to ground truth. Artif. Intell. 275: 618-638 (2019) - [j33]Jean L. Kiddoo, Evan Kwerel, Sasha Javid, Melissa Dunford, Gary M. Epstein, Charles E. Meisch Jr., Karla L. Hoffman, Brian B. Smith, Anthony B. Coudert, Rudy K. Sultana, James A. Costa, Steven Charbonneau, Michael A. Trick, Ilya Segal, Kevin Leyton-Brown, Neil Newman, Alexandre Fréchette, Dinesh Menon, Paul Salasznyk:
Operations Research Enables Auction to Repurpose Television Spectrum for Next-Generation Wireless Technologies. INFORMS J. Appl. Anal. 49(1): 7-22 (2019) - [j32]James R. Wright, Kevin Leyton-Brown:
Level-0 Models for Predicting Human Behavior in Games. J. Artif. Intell. Res. 64: 357-383 (2019) - [c85]Hedayat Zarkoob, Hu Fu, Kevin Leyton-Brown:
Report-Sensitive Spot-checking in Peer Grading Systems. AAMAS 2019: 2306-2308 - [c84]Taylor Lundy, Alexander Wei, Hu Fu, Scott Duke Kominers, Kevin Leyton-Brown:
Allocation for Social Good: Auditing Mechanisms for Utility Maximization. EC 2019: 785-803 - [c83]Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon R. Graham:
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration. NeurIPS 2019: 8881-8891 - [p3]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. Automated Machine Learning 2019: 81-95 - [i29]Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon R. Graham:
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration. CoRR abs/1902.05454 (2019) - [i28]Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz:
Fiduciary Bandits. CoRR abs/1905.07043 (2019) - [i27]Hedayat Zarkoob, Hu Fu, Kevin Leyton-Brown:
Report-Sensitive Spot-Checking in Peer-Grading Systems. CoRR abs/1906.05884 (2019) - 2018
- [j31]Maria L. Gini, Noa Agmon, Fausto Giunchiglia, Sven Koenig, Kevin Leyton-Brown:
Artificial intelligence in 2027. AI Matters 4(1): 10-20 (2018) - [j30]Neil Newman, Alexandre Fréchette, Kevin Leyton-Brown:
Deep optimization for spectrum repacking. Commun. ACM 61(1): 97-104 (2018) - [j29]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient benchmarking of algorithm configurators via model-based surrogates. Mach. Learn. 107(1): 15-41 (2018) - [c82]Neil Newman, Lauren Falcao Bergquist, Nicole Immorlica, Kevin Leyton-Brown, Brendan Lucier, Craig McIntosh, John A. Quinn, Richard Ssekibuule:
Designing and Evolving an Electronic Agricultural Marketplace in Uganda. COMPASS 2018: 14:1-14:11 - [c81]Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh:
Deep Models of Interactions Across Sets. ICML 2018: 1914-1923 - [c80]Lars Kotthoff, Alexandre Fréchette, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown:
Quantifying Algorithmic Improvements over Time. IJCAI 2018: 5165-5171 - [p2]Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Selection and Configuration of Parallel Portfolios. Handbook of Parallel Constraint Reasoning 2018: 583-615 - [i26]Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh:
Deep Models of Interactions Across Sets. CoRR abs/1803.02879 (2018) - [i25]James R. Wright, Kevin Leyton-Brown:
Formalizing the Boundary Between Strategic and Nonstrategic Reasoning. CoRR abs/1812.11571 (2018) - 2017
- [j28]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). Artif. Intell. 243: 1-25 (2017) - [j27]Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Torsten Schaub:
Automatic construction of parallel portfolios via algorithm configuration. Artif. Intell. 244: 272-290 (2017) - [j26]David R. M. Thompson, Kevin Leyton-Brown:
Computational analysis of perfect-information position auctions. Games Econ. Behav. 102: 583-623 (2017) - [j25]James R. Wright, Kevin Leyton-Brown:
Predicting human behavior in unrepeated, simultaneous-move games. Games Econ. Behav. 106: 16-37 (2017) - [j24]Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18: 25:1-25:5 (2017) - [j23]Kevin Leyton-Brown, Paul Milgrom, Ilya Segal:
Economics and computer science of a radio spectrum reallocation. Proc. Natl. Acad. Sci. USA 114(28): 7202-7209 (2017) - [c79]Albert Xin Jiang, Hau Chan, Kevin Leyton-Brown:
Resource Graph Games: A Compact Representation for Games with Structured Strategy Spaces. AAAI 2017: 572-578 - [c78]David R. M. Thompson, Neil Newman, Kevin Leyton-Brown:
The Positronic Economist: A Computational System for Analyzing Economic Mechanisms. AAAI 2017: 720-727 - [c77]Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy:
Deep IV: A Flexible Approach for Counterfactual Prediction. ICML 2017: 1414-1423 - [c76]Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier:
Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees. IJCAI 2017: 2023-2031 - [i24]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown, Frank Hutter:
OASC-2017: *Zilla Submission. OASC 2017: 15-18 - [i23]Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown:
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates. CoRR abs/1703.10342 (2017) - [i22]Neil Newman, Alexandre Fréchette, Kevin Leyton-Brown:
Deep Optimization for Spectrum Repacking. CoRR abs/1706.03304 (2017) - [i21]Neil Newman, Kevin Leyton-Brown, Paul Milgrom, Ilya Segal:
Assessing Economic Outcomes in Simulated Reverse Clock Auctions for Radio Spectrum. CoRR abs/1706.04324 (2017) - 2016
- [j22]Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
SATenstein: Automatically building local search SAT solvers from components. Artif. Intell. 232: 20-42 (2016) - [j21]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A benchmark library for algorithm selection. Artif. Intell. 237: 41-58 (2016) - [c75]Alexandre Fréchette, Neil Newman, Kevin Leyton-Brown:
Solving the Station Repacking Problem. AAAI 2016: 702-709 - [c74]Alexandre Fréchette, Lars Kotthoff, Tomasz P. Michalak, Talal Rahwan, Holger H. Hoos, Kevin Leyton-Brown:
Using the Shapley Value to Analyze Algorithm Portfolios. AAAI 2016: 3397-3403 - [c73]Chris Cameron, Holger H. Hoos, Kevin Leyton-Brown:
Bias in Algorithm Portfolio Performance Evaluation. IJCAI 2016: 712-719 - [c72]Lin Xu, Ashiqur R. KhudaBukhsh, Holger H. Hoos, Kevin Leyton-Brown:
Quantifying the Similarity of Algorithm Configurations. LION 2016: 203-217 - [c71]Jason S. Hartford, James R. Wright, Kevin Leyton-Brown:
Deep Learning for Predicting Human Strategic Behavior. NIPS 2016: 2424-2432 - [c70]Hau Chan, Albert Xin Jiang, Kevin Leyton-Brown, Ruta Mehta:
Multilinear Games. WINE 2016: 44-58 - [i20]Xi Alice Gao, James R. Wright, Kevin Leyton-Brown:
Incentivizing Evaluation via Limited Access to Ground Truth: Peer-Prediction Makes Things Worse. CoRR abs/1606.07042 (2016) - [i19]James R. Wright, Kevin Leyton-Brown:
Models of Level-0 Behavior for Predicting Human Behavior in Games. CoRR abs/1609.08923 (2016) - [i18]Jason S. Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy:
Counterfactual Prediction with Deep Instrumental Variables Networks. CoRR abs/1612.09596 (2016) - 2015
- [j20]Albert Xin Jiang, Kevin Leyton-Brown:
Polynomial-time computation of exact correlated equilibrium in compact games. Games Econ. Behav. 91: 347-359 (2015) - [j19]Kevin Leyton-Brown, Panagiotis G. Ipeirotis:
Introduction to the Special Issue on EC'12. ACM Trans. Economics and Comput. 3(1): 1:1-1:2 (2015) - [c69]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Efficient Benchmarking of Hyperparameter Optimizers via Surrogates. AAAI 2015: 1114-1120 - [c68]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Runtime Prediction: Methods and Evaluation (Extended Abstract). IJCAI 2015: 4197-4201 - [c67]James R. Wright, Chris Thornton, Kevin Leyton-Brown:
Mechanical TA: Partially Automated High-Stakes Peer Grading. SIGCSE 2015: 96-101 - [i17]Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger H. Hoos, Kevin Leyton-Brown:
The Configurable SAT Solver Challenge (CSSC). CoRR abs/1505.01221 (2015) - [i16]Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren:
ASlib: A Benchmark Library for Algorithm Selection. CoRR abs/1506.02465 (2015) - 2014
- [j18]Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm runtime prediction: Methods & evaluation. Artif. Intell. 206: 79-111 (2014) - [j17]Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Lin Xu:
Understanding the empirical hardness of NP-complete problems. Commun. ACM 57(5): 98-107 (2014) - [j16]Michal Feldman, Kevin Leyton-Brown:
Introduction. Games Econ. Behav. 86: 339 (2014) - [c66]Chris Fawcett, Mauro Vallati, Frank Hutter, Jörg Hoffmann, Holger H. Hoos, Kevin Leyton-Brown:
Improved Features for Runtime Prediction of Domain-Independent Planners. ICAPS 2014 - [c65]Katharina Eggensperger, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
Surrogate Benchmarks for Hyperparameter Optimization. MetaSel@ECAI 2014: 24-31 - [c64]Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown:
An Efficient Approach for Assessing Hyperparameter Importance. ICML 2014: 754-762 - [c63]Frank Hutter, Manuel López-Ibáñez, Chris Fawcett, Marius Lindauer, Holger H. Hoos, Kevin Leyton-Brown, Thomas Stützle:
AClib: A Benchmark Library for Algorithm Configuration. LION 2014: 36-40 - [c62]Daniel Geschwender, Frank Hutter, Lars Kotthoff, Yuri Malitsky, Holger H. Hoos, Kevin Leyton-Brown:
Algorithm Configuration in the Cloud: A Feasibility Study. LION 2014: 41-46 - [c61]