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ORCIDJacob D. Abernethy
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- affiliation: University of Michigan, Department of Electrical Engineering and Computer Science
- affiliation: University of Pennsylvania, Computer and Information Science Department
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2020 – today
- 2024
[j9]Jun-Kun Wang
, Jacob D. Abernethy, Kfir Y. Levy:
No-regret dynamics in the Fenchel game: a unified framework for algorithmic convex optimization. Math. Program. 205(1): 203-268 (2024)- [c63]Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao:
Extragradient Type Methods for Riemannian Variational Inequality Problems. AISTATS 2024: 2080-2088 - [c62]Jacob D. Abernethy, Robert E. Schapire, Umar Syed:
Lexicographic Optimization: Algorithms and Stability. AISTATS 2024: 2503-2511 - [c61]Jacob D. Abernethy, Alekh Agarwal, Teodor Vanislavov Marinov, Manfred K. Warmuth:
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. ALT 2024: 3-46 - [i44]Leyan Pan, Vijay Ganesh, Jacob D. Abernethy, Chris Esposo, Wenke Lee:
Can Transformers Reason Logically? A Study in SAT Solving. CoRR abs/2410.07432 (2024) - 2023
- [j8]Yeojoon Youn, Bhuvesh Kumar, Jacob D. Abernethy:
Accelerated Federated Optimization with Quantization. IEEE Data Eng. Bull. 46(1): 79-123 (2023) - [c60]Feng Luo, Ling Liu, G. Geoff Wang, Vijay Kumar, Mark S. Ashton, Jacob D. Abernethy, Fatemeh Afghah, Matthew H. E. M. Browning, David Coyle, Philip M. Dames, Tom O'Halloran, James Hays, Patrick Hiesl, Chenfanfu Jiang, Puskar Khanal, Venkat Narayan Krovi, Sara Kuebbing, Nianyi Li, JingJing Liang, Ninghao Liu, Steve McNulty, Christopher M. Oswalt, Neil Pederson, Demetri Terzopoulos, Christopher W. Woodall, Yongkai Wu, Jian Yang, Yin Yang, Liang Zhao:
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI 2023: 1-10 - [c59]Zihao Hu, Guanghui Wang, Jacob D. Abernethy:
Minimizing Dynamic Regret on Geodesic Metric Spaces. COLT 2023: 4336-4383 - [c58]Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy:
On Accelerated Perceptrons and Beyond. ICLR 2023 - [c57]Zihao Hu, Guanghui Wang, Jacob D. Abernethy:
Riemannian Projection-free Online Learning. NeurIPS 2023 - [c56]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Faster Margin Maximization Rates for Generic Optimization Methods. NeurIPS 2023 - [i43]Zihao Hu, Guanghui Wang, Jacob D. Abernethy:
Minimizing Dynamic Regret on Geodesic Metric Spaces. CoRR abs/2302.08652 (2023) - [i42]Jacob D. Abernethy, Alekh Agarwal, Teodor V. Marinov, Manfred K. Warmuth:
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks. CoRR abs/2305.17040 (2023) - [i41]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Faster Margin Maximization Rates for Generic Optimization Methods. CoRR abs/2305.17544 (2023) - [i40]Zihao Hu, Guanghui Wang, Jacob D. Abernethy:
On Riemannian Projection-free Online Learning. CoRR abs/2305.19349 (2023) - [i39]Yeojoon Youn, Zihao Hu, Juba Ziani, Jacob D. Abernethy:
Randomized Quantization is All You Need for Differential Privacy in Federated Learning. CoRR abs/2306.11913 (2023) - [i38]Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy:
On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms. CoRR abs/2307.07675 (2023) - [i37]Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao:
Extragradient Type Methods for Riemannian Variational Inequality Problems. CoRR abs/2309.14155 (2023) - 2022
- [c55]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang:
Active Sampling for Min-Max Fairness. ICML 2022: 53-65 - [c54]Bhuvesh Kumar, Jacob D. Abernethy, Venkatesh Saligrama:
ActiveHedge: Hedge meets Active Learning. ICML 2022: 11694-11709 - [c53]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Adaptive Oracle-Efficient Online Learning. NeurIPS 2022 - [i36]Guanghui Wang, Rafael Hanashiro, Etash Kumar Guha, Jacob D. Abernethy:
On Accelerated Perceptrons and Beyond. CoRR abs/2210.09371 (2022) - [i35]Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy:
Adaptive Oracle-Efficient Online Learning. CoRR abs/2210.09385 (2022) - 2021
- [c52]Jun-Kun Wang, Jacob D. Abernethy:
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. ACML 2021: 17-32 - [c51]Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono:
Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. ALT 2021: 3-47 - [c50]Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy:
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network. ICML 2021: 10816-10827 - [c49]Yinglun Xu, Bhuvesh Kumar, Jacob D. Abernethy:
Observation-Free Attacks on Stochastic Bandits. NeurIPS 2021: 22550-22561 - [c48]Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono:
Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. SODA 2021: 1387-1404 - [i34]Jacob D. Abernethy, Pranjal Awasthi, Satyen Kale:
A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness. CoRR abs/2103.01276 (2021) - [i33]Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy:
Escaping Saddle Points Faster with Stochastic Momentum. CoRR abs/2106.02985 (2021) - [i32]Jun-Kun Wang, Jacob D. Abernethy, Kfir Y. Levy:
No-Regret Dynamics in the Fenchel Game: A Unified Framework for Algorithmic Convex Optimization. CoRR abs/2111.11309 (2021) - 2020
- [c47]Jacob D. Abernethy, Shivani Agarwal:
Conference on Learning Theory 2020: Preface. COLT 2020: 1-2 - [c46]Jun-Kun Wang, Chi-Heng Lin, Jacob D. Abernethy:
Escaping Saddle Points Faster with Stochastic Momentum. ICLR 2020 - [e1]Jacob D. Abernethy, Shivani Agarwal:
Conference on Learning Theory, COLT 2020, 9-12 July 2020, Virtual Event [Graz, Austria]. Proceedings of Machine Learning Research 125, PMLR 2020 [contents] - [i31]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang:
Adaptive Sampling to Reduce Disparate Performance. CoRR abs/2006.06879 (2020) - [i30]Yeojoon Youn, Neil Thistlethwaite, Sang Keun Choe, Jacob D. Abernethy:
Online Kernel based Generative Adversarial Networks. CoRR abs/2006.11432 (2020) - [i29]Jun-Kun Wang, Jacob D. Abernethy:
Quickly Finding a Benign Region via Heavy Ball Momentum in Non-Convex Optimization. CoRR abs/2010.01449 (2020) - [i28]Jun-Kun Wang, Jacob D. Abernethy:
Provable Acceleration of Neural Net Training via Polyak's Momentum. CoRR abs/2010.01618 (2020) - [i27]Jun-Kun Wang, Jacob D. Abernethy:
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron. CoRR abs/2010.01637 (2020) - [i26]Rafael Hanashiro, Jacob D. Abernethy:
Linear Separation via Optimism. CoRR abs/2011.08797 (2020)
2010 – 2019
- 2019
- [j7]Kanishka Misra, Eric M. Schwartz, Jacob D. Abernethy:
Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments. Mark. Sci. 38(2): 226-252 (2019) - [c45]Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu:
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. ICML 2019: 921-930 - [c44]Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via the Differential Privacy Lens. NeurIPS 2019: 8892-8902 - [c43]Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern:
Learning Auctions with Robust Incentive Guarantees. NeurIPS 2019: 11587-11597 - [i25]Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono:
Last-iterate convergence rates for min-max optimization. CoRR abs/1906.02027 (2019) - [i24]Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu:
Competing Against Equilibria in Zero-Sum Games with Evolving Payoffs. CoRR abs/1907.07723 (2019) - [i23]Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono:
Fictitious Play: Convergence, Smoothness, and Optimism. CoRR abs/1911.08418 (2019) - 2018
- [c42]Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang:
Faster Rates for Convex-Concave Games. COLT 2018: 1595-1625 - [c41]Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb:
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. KDD 2018: 5-14 - [c40]Jun-Kun Wang, Jacob D. Abernethy:
Acceleration through Optimistic No-Regret Dynamics. NeurIPS 2018: 3828-3838 - [i22]Jacob D. Abernethy, Kevin A. Lai, Kfir Y. Levy, Jun-Kun Wang:
Faster Rates for Convex-Concave Games. CoRR abs/1805.06792 (2018) - [i21]Jacob D. Abernethy, Alex Chojnacki, Arya Farahi, Eric M. Schwartz, Jared Webb:
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan. CoRR abs/1806.10692 (2018) - [i20]Jun-Kun Wang, Jacob D. Abernethy:
Acceleration through Optimistic No-Regret Dynamics. CoRR abs/1807.10455 (2018) - 2017
- [c39]Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz:
A Data Science Approach to Understanding Residential Water Contamination in Flint. KDD 2017: 1407-1416 - [c38]Jacob D. Abernethy, Jun-Kun Wang:
On Frank-Wolfe and Equilibrium Computation. NIPS 2017: 6584-6593 - [i19]Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy:
Addendum to "A Market Framework for Eliciting Private Data". CoRR abs/1703.00899 (2017) - [i18]Naveen Kodali, Jacob D. Abernethy, James Hays, Zsolt Kira:
How to Train Your DRAGAN. CoRR abs/1705.07215 (2017) - [i17]Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob D. Abernethy, Eric M. Schwartz:
A Data Science Approach to Understanding Residential Water Contamination in Flint. CoRR abs/1707.01591 (2017) - [i16]Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via Differential Privacy. CoRR abs/1711.10019 (2017) - 2016
- [c37]Jacob D. Abernethy, Elad Hazan:
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. ICML 2016: 2520-2528 - [c36]Jie Li, Paul Ozog, Jacob D. Abernethy, Ryan M. Eustice, Matthew Johnson-Roberson:
Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive Bayesian estimation. IROS 2016: 1230-1237 - [c35]Jacob D. Abernethy, Kareem Amin, Ruihao Zhu:
Threshold Bandits, With and Without Censored Feedback. NIPS 2016: 4889-4897 - [c34]Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky:
Rate of Price Discovery in Iterative Combinatorial Auctions. EC 2016: 809 - [c33]Mahmoud Azab, Rada Mihalcea, Jacob D. Abernethy:
Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor. SocInfo (1) 2016: 438-453 - [i15]Jacob D. Abernethy, Cyrus Anderson, Chengyu Dai, Arya Farahi, Linh Nguyen, Adam Rauh, Eric M. Schwartz, Wenbo Shen, Guangsha Shi, Jonathan C. Stroud, Xinyu Tan, Jared Webb, Sheng Yang:
Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing. CoRR abs/1610.00580 (2016) - [i14]Jacob D. Abernethy, Cyrus Anderson, Alex Chojnacki, Chengyu Dai, John Dryden, Eric M. Schwartz, Wenbo Shen, Jonathan C. Stroud, Laura Wendlandt, Sheng Yang, Daniel T. Zhang:
Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences. CoRR abs/1611.05788 (2016) - 2015
- [c32]Jacob D. Abernethy, Matthew Johnson-Roberson:
Financialized methods for market-based multi-sensor fusion. IROS 2015: 900-907 - [c31]Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari:
Fighting Bandits with a New Kind of Smoothness. NIPS 2015: 2197-2205 - [c30]Bo Waggoner, Rafael M. Frongillo, Jacob D. Abernethy:
A Market Framework for Eliciting Private Data. NIPS 2015: 3510-3518 - [c29]Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner:
Low-Cost Learning via Active Data Procurement. EC 2015: 619-636 - [i13]Jacob D. Abernethy, Yiling Chen, Chien-Ju Ho, Bo Waggoner:
Actively Purchasing Data for Learning. CoRR abs/1502.05774 (2015) - [i12]Jacob D. Abernethy, Elad Hazan:
Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier. CoRR abs/1507.02528 (2015) - [i11]Jacob D. Abernethy, Sébastien Lahaie, Matus Telgarsky:
Rate of Price Discovery in Iterative Combinatorial Auctions. CoRR abs/1511.06017 (2015) - [i10]Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari:
Fighting Bandits with a New Kind of Smoothness. CoRR abs/1512.04152 (2015) - 2014
- [j6]Jacob D. Abernethy, Rafael M. Frongillo, Sindhu Kutty:
On risk measures, market making, and exponential families. SIGecom Exch. 13(2): 21-25 (2014) - [c28]Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari:
Online Linear Optimization via Smoothing. COLT 2014: 807-823 - [c27]Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami:
Information aggregation in exponential family markets. EC 2014: 395-412 - [c26]Jacob D. Abernethy, Rafael M. Frongillo, Xiaolong Li, Jennifer Wortman Vaughan:
A general volume-parameterized market making framework. EC 2014: 413-430 - [c25]Qingsi Wang, Shang-Pin Sheng, Jacob D. Abernethy, Mingyan Liu:
Jamming defense against a resource-replenishing adversary in multi-channel wireless systems. WiOpt 2014: 210-217 - [i9]Jacob D. Abernethy, Sindhu Kutty, Sébastien Lahaie, Rahul Sami:
Information Aggregation in Exponential Family Markets. CoRR abs/1402.5458 (2014) - [i8]Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari:
Online Linear Optimization via Smoothing. CoRR abs/1405.6076 (2014) - 2013
- [j5]Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan:
Efficient Market Making via Convex Optimization, and a Connection to Online Learning. ACM Trans. Economics and Comput. 1(2): 12:1-12:39 (2013) - [c24]Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief:
Large-Scale Bandit Problems and KWIK Learning. ICML (1) 2013: 588-596 - [c23]Jacob D. Abernethy, Satyen Kale:
Adaptive Market Making via Online Learning. NIPS 2013: 2058-2066 - [c22]Jacob D. Abernethy, Peter L. Bartlett, Rafael M. Frongillo, Andre Wibisono:
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. NIPS 2013: 2346-2354 - [c21]H. Brendan McMahan, Jacob D. Abernethy:
Minimax Optimal Algorithms for Unconstrained Linear Optimization. NIPS 2013: 2724-2732 - 2012
- [j4]Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin:
Interior-Point Methods for Full-Information and Bandit Online Learning. IEEE Trans. Inf. Theory 58(7): 4164-4175 (2012) - [c20]Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono:
Minimax option pricing meets black-scholes in the limit. STOC 2012: 1029-1040 - [c19]Jacob D. Abernethy, Rafael M. Frongillo:
A Characterization of Scoring Rules for Linear Properties. COLT 2012: 27.1-27.13 - [i7]Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono:
Minimax Option Pricing Meets Black-Scholes in the Limit. CoRR abs/1202.2585 (2012) - 2011
- [b1]Jacob D. Abernethy:
Sequential Decision Making in Non-stochastic Environments. University of California, Berkeley, USA, 2011 - [c18]Jacob D. Abernethy, Rafael M. Frongillo:
A Collaborative Mechanism for Crowdsourcing Prediction Problems. NIPS 2011: 2600-2608 - [c17]Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan:
An optimization-based framework for automated market-making. EC 2011: 297-306 - [c16]Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan:
Blackwell Approachability and No-Regret Learning are Equivalent. COLT 2011: 27-46 - [c15]Jacob D. Abernethy, Shie Mannor:
Does an Efficient Calibrated Forecasting Strategy Exist? COLT 2011: 809-812 - [i6]Jacob D. Abernethy, Rafael M. Frongillo:
A Collaborative Mechanism for Crowdsourcing Prediction Problems. CoRR abs/1111.2664 (2011) - 2010
- [j3]Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo:
Graph regularization methods for Web spam detection. Mach. Learn. 81(2): 207-225 (2010) - [c14]Jacob D. Abernethy, Peter L. Bartlett, Niv Buchbinder, Isabelle Stanton:
A Regularization Approach to Metrical Task Systems. ALT 2010: 270-284 - [c13]Jacob D. Abernethy:
Can We Learn to Gamble Efficiently? COLT 2010: 318-319 - [c12]Jacob D. Abernethy, Manfred K. Warmuth:
Repeated Games against Budgeted Adversaries. NIPS 2010: 1-9 - [i5]Jacob D. Abernethy, Peter L. Bartlett, Elad Hazan:
Blackwell Approachability and Low-Regret Learning are Equivalent. CoRR abs/1011.1936 (2010) - [i4]Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan:
An Optimization-Based Framework for Automated Market-Making. CoRR abs/1011.1941 (2010)
2000 – 2009
- 2009
- [j2]Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert:
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. J. Mach. Learn. Res. 10: 803-826 (2009) - [c11]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. COLT 2009 - [c10]Jacob D. Abernethy, Alexander Rakhlin:
Beating the Adaptive Bandit with High Probability. COLT 2009 - [c9]Jacob D. Abernethy, Alexander Rakhlin:
An Efficient Bandit Algorithm for sqrt(T) Regret in Online Multiclass Prediction?. COLT 2009 - [c8]Jacob D. Abernethy, Manfred K. Warmuth:
Minimax Games with Bandits. COLT 2009 - [i3]Jacob D. Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin:
A Stochastic View of Optimal Regret through Minimax Duality. CoRR abs/0903.5328 (2009) - 2008
- [j1]Jacob D. Abernethy, Theodoros Evgeniou, Olivier Toubia, Jean-Philippe Vert:
Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires. IEEE Trans. Knowl. Data Eng. 20(2): 145-155 (2008) - [c7]Jacob D. Abernethy, Olivier Chapelle, Carlos Castillo:
Web spam identification through content and hyperlinks. AIRWeb 2008: 41-44 - [c6]Jacob D. Abernethy, Elad Hazan, Alexander Rakhlin:
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization. COLT 2008: 263-274 - [c5]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari:
Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424 - [c4]Jacob D. Abernethy, Manfred K. Warmuth, Joel Yellin:
When Random Play is Optimal Against an Adversary. COLT 2008: 437-446 - [i2]Francis R. Bach, Jacob D. Abernethy, Jean-Philippe Vert, Theodoros Evgeniou:
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. CoRR abs/0802.1430 (2008) - 2007
- [c3]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin:
Multitask Learning with Expert Advice. COLT 2007: 484-498 - [c2]Alexander Rakhlin, Jacob D. Abernethy, Peter L. Bartlett:
Online discovery of similarity mappings. ICML 2007: 767-774 - 2006
- [c1]Jacob D. Abernethy, John Langford, Manfred K. Warmuth:
Continuous Experts and the Binning Algorithm. COLT 2006: 544-558 - [i1]Jacob D. Abernethy, Francis R. Bach, Theodoros Evgeniou, Jean-Philippe Vert:
Low-rank matrix factorization with attributes. CoRR abs/cs/0611124 (2006)
Coauthor Index
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