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ORCIDAvrim Blum
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- affiliation: Toyota Technological Institute at Chicago, IL, USA
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
[c169]Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl:
Agnostic Multi-Robust Learning using ERM. AISTATS 2024: 2242-2250- [c168]Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl:
On the Vulnerability of Fairness Constrained Learning to Malicious Noise. AISTATS 2024: 4096-4104 - [c167]Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj
, Aadirupa Saha, Yuanyuan Yang:
Dueling Optimization with a Monotone Adversary. ALT 2024: 221-243 - [c166]Avrim Blum, Melissa Dutz:
Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round. ITCS 2024: 18:1-18:18 - [i66]Avrim Blum, Melissa Dutz:
Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round. CoRR abs/2404.00150 (2024) - [i65]Avrim Blum, Kavya Ravichandran:
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem. CoRR abs/2404.01198 (2024) - [i64]Keziah Naggita, Matthew R. Walter, Avrim Blum:
Learning Actionable Counterfactual Explanations in Large State Spaces. CoRR abs/2404.17034 (2024) - [i63]Vaidehi Srinivas, Avrim Blum:
Competitive strategies to use "warm start" algorithms with predictions. CoRR abs/2405.03661 (2024) - [i62]Saba Ahmadi, Siddharth Bhandari, Avrim Blum, Chen Dan, Prabhav Jain:
Distributional Adversarial Loss. CoRR abs/2406.03458 (2024) - [i61]Emily Diana, Alexander Williams Tolbert, Kavya Ravichandran, Avrim Blum:
Adaptive Algorithmic Interventions for Escaping Pessimism Traps in Dynamic Sequential Decisions. CoRR abs/2406.04462 (2024) - [i60]Avrim Blum, Kavya Ravichandran:
A Model for Combinatorial Dictionary Learning and Inference. CoRR abs/2407.18436 (2024) - 2023
- [j62]Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
An Analysis of Robustness of Non-Lipschitz Networks. J. Mach. Learn. Res. 24: 98:1-98:43 (2023) - [c165]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
Setting Fair Incentives to Maximize Improvement. FORC 2023: 5:1-5:22 - [c164]Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter:
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. NeurIPS 2023 - [c163]Han Shao, Avrim Blum, Omar Montasser:
Strategic Classification under Unknown Personalized Manipulation. NeurIPS 2023 - [c162]Saba Ahmadi, Avrim Blum, Kunhe Yang:
Fundamental Bounds on Online Strategic Classification. EC 2023: 22-58 - [i59]Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter:
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback. CoRR abs/2302.03805 (2023) - [i58]Saba Ahmadi, Avrim Blum, Kunhe Yang:
Fundamental Bounds on Online Strategic Classification. CoRR abs/2302.12355 (2023) - [i57]Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl:
Certifiable (Multi)Robustness Against Patch Attacks Using ERM. CoRR abs/2303.08944 (2023) - [i56]Han Shao, Avrim Blum, Omar Montasser:
Strategic Classification under Unknown Personalized Manipulation. CoRR abs/2305.16501 (2023) - [i55]Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin Stangl:
On the Vulnerability of Fairness Constrained Learning to Malicious Noise. CoRR abs/2307.11892 (2023) - [i54]Avrim Blum, Meghal Gupta, Gene Li, Naren Sarayu Manoj, Aadirupa Saha, Yuanyuan Yang:
Dueling Optimization with a Monotone Adversary. CoRR abs/2311.11185 (2023) - 2022
- [c161]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. COLT 2022: 4498-4534 - [c160]Avrim Blum, Kevin Stangl, Ali Vakilian:
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline. FAccT 2022: 1178-1193 - [c159]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
On Classification of Strategic Agents Who Can Both Game and Improve. FORC 2022: 3:1-3:22 - [c158]Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang:
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. NeurIPS 2022 - [c157]Han Shao, Omar Montasser, Avrim Blum:
A Theory of PAC Learnability under Transformation Invariances. NeurIPS 2022 - [c156]Soheil Behnezhad, Avrim Blum, Mahsa Derakhshan:
Stochastic Vertex Cover with Few Queries. SODA 2022: 1808-1846 - [i53]Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang:
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. CoRR abs/2202.05920 (2022) - [i52]Han Shao, Omar Montasser, Avrim Blum:
A Theory of PAC Learnability under Transformation Invariances. CoRR abs/2202.07552 (2022) - [i51]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
On classification of strategic agents who can both game and improve. CoRR abs/2203.00124 (2022) - [i50]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
Setting Fair Incentives to Maximize Improvement. CoRR abs/2203.00134 (2022) - [i49]Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. CoRR abs/2203.04160 (2022) - [i48]Avrim Blum, Kevin Stangl, Ali Vakilian:
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline. CoRR abs/2203.07513 (2022) - 2021
- [c155]Avrim Blum, Shelby Heinecke, Lev Reyzin:
Communication-Aware Collaborative Learning. AAAI 2021: 6786-6793 - [c154]Avrim Blum, Chen Dan, Saeed Seddighin:
Learning Complexity of Simulated Annealing. AISTATS 2021: 1540-1548 - [c153]Avrim Blum, Steve Hanneke, Jian Qian, Han Shao:
Robust learning under clean-label attack. COLT 2021: 591-634 - [c152]Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao:
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. ICML 2021: 1005-1014 - [c151]Naren Manoj, Avrim Blum:
Excess Capacity and Backdoor Poisoning. NeurIPS 2021: 20373-20384 - [c150]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
The Strategic Perceptron. EC 2021: 6-25 - [c149]Avrim Blum, Paul Gölz:
Incentive-Compatible Kidney Exchange in a Slightly Semi-Random Model. EC 2021: 138-156 - [i47]Avrim Blum, Steve Hanneke, Jian Qian, Han Shao:
Robust learning under clean-label attack. CoRR abs/2103.00671 (2021) - [i46]Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao:
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. CoRR abs/2103.03228 (2021) - [i45]Avrim Blum, Paul Gölz:
Incentive-Compatible Kidney Exchange in a Slightly Semi-Random Model. CoRR abs/2106.11387 (2021) - [i44]Naren Sarayu Manoj, Avrim Blum:
Excess Capacity and Backdoor Poisoning. CoRR abs/2109.00685 (2021) - [i43]Soheil Behnezhad, Avrim Blum, Mahsa Derakhshan:
Stochastic Vertex Cover with Few Queries. CoRR abs/2112.05415 (2021) - 2020
- [j61]Avrim Blum:
Technical perspective: Algorithm selection as a learning problem. Commun. ACM 63(6): 86 (2020) - [j60]Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma:
Ignorance Is Almost Bliss: Near-Optimal Stochastic Matching with Few Queries. Oper. Res. 68(1): 16-34 (2020) - [j59]Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang:
Random Smoothing Might be Unable to Certify L∞ Robustness for High-Dimensional Images. J. Mach. Learn. Res. 21: 211:1-211:21 (2020) - [j58]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong learning in costly feature spaces. Theor. Comput. Sci. 808: 14-37 (2020) - [c148]Arturs Backurs, Avrim Blum, Neha Gupta:
Active Local Learning. COLT 2020: 363-390 - [c147]Avrim Blum, Kevin Stangl:
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? FORC 2020: 3:1-3:20 - [c146]Avrim Blum, Thodoris Lykouris:
Advancing Subgroup Fairness via Sleeping Experts. ITCS 2020: 55:1-55:24 - [c145]Avrim Blum, Han Shao:
Online Learning with Primary and Secondary Losses. NeurIPS 2020 - [p2]Avrim Blum:
Approximation Stability and Proxy Objectives. Beyond the Worst-Case Analysis of Algorithms 2020: 120-139 - [i42]Avrim Blum, Travis Dick, Naren Manoj, Hongyang Zhang:
Random Smoothing Might be Unable to Certify 𝓁∞ Robustness for High-Dimensional Images. CoRR abs/2002.03517 (2020) - [i41]Avrim Blum, Chen Dan, Saeed Seddighin:
Learning Complexity of Simulated Annealing. CoRR abs/2003.02981 (2020) - [i40]Saba Ahmadi, Hedyeh Beyhaghi, Avrim Blum, Keziah Naggita:
The Strategic Perceptron. CoRR abs/2008.01710 (2020) - [i39]Arturs Backurs, Avrim Blum, Neha Gupta:
Active Local Learning. CoRR abs/2008.13374 (2020) - [i38]Avrim Blum, Yishay Mansour:
Kidney exchange and endless paths: On the optimal use of an altruistic donor. CoRR abs/2010.01645 (2020) - [i37]Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang:
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness. CoRR abs/2010.06154 (2020) - [i36]Avrim Blum, Han Shao:
Online Learning with Primary and Secondary Losses. CoRR abs/2010.14670 (2020) - [i35]Avrim Blum, Shelby Heinecke, Lev Reyzin:
Communication-Aware Collaborative Learning. CoRR abs/2012.10569 (2020)
2010 – 2019
- 2019
- [j57]Avrim Blum, Sariel Har-Peled, Benjamin Raichel:
Sparse Approximation via Generating Point Sets. ACM Trans. Algorithms 15(3): 32:1-32:16 (2019) - [c144]Soheil Behnezhad, Avrim Blum, Mahsa Derakhshan, Mohammad Taghi Hajiaghayi, Christos H. Papadimitriou, Saeed Seddighin:
Optimal Strategies of Blotto Games: Beyond Convexity. EC 2019: 597-616 - [c143]Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan:
Bilu-Linial Stability, Certified Algorithms and the Independent Set Problem. ESA 2019: 7:1-7:16 - [c142]Avrim Blum, Nika Haghtalab, MohammadTaghi Hajiaghayi, Saeed Seddighin:
Computing Stackelberg Equilibria of Large General-Sum Games. SAGT 2019: 168-182 - [i34]Soheil Behnezhad, Avrim Blum, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Christos H. Papadimitriou, Saeed Seddighin:
Optimal Strategies of Blotto Games: Beyond Convexity. CoRR abs/1901.04153 (2019) - [i33]Avrim Blum, Nika Haghtalab, MohammadTaghi Hajiaghayi, Saeed Seddighin:
Computing Stackelberg Equilibria of Large General-Sum Games. CoRR abs/1909.03319 (2019) - [i32]Avrim Blum, Thodoris Lykouris:
Advancing subgroup fairness via sleeping experts. CoRR abs/1909.08375 (2019) - [i31]Avrim Blum, Kevin Stangl:
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy? CoRR abs/1912.01094 (2019) - 2018
- [j56]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. ACM Trans. Economics and Comput. 6(3-4): 13:1-13:20 (2018) - [c141]Avrim Blum, Nika Haghtalab:
Algorithms for Generalized Topic Modeling. AAAI 2018: 2730-2737 - [c140]Maria-Florina Balcan, Avrim Blum, Shang-Tse Chen:
Diversified Strategies for Mitigating Adversarial Attacks in Multiagent Systems. AAMAS 2018: 407-415 - [c139]Avrim Blum, Lunjia Hu:
Active Tolerant Testing. COLT 2018: 474-497 - [c138]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang:
Approximate Convex Hull of Data Streams. ICALP 2018: 21:1-21:13 - [c137]Avrim Blum, Yishay Mansour:
On Price versus Quality. ITCS 2018: 16:1-16:12 - [c136]Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nati Srebro:
On preserving non-discrimination when combining expert advice. NeurIPS 2018: 8386-8397 - [c135]Soheil Behnezhad, Avrim Blum, Mahsa Derakhshan, Mohammad Taghi Hajiaghayi, Mohammad Mahdian, Christos H. Papadimitriou, Ronald L. Rivest, Saeed Seddighin, Philip B. Stark:
From Battlefields to Elections: Winning Strategies of Blotto and Auditing Games. SODA 2018: 2291-2310 - [e2]Avrim Blum:
10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA. LIPIcs 124, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2018, ISBN 978-3-95977-095-8 [contents] - [i30]Haris Angelidakis, Pranjal Awasthi, Avrim Blum, Vaggos Chatziafratis, Chen Dan:
Bilu-Linial stability, certified algorithms and the Independent Set problem. CoRR abs/1810.08414 (2018) - [i29]Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nathan Srebro:
On preserving non-discrimination when combining expert advice. CoRR abs/1810.11829 (2018) - 2017
- [c134]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. ALT 2017: 250-287 - [c133]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. COLT 2017: 127-150 - [c132]Avrim Blum, Yishay Mansour:
Efficient Co-Training of Linear Separators under Weak Dependence. COLT 2017: 302-318 - [c131]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao:
Collaborative PAC Learning. NIPS 2017: 2392-2401 - [c130]Avrim Blum, Ioannis Caragiannis, Nika Haghtalab, Ariel D. Procaccia, Eviatar B. Procaccia, Rohit Vaish:
Opting Into Optimal Matchings. SODA 2017: 2351-2363 - [i28]Pranjal Awasthi, Avrim Blum, Nika Haghtalab, Yishay Mansour:
Efficient PAC Learning from the Crowd. CoRR abs/1703.07432 (2017) - [i27]Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan:
Lifelong Learning in Costly Feature Spaces. CoRR abs/1706.10271 (2017) - [i26]Avrim Blum, Lunjia Hu:
Active Tolerant Testing. CoRR abs/1711.00388 (2017) - [i25]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang:
Approximate Convex Hull of Data Streams. CoRR abs/1712.04564 (2017) - 2016
- [c129]Rohit Vaish, Neeldhara Misra, Shivani Agarwal, Avrim Blum:
On the Computational Hardness of Manipulating Pairwise Voting Rules. AAMAS 2016: 358-367 - [c128]Avrim Blum, Sariel Har-Peled, Benjamin Raichel:
Sparse Approximation via Generating Point Sets. SODA 2016: 548-557 - [r1]Avrim Blum:
Semi-supervised Learning. Encyclopedia of Algorithms 2016: 1936-1941 - [i24]Avrim Blum, Ioannis Caragiannis, Nika Haghtalab, Ariel D. Procaccia, Eviatar B. Procaccia, Rohit Vaish:
Opting Into Optimal Matchings. CoRR abs/1609.04051 (2016) - [i23]Avrim Blum, Nika Haghtalab:
Generalized Topic Modeling. CoRR abs/1611.01259 (2016) - 2015
- [j55]Avrim Blum, Philip M. Long:
Special Issue on New Theoretical Challenges in Machine Learning. Algorithmica 72(1): 191-192 (2015) - [c127]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning Valuation Distributions from Partial Observation. AAAI 2015: 798-804 - [c126]Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
Efficient Representations for Lifelong Learning and Autoencoding. COLT 2015: 191-210 - [c125]Avrim Blum, Moritz Hardt:
The Ladder: A Reliable Leaderboard for Machine Learning Competitions. ICML 2015: 1006-1014 - [c124]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. ITCS 2015: 173-180 - [c123]Maria-Florina Balcan, Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Commitment Without Regrets: Online Learning in Stackelberg Security Games. EC 2015: 61-78 - [c122]Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, Ankit Sharma:
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries. EC 2015: 325-342 - [c121]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. EC 2015: 601-618 - [c120]Avrim Blum, Yishay Mansour, Liu Yang:
Online Allocation and Pricing with Economies of Scale. WINE 2015: 159-172 - [i22]Avrim Blum, Moritz Hardt:
The Ladder: A Reliable Leaderboard for Machine Learning Competitions. CoRR abs/1502.04585 (2015) - [i21]Avrim Blum, Sariel Har-Peled, Benjamin Raichel:
Sparse Approximation via Generating Point Sets. CoRR abs/1507.02574 (2015) - 2014
- [c119]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Lazy Defenders Are Almost Optimal against Diligent Attackers. AAAI 2014: 573-579 - [c118]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia:
Learning Optimal Commitment to Overcome Insecurity. NIPS 2014: 1826-1834 - [c117]Maria-Florina Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song:
Active Learning and Best-Response Dynamics. NIPS 2014: 2222-2230 - [c116]Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan:
Learning Mixtures of Ranking Models. NIPS 2014: 2609-2617 - [c115]Emmanouil Antonios Platanios, Avrim Blum, Tom M. Mitchell:
Estimating Accuracy from Unlabeled Data. UAI 2014: 682-691 - [i20]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. CoRR abs/1402.4488 (2014) - [i19]Maria-Florina Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song:
Active Learning and Best-Response Dynamics. CoRR abs/1406.6633 (2014) - [i18]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning Valuation Distributions from Partial Observation. CoRR abs/1407.2855 (2014) - [i17]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Ankit Sharma:
Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries. CoRR abs/1407.4094 (2014) - [i16]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning What's going on: reconstructing preferences and priorities from opaque transactions. CoRR abs/1408.6575 (2014) - [i15]Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan:
Learning Mixtures of Ranking Models. CoRR abs/1410.8750 (2014) - [i14]Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
Efficient Representations for Life-Long Learning and Autoencoding. CoRR abs/1411.1490 (2014) - 2013
- [j54]Maria-Florina Balcan, Avrim Blum, Anupam Gupta:
Clustering under approximation stability. J. ACM 60(2): 8:1-8:34 (2013) - [j53]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to noninteractive database privacy. J. ACM 60(2): 12:1-12:25 (2013) - [j52]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior. SIAM J. Comput. 42(1): 230-264 (2013) - [j51]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
The Price of Uncertainty. ACM Trans. Economics and Comput. 1(3): 15:1-15:29 (2013) - [c114]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. APPROX-RANDOM 2013: 395-410 - [c113]Nina Balcan, Avrim Blum, Yishay Mansour:
Exploiting Ontology Structures and Unlabeled Data for Learning. ICML (3) 2013: 1112-1120 - [c112]Liu Yang, Avrim Blum, Jaime G. Carbonell:
Learnability of DNF with representation-specific queries. ITCS 2013: 37-46 - [c111]Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet:
Differentially private data analysis of social networks via restricted sensitivity. ITCS 2013: 87-96 - [c110]Avrim Blum, Anupam Gupta, Ariel D. Procaccia, Ankit Sharma:
Harnessing the power of two crossmatches. EC 2013: 123-140 - 2012
- [j50]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Center-based clustering under perturbation stability. Inf. Process. Lett. 112(1-2): 49-54 (2012) - [c109]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. APPROX-RANDOM 2012: 25-36 - [c108]Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang:
Active Property Testing. FOCS 2012: 21-30 - [c107]Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet:
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy. FOCS 2012: 410-419 - [c106]Maria-Florina Balcan, Avrim Blum, Shai Fine, Yishay Mansour:
Distributed Learning, Communication Complexity and Privacy. COLT 2012: 26.1-26.22 - [i13]Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet:
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy. CoRR abs/1204.2136 (2012) - [i12]Maria-Florina Balcan, Avrim Blum, Shai Fine, Yishay Mansour:
Distributed Learning, Communication Complexity and Privacy. CoRR abs/1204.3514 (2012) - [i11]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. CoRR abs/1206.3334 (2012) - [i10]Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet:
Differentially Private Data Analysis of Social Networks via Restricted Sensitivity. CoRR abs/1208.4586 (2012) - 2011
- [c105]Avrim Blum, Anupam Gupta, Yishay Mansour, Ankit Sharma:
Welfare and Profit Maximization with Production Costs. FOCS 2011: 77-86 - [i9]Avrim Blum, Katrina Ligett, Aaron Roth:
A Learning Theory Approach to Non-Interactive Database Privacy. CoRR abs/1109.2229 (2011) - [i8]Avrim Blum, Anupam Gupta, Yishay Mansour, Ankit Sharma:
Welfare and Profit Maximization with Production Costs. CoRR abs/1110.4992 (2011) - [i7]Maria-Florina Balcan, Eric Blais, Avrim Blum, Liu Yang:
Active Testing. CoRR abs/1111.0897 (2011) - [i6]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. CoRR abs/1111.6842 (2011) - 2010
- [j49]Maria-Florina Balcan, Avrim Blum:
A discriminative model for semi-supervised learning. J. ACM 57(3): 19:1-19:46 (2010) - [j48]Avrim Blum, Eyal Even-Dar, Katrina Ligett:
Routing Without Regret: On Convergence to Nash Equilibria of Regret-Minimizing Algorithms in Routing Games. Theory Comput. 6(1): 179-199 (2010) - [c104]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Improved Guarantees for Agnostic Learning of Disjunctions. COLT 2010: 359-367 - [c103]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Stability Yields a PTAS for k-Median and k-Means Clustering. FOCS 2010: 309-318 - [c102]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior. ICS 2010: 200-213 - [c101]Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum:
Trading off Mistakes and Don't-Know Predictions. NIPS 2010: 2092-2100 - [c100]Pranjal Awasthi, Maria-Florina Balcan, Avrim Blum, Or Sheffet, Santosh S. Vempala:
On Nash-Equilibria of Approximation-Stable Games. SAGT 2010: 78-89 - [i5]Pranjal Awasthi, Avrim Blum, Or Sheffet:
Center-based Clustering under Perturbation Stability. CoRR abs/1009.3594 (2010)
2000 – 2009
- 2009
- [j47]Sharath R. Cholleti, Sally A. Goldman, Avrim Blum, David G. Politte, Steven Don, Kirk E. Smith, Fred W. Prior:
Veritas: Combining Expert Opinions without Labeled Data. Int. J. Artif. Intell. Tools 18(5): 633-651 (2009) - [c99]Shobha Venkataraman, Avrim Blum, Dawn Song, Subhabrata Sen, Oliver Spatscheck:
Tracking Dynamic Sources of Malicious Activity at Internet Scale. NIPS 2009: 1946-1954 - [c98]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
The price of uncertainty. EC 2009: 285-294 - [c97]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
Improved equilibria via public service advertising. SODA 2009: 728-737 - [c96]Maria-Florina Balcan, Avrim Blum, Anupam Gupta:
Approximate clustering without the approximation. SODA 2009: 1068-1077 - 2008
- [j46]Maria-Florina Balcan, Avrim Blum, Jason D. Hartline, Yishay Mansour:
Reducing mechanism design to algorithm design via machine learning. J. Comput. Syst. Sci. 74(8): 1245-1270 (2008) - [j45]Maria-Florina Balcan, Avrim Blum, Nathan Srebro:
A theory of learning with similarity functions. Mach. Learn. 72(1-2): 89-112 (2008) - [j44]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
Item pricing for revenue maximization. SIGecom Exch. 7(3) (2008) - [c95]Maria-Florina Balcan, Avrim Blum:
Clustering with Interactive Feedback. ALT 2008: 316-328 - [c94]Maria-Florina Balcan, Avrim Blum, Nathan Srebro:
Improved Guarantees for Learning via Similarity Functions. COLT 2008: 287-298 - [c93]Sharath R. Cholleti, Sally A. Goldman, Avrim Blum, David G. Politte, Steven Don:
Veritas: Combining Expert Opinions without Labeled Data. ICTAI (1) 2008: 45-52 - [c92]Shobha Venkataraman, Avrim Blum, Dawn Song:
Limits of Learning-based Signature Generation with Adversaries. NDSS 2008 - [c91]Maria-Florina Balcan, Avrim Blum, Yishay Mansour:
Item pricing for revenue maximization. EC 2008: 50-59 - [c90]Avrim Blum, MohammadTaghi Hajiaghayi, Katrina Ligett, Aaron Roth:
Regret minimization and the price of total anarchy. STOC 2008: 373-382 - [c89]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to non-interactive database privacy. STOC 2008: 609-618 - [c88]Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
A discriminative framework for clustering via similarity functions. STOC 2008: 671-680 - 2007
- [j43]Avrim Blum, Yishay Mansour:
From External to Internal Regret. J. Mach. Learn. Res. 8: 1307-1324 (2007) - [j42]Avrim Blum, Gábor Lugosi, Hans Ulrich Simon:
Introduction to the special issue on COLT 2006. Mach. Learn. 69(2-3): 75-77 (2007) - [j41]Avrim Blum, Shuchi Chawla, David R. Karger, Terran Lane, Adam Meyerson, Maria Minkoff:
Approximation Algorithms for Orienteering and Discounted-Reward TSP. SIAM J. Comput. 37(2): 653-670 (2007) - [j40]Maria-Florina Balcan, Avrim Blum:
Mechanism design, machine learning, and pricing problems. SIGecom Exch. 7(1): 34-36 (2007) - [j39]Maria-Florina Balcan, Avrim Blum:
Approximation Algorithms and Online Mechanisms for Item Pricing. Theory Comput. 3(1): 179-195 (2007) - [c87]Avrim Blum:
A Theory of Similarity Functions for Learning and Clustering. ALT 2007: 9 - [c86]Avrim Blum, Maria-Florina Balcan:
Open Problems in Efficient Semi-supervised PAC Learning. COLT 2007: 622-624 - [c85]Avrim Blum:
A Theory of Similarity Functions for Learning and Clustering. Discovery Science 2007: 39 - [c84]Avrim Blum, Amin Coja-Oghlan, Alan M. Frieze, Shuheng Zhou:
Separating Populations with Wide Data: A Spectral Analysis. ISAAC 2007: 439-451 - [c83]David J. Abraham, Avrim Blum, Tuomas Sandholm:
Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges. EC 2007: 295-304 - [c82]Maria-Florina Balcan, Avrim Blum, T.-H. Hubert Chan, MohammadTaghi Hajiaghayi:
A Theory of Loss-Leaders: Making Money by Pricing Below Cost. WINE 2007: 293-299 - 2006
- [j38]Avrim Blum, Tuomas Sandholm, Martin Zinkevich:
Online algorithms for market clearing. J. ACM 53(5): 845-879 (2006) - [j37]Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
Kernels as features: On kernels, margins, and low-dimensional mappings. Mach. Learn. 65(1): 79-94 (2006) - [c81]Avrim Blum, T.-H. Hubert Chan, Mugizi Robert Rwebangira:
A Random-Surfer Web-Graph Model. ANALCO 2006: 238-246 - [c80]Shobha Venkataraman, Juan Caballero, Dawn Song, Avrim Blum, Jennifer Yates:
Black Box Anomaly Detection: Is It Utopian?. HotNets 2006 - [c79]Maria-Florina Balcan, Avrim Blum:
On a theory of learning with similarity functions. ICML 2006: 73-80 - [c78]Avrim Blum, Eyal Even-Dar, Katrina Ligett:
Routing without regret: on convergence to nash equilibria of regret-minimizing algorithms in routing games. PODC 2006: 45-52 - [c77]Maria-Florina Balcan, Avrim Blum:
Approximation algorithms and online mechanisms for item pricing. EC 2006: 29-35 - [p1]Maria-Florina Balcan, Avrim Blum:
An Augmented PAC Model for Semi-Supervised Learning. Semi-Supervised Learning 2006: 396-419 - 2005
- [j36]Yossi Azar, Avrim Blum, David P. Bunde, Yishay Mansour:
Combining Online Algorithms for Acceptance and Rejection. Theory Comput. 1(1): 105-117 (2005) - [c76]Maria-Florina Balcan, Avrim Blum:
A PAC-Style Model for Learning from Labeled and Unlabeled Data. COLT 2005: 111-126 - [c75]Avrim Blum, Yishay Mansour:
From External to Internal Regret. COLT 2005: 621-636 - [c74]Maria-Florina Balcan, Avrim Blum, Jason D. Hartline, Yishay Mansour:
Mechanism Design via Machine Learning. FOCS 2005: 605-614 - [c73]Shobha Venkataraman, Dawn Xiaodong Song, Phillip B. Gibbons, Avrim Blum:
New Streaming Algorithms for Fast Detection of Superspreaders. NDSS 2005 - [c72]Avrim Blum, Cynthia Dwork, Frank McSherry, Kobbi Nissim:
Practical privacy: the SuLQ framework. PODS 2005: 128-138 - [c71]Avrim Blum:
Random Projection, Margins, Kernels, and Feature-Selection. SLSFS 2005: 52-68 - [c70]Avrim Blum, Jason D. Hartline:
Near-optimal online auctions. SODA 2005: 1156-1163 - 2004
- [j35]Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, Martin Zinkevich:
Preference Elicitation and Query Learning. J. Mach. Learn. Res. 5: 649-667 (2004) - [j34]Nikhil Bansal, Avrim Blum, Shuchi Chawla:
Correlation Clustering. Mach. Learn. 56(1-3): 89-113 (2004) - [j33]Avrim Blum, Vijay Kumar, Atri Rudra, Felix Wu:
Online learning in online auctions. Theor. Comput. Sci. 324(2-3): 137-146 (2004) - [c69]Maria-Florina Balcan, Avrim Blum, Santosh S. Vempala:
On Kernels, Margins, and Low-Dimensional Mappings. ALT 2004: 194-205 - [c68]H. Brendan McMahan, Avrim Blum:
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary. COLT 2004: 109-123 - [c67]Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy:
Semi-supervised learning using randomized mincuts. ICML 2004 - [c66]Maria-Florina Balcan, Avrim Blum, Ke Yang:
Co-Training and Expansion: Towards Bridging Theory and Practice. NIPS 2004: 89-96 - [c65]Avrim Blum, Dawn Xiaodong Song, Shobha Venkataraman:
Detection of Interactive Stepping Stones: Algorithms and Confidence Bounds. RAID 2004: 258-277 - [c64]Nikhil Bansal, Avrim Blum, Shuchi Chawla, Adam Meyerson:
Approximation algorithms for deadline-TSP and vehicle routing with time-windows. STOC 2004: 166-174 - 2003
- [j32]Avrim Blum, Shuchi Chawla, Adam Kalai:
Static Optimality and Dynamic Search-Optimality in Lists and Trees. Algorithmica 36(3): 249-260 (2003) - [j31]Avrim Blum, Adam Tauman Kalai, Jon M. Kleinberg:
Admission Control to Minimize Rejections. Internet Math. 1(2): 165-176 (2003) - [j30]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM 50(4): 506-519 (2003) - [j29]John Langford, Avrim Blum:
Microchoice Bounds and Self Bounding Learning Algorithms. Mach. Learn. 51(2): 165-179 (2003) - [c63]Ke Yang, Avrim Blum:
On Statistical Query Sampling and NMR Quantum Computing. CCC 2003: 194- - [c62]Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, Martin Zinkevich:
Preference Elicitation and Query Learning. COLT 2003: 13-25 - [c61]Avrim Blum, John Langford:
PAC-MDL Bounds. COLT 2003: 344-357 - [c60]Avrim Blum:
Learning a Function of r Relevant Variables. COLT 2003: 731-733 - [c59]Nikhil Bansal, Avrim Blum, Shuchi Chawla, Kedar Dhamdhere:
Scheduling for Flow-Time with Admission Control. ESA 2003: 43-54 - [c58]Avrim Blum:
Machine Learning: My Favorite Results, Directions, and Open Problems. FOCS 2003: 2- - [c57]Avrim Blum, Shuchi Chawla, David R. Karger, Terran Lane, Adam Meyerson, Maria Minkoff:
Approximation Algorithms for Orienteering and Discounted-Reward TSP. FOCS 2003: 46-55 - [c56]H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum:
Planning in the Presence of Cost Functions Controlled by an Adversary. ICML 2003: 536-543 - [c55]Martin Zinkevich, Avrim Blum, Tuomas Sandholm:
On polynomial-time preference elicitation with value queries. EC 2003: 176-185 - [c54]Avrim Blum, Vijay Kumar, Atri Rudra, Felix Wu:
Online learning in online auctions. SODA 2003: 202-204 - [c53]Nikhil Bansal, Avrim Blum, Shuchi Chawla, Adam Meyerson:
Online oblivious routing. SPAA 2003: 44-49 - [c52]Yossi Azar, Avrim Blum, Yishay Mansour:
Combining online algorithms for rejection and acceptance. SPAA 2003: 159-163 - [i4]Avrim Blum, Ke Yang:
On Statistical Query Sampling and NMR Quantum Computing. Electron. Colloquium Comput. Complex. TR03 (2003) - 2002
- [c51]Nikhil Bansal, Avrim Blum, Shuchi Chawla:
Correlation Clustering. FOCS 2002: 238- - [c50]Avrim Blum, Shuchi Chawla, Adam Kalai:
Static optimality and dynamic search-optimality in lists and trees. SODA 2002: 1-8 - [c49]Avrim Blum, John Dunagan:
Smoothed analysis of the perceptron algorithm for linear programming. SODA 2002: 905-914 - [c48]Avrim Blum, Tuomas Sandholm, Martin Zinkevich:
Online algorithms for market clearing. SODA 2002: 971-980 - 2001
- [c47]Avrim Blum, Shuchi Chawla:
Learning from Labeled and Unlabeled Data using Graph Mincuts. ICML 2001: 19-26 - [c46]Avrim Blum, Adam Kalai, Jon M. Kleinberg:
Admission Control to Minimize Rejections. WADS 2001: 155-164 - 2000
- [j28]Avrim Blum, Carl Burch:
On-line Learning and the Metrical Task System Problem. Mach. Learn. 39(1): 35-58 (2000) - [j27]Avrim Blum, Prasad Chalasani:
An Online Algorithm for Improving Performance in Navigation. SIAM J. Comput. 29(6): 1907-1938 (2000) - [j26]Avrim Blum, Howard J. Karloff, Yuval Rabani, Michael E. Saks:
A Decomposition Theorem for Task Systems and Bounds for Randomized Server Problems. SIAM J. Comput. 30(5): 1624-1661 (2000) - [j25]Avrim Blum, Goran Konjevod, R. Ravi, Santosh S. Vempala:
Semi-definite relaxations for minimum bandwidth and other vertex-ordering problems. Theor. Comput. Sci. 235(1): 25-42 (2000) - [c45]Joseph O'Sullivan, John Langford, Rich Caruana, Avrim Blum:
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness. ICML 2000: 703-710 - [c44]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-tolerant learning, the parity problem, and the statistical query model. STOC 2000: 435-440 - [i3]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model. CoRR cs.LG/0010022 (2000)
1990 – 1999
- 1999
- [j24]Avrim Blum, R. Ravi, Santosh S. Vempala:
A Constant-Factor Approximation Algorithm for the k-MST Problem. J. Comput. Syst. Sci. 58(1): 101-108 (1999) - [j23]Avrim Blum, Adam Kalai:
Universal Portfolios With and Without Transaction Costs. Mach. Learn. 35(3): 193-205 (1999) - [c43]Avrim Blum, Adam Kalai, John Langford:
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999: 203-208 - [c42]John Langford, Avrim Blum:
Microchoice Bounds and Self Bounding Learning Algorithms. COLT 1999: 209-214 - [c41]Avrim Blum, John Langford:
Probabilistic Planning in the Graphplan Framework. ECP 1999: 319-332 - [c40]Avrim Blum, Carl Burch, Adam Kalai:
Finely-Competitive Paging. FOCS 1999: 450-458 - [c39]Adam Kalai, Stanley F. Chen, Avrim Blum, Ronald Rosenfeld:
On-line algorithms for combining language models. ICASSP 1999: 745-748 - 1998
- [j22]Avrim Blum, Alan M. Frieze, Ravi Kannan, Santosh S. Vempala:
A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions. Algorithmica 22(1/2): 35-52 (1998) - [j21]Avrim Blum, Prasad Chalasani, Sally A. Goldman, Donna K. Slonim:
Learning with Unreliable Boundary Queries. J. Comput. Syst. Sci. 56(2): 209-222 (1998) - [j20]Avrim Blum, Adam Kalai:
A Note on Learning from Multiple-Instance Examples. Mach. Learn. 30(1): 23-29 (1998) - [j19]Howard Aizenstein, Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, Dan Roth:
On Learning Read-k-Satisfy-j DNF. SIAM J. Comput. 27(6): 1515-1530 (1998) - [j18]Baruch Awerbuch, Yossi Azar, Avrim Blum, Santosh S. Vempala:
New Approximation Guarantees for Minimum-Weight k-Trees and Prize-Collecting Salesmen. SIAM J. Comput. 28(1): 254-262 (1998) - [j17]Joseph S. B. Mitchell, Avrim Blum, Prasad Chalasani, Santosh S. Vempala:
A Constant-Factor Approximation Algorithm for the Geometric k-MST Problem in the Plane. SIAM J. Comput. 28(3): 771-781 (1998) - [j16]Avrim Blum, Prabhakar Raghavan:
On a theory of computing symposia. SIGACT News 29(3): 104-111 (1998) - [c38]Avrim Blum, Tom M. Mitchell:
Combining Labeled and Unlabeled Data with Co-Training. COLT 1998: 92-100 - [c37]Avrim Blum, Carl Burch, John Langford:
On Learning Monotone Boolean Functions. FOCS 1998: 408-415 - [c36]Avrim Blum, Goran Konjevod, R. Ravi, Santosh S. Vempala:
Semi-Definite Relaxations for Minimum Bandwidth and other Vertex-Ordering Problems. STOC 1998: 100-105 - 1997
- [j15]Avrim Blum, Merrick L. Furst:
Fast Planning Through Planning Graph Analysis. Artif. Intell. 90(1-2): 281-300 (1997) - [j14]Avrim Blum, Pat Langley:
Selection of Relevant Features and Examples in Machine Learning. Artif. Intell. 97(1-2): 245-271 (1997) - [j13]Avrim Blum, David R. Karger:
An Õ(n^{3/14})-Coloring Algorithm for 3-Colorable Graphs. Inf. Process. Lett. 61(1): 49-53 (1997) - [j12]Avrim Blum, Ravindran Kannan:
Learning an Intersection of a Constant Number of Halfspaces over a Uniform Distribution. J. Comput. Syst. Sci. 54(2): 371-380 (1997) - [j11]Avrim Blum:
Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain. Mach. Learn. 26(1): 5-23 (1997) - [j10]Avrim Blum, Prabhakar Raghavan, Baruch Schieber:
Navigating in Unfamiliar Geometric Terrain. SIAM J. Comput. 26(1): 110-137 (1997) - [c35]Avrim Blum, Carl Burch:
On-line Learning and the Metrical Task System Problem. COLT 1997: 45-53 - [c34]Avrim Blum, Adam Kalai:
Universal Portfolios With and Without Transaction Costs. COLT 1997: 309-313 - [c33]Yair Bartal, Avrim Blum, Carl Burch, Andrew Tomkins:
A polylog(n)-Competitive Algorithm for Metrical Task Systems. STOC 1997: 711-719 - 1996
- [c32]Avrim Blum:
On-line Algorithms in Machine Learning. Online Algorithms 1996: 306-325 - [c31]Avrim Blum, Alan M. Frieze, Ravi Kannan, Santosh S. Vempala:
A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions. FOCS 1996: 330-338 - [c30]Piotr Berman, Avrim Blum, Amos Fiat, Howard J. Karloff, Adi Rosén, Michael E. Saks:
Randomized Robot Navigation Algorithms. SODA 1996: 75-84 - [c29]Avrim Blum, R. Ravi, Santosh S. Vempala:
A Constant-factor Approximation Algorithm for the k MST Problem (Extended Abstract). STOC 1996: 442-448 - [e1]Avrim Blum, Michael J. Kearns:
Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano del Garda, Italy, June 28-July 1, 1996. ACM 1996, ISBN 0-89791-811-8 [contents] - 1995
- [j9]Avrim Blum, Joel Spencer:
Coloring Random and Semi-Random k-Colorable Graphs. J. Algorithms 19(2): 204-234 (1995) - [j8]Avrim Blum, Lisa Hellerstein, Nick Littlestone:
Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. J. Comput. Syst. Sci. 50(1): 32-40 (1995) - [j7]Avrim Blum, Steven Rudich:
Fast Learning of k-Term DNF Formulas with Queries. J. Comput. Syst. Sci. 51(3): 367-373 (1995) - [c28]Avrim Blum, Prasad Chalasani, Sally A. Goldman, Donna K. Slonim:
Learning with Unreliable Boundary Queries. COLT 1995: 98-107 - [c27]Avrim Blum:
Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain. ICML 1995: 64-72 - [c26]Avrim Blum, Merrick L. Furst:
Fast Planning Through Planning Graph Analysis. IJCAI 1995: 1636-1642 - [c25]Baruch Awerbuch, Yossi Azar, Avrim Blum, Santosh S. Vempala:
Improved approximation guarantees for minimum-weight k-trees and prize-collecting salesmen. STOC 1995: 277-283 - [c24]Avrim Blum, Prasad Chalasani, Santosh S. Vempala:
A constant-factor approximation for the k-MST problem in the plane. STOC 1995: 294-302 - 1994
- [j6]Avrim Blum:
New Approximation Algorithms for Graph Coloring. J. ACM 41(3): 470-516 (1994) - [j5]Avrim Blum, Tao Jiang, Ming Li, John Tromp, Mihalis Yannakakis:
Linear Approximation of Shortest Superstrings. J. ACM 41(4): 630-647 (1994) - [j4]Avrim Blum:
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain. SIAM J. Comput. 23(5): 990-1000 (1994) - [c23]Avrim Blum, Roni Khardon, Eyal Kushilevitz, Leonard Pitt, Dan Roth:
On Learning Read-k-Satisfy-j DNF. COLT 1994: 110-117 - [c22]Avrim Blum, Prasad Chalasani, Don Coppersmith, William R. Pulleyblank, Prabhakar Raghavan, Madhu Sudan:
The minimum latency problem. STOC 1994: 163-171 - [c21]Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, Steven Rudich:
Weakly learning DNF and characterizing statistical query learning using Fourier analysis. STOC 1994: 253-262 - [i2]Avrim Blum, Prasad Chalasani, Don Coppersmith, William R. Pulleyblank, Prabhakar Raghavan, Madhu Sudan:
On the minimum latency problem. CoRR abs/math/9409223 (1994) - [i1]Avrim Blum, Prasad Chalasani:
An on-line algorithm for improving performance in navigation. CoRR abs/math/9409224 (1994) - 1993
- [c20]Avrim Blum, Prasad Chalasani, Jeffrey C. Jackson:
On Learning Embedded Symmetric Concepts. COLT 1993: 337-346 - [c19]Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton:
Cryptographic Primitives Based on Hard Learning Problems. CRYPTO 1993: 278-291 - [c18]Avrim Blum, Prasad Chalasani:
An On-Line Algorithm for Improving Performance in Navigation. FOCS 1993: 2-11 - [c17]Avrim Blum, Ravi Kannan:
Learning an Intersection of k Halfspaces over a Uniform Distribution. FOCS 1993: 312-320 - [c16]Avrim Blum, Ronald L. Rivest:
Training a 3-Node Neural Network is NP-Complete. Machine Learning: From Theory to Applications 1993: 9-28 - 1992
- [j3]Avrim Blum:
Rank-r Decision Trees are a Subclass of r-Decision Lists. Inf. Process. Lett. 42(4): 183-185 (1992) - [j2]Avrim Blum:
Learning Boolean Functions in an Infinite Attribute Space. Mach. Learn. 9: 373-386 (1992) - [j1]Avrim Blum, Ronald L. Rivest:
Training a 3-node neural network is NP-complete. Neural Networks 5(1): 117-127 (1992) - [c15]Avrim Blum, Prasad Chalasani:
Learning Switching Concepts. COLT 1992: 231-242 - [c14]Avrim Blum, Howard J. Karloff, Yuval Rabani, Michael E. Saks:
A Decomposition Theorem and Bounds for Randomized Server Problems. FOCS 1992: 197-207 - [c13]Avrim Blum, Steven Rudich:
Fast Learning of k-Term DNF Formulas with Queries. STOC 1992: 382-389 - 1991
- [c12]Avrim Blum, Lisa Hellerstein, Nick Littlestone:
Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. COLT 1991: 157-166 - [c11]Avrim Blum, Prabhakar Raghavan, Baruch Schieber:
Navigating in Unfamiliar Geometric Terrain (Extended Summary). On-Line Algorithms 1991: 151-156 - [c10]Avrim Blum, Tao Jiang, Ming Li, John Tromp, Mihalis Yannakakis:
Linear Approximation of Shortest Superstrings. STOC 1991: 328-336 - [c9]Avrim Blum, Prabhakar Raghavan, Baruch Schieber:
Navigating in Unfamiliar Geometric Terrain (Preliminary Version). STOC 1991: 494-504 - 1990
- [c8]Avrim Blum, Mona Singh:
Learning Functions of k Terms. COLT 1990: 144-153 - [c7]Avrim Blum:
Separating PAC and Mistake-Bound Learning Models Over the Boolean Domain (Abstract). COLT 1990: 393 - [c6]Avrim Blum:
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain. FOCS 1990: 211-218 - [c5]Avrim Blum:
Some Tools for Approximate 3-Coloring (Extended Abstract). FOCS 1990: 554-562 - [c4]Avrim Blum:
Learning Boolean Functions in an Infinite Atribute Space (Extended Abstract). STOC 1990: 64-72
1980 – 1989
- 1989
- [c3]Avrim Blum:
An \tildeO(n^0.4)-Approximation Algorithm for 3-Coloring (and Improved Approximation Algorithm for k-Coloring). STOC 1989: 535-542 - 1988
- [c2]Avrim Blum, Ronald L. Rivest:
Training a 3-Node Neural Network is NP-Complete. COLT 1988: 9-18 - [c1]Avrim Blum, Ronald L. Rivest:
Training a 3-Node Neural Network is NP-Complete. NIPS 1988: 494-501
Coauthor Index
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