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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
- 2023
[i56]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)- [i55]Saba Ahmadi, Avrim Blum, Kunhe Yang:
Fundamental Bounds on Online Strategic Classification. CoRR abs/2302.12355 (2023) - [i54]Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl:
Certifiable (Multi)Robustness Against Patch Attacks Using ERM. CoRR abs/2303.08944 (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]