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ORCIDEdith Cohen
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- affiliation: Google Research, Mountain View, CA, USA
- affiliation: Tel Aviv University, Israel
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2020 – today
- 2024
[j55]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators. Algorithmica 86(11): 3339-3394 (2024)- [c99]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. COLT 2024: 1200-1222 - [c98]Sara Ahmadian, Edith Cohen:
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs. ICML 2024 - [i51]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. CoRR abs/2403.00028 (2024) - [i50]Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia:
Data Reconstruction: When You See It and When You Don't. CoRR abs/2405.15753 (2024) - [i49]Sara Ahmadian, Edith Cohen:
Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs. CoRR abs/2405.17780 (2024) - [i48]Edith Cohen, Jelani Nelson, Tamás Sarlós, Mihir Singhal, Uri Stemmer:
One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches. CoRR abs/2411.06370 (2024) - [i47]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. IACR Cryptol. ePrint Arch. 2024: 373 (2024) - 2023
- [c97]Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. AAAI 2023: 7235-7243 - [c96]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. ITCS 2023: 8:1-8:19 - [c95]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Generalized Private Selection and Testing with High Confidence. ITCS 2023: 39:1-39:23 - [c94]Edith Cohen, Xin Lyu:
The Target-Charging Technique for Privacy Analysis across Interactive Computations. NeurIPS 2023 - [c93]Edith Cohen
:
Sampling Big Ideas in Query Optimization. PODS 2023: 361-371 - [c92]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. STOC 2023: 472-482 - [i46]Edith Cohen, Xin Lyu:
The Target-Charging Technique for Privacy Accounting across Interactive Computations. CoRR abs/2302.11044 (2023) - [i45]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Hot PATE: Private Aggregation of Distributions for Diverse Task. CoRR abs/2312.02132 (2023) - 2022
- [j54]Niklas Carlsson, Edith Cohen, Philippe Robert:
POMACS V6, N1, March 2022 Editorial. Proc. ACM Meas. Anal. Comput. Syst. 6(1): 1:1 (2022) - [j53]Niklas Carlsson, Edith Cohen, Philippe Robert:
POMACS V6, N2, June 2022 Editorial. Proc. ACM Meas. Anal. Comput. Syst. 6(2): 24:1 (2022) - [c91]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer:
On the Robustness of CountSketch to Adaptive Inputs. ICML 2022: 4112-4140 - [c90]Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer:
FriendlyCore: Practical Differentially Private Aggregation. ICML 2022: 21828-21863 - [e2]D. Manjunath, Jayakrishnan Nair, Niklas Carlsson, Edith Cohen, Philippe Robert:
SIGMETRICS/PERFORMANCE '22: ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, Mumbai, India, June 6 - 10, 2022. ACM 2022, ISBN 978-1-4503-9141-2 [contents] - [i44]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer:
On the Robustness of CountSketch to Adaptive Inputs. CoRR abs/2202.13736 (2022) - [i43]Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. CoRR abs/2207.00956 (2022) - [i42]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. CoRR abs/2211.06387 (2022) - [i41]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Generalized Private Selection and Testing with High Confidence. CoRR abs/2211.12063 (2022) - 2021
- [j52]Niklas Carlsson, Edith Cohen, Philippe Robert:
POMACS V5, N3, December 2021 Editorial. Proc. ACM Meas. Anal. Comput. Syst. 5(3): 29:1 (2021) - [j51]Edith Cohen:
Editorial. ACM Trans. Algorithms 17(3): 19e:1 (2021) - [c89]Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer:
Differentially Private Weighted Sampling. AISTATS 2021: 2404-2412 - [c88]Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Differentially-Private Clustering of Easy Instances. ICML 2021: 2049-2059 - [i40]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR abs/2107.14527 (2021) - [i39]Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer:
FriendlyCore: Practical Differentially Private Aggregation. CoRR abs/2110.10132 (2021) - [i38]Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Differentially-Private Clustering of Easy Instances. CoRR abs/2112.14445 (2021) - 2020
- [c87]Edith Cohen, Ofir Geri, Rasmus Pagh:
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. ICML 2020: 2057-2067 - [c86]Gal Sadeh, Edith Cohen, Haim Kaplan:
Sample Complexity Bounds for Influence Maximization. ITCS 2020: 29:1-29:36 - [c85]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for ℓp-Sampling Without Replacement. NeurIPS 2020 - [c84]Eliav Buchnik, Edith Cohen:
Graph Learning with Loss-Guided Training. GRADES-NDA@SIGMOD 2020: 7:1-7:13 - [i37]Edith Cohen, Ofir Geri, Rasmus Pagh:
Composable Sketches for Functions of Frequencies: Beyond the Worst Case. CoRR abs/2004.04772 (2020) - [i36]Eliav Buchnik, Edith Cohen:
Graph Learning with Loss-Guided Training. CoRR abs/2006.00460 (2020) - [i35]Edith Cohen, Rasmus Pagh, David P. Woodruff:
WOR and p's: Sketches for 𝓁p-Sampling Without Replacement. CoRR abs/2007.06744 (2020) - [i34]Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer:
Differentially Private Weighted Sampling. CoRR abs/2010.13048 (2020)
2010 – 2019
- 2019
- [c83]Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias:
Self-similar Epochs: Value in arrangement. ICML 2019: 841-850 - [c82]Edith Cohen, Ofir Geri:
Sampling Sketches for Concave Sublinear Functions of Frequencies. NeurIPS 2019: 1361-1371 - [e1]Moses Charikar, Edith Cohen:
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019, Phoenix, AZ, USA, June 23-26, 2019. ACM 2019, ISBN 978-1-4503-6705-9 [contents] - [i33]Edith Cohen, Ofir Geri:
Sampling Sketches for Concave Sublinear Functions of Frequencies. CoRR abs/1907.02218 (2019) - [i32]Gal Sadeh, Edith Cohen, Haim Kaplan:
Influence Maximization with Few Simulations. CoRR abs/1907.13301 (2019) - 2018
- [j50]Eliav Buchnik, Edith Cohen:
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity. Proc. ACM Meas. Anal. Comput. Syst. 2(1): 10:1-10:19 (2018) - [j49]Edith Cohen:
Stream Sampling Framework and Application for Frequency Cap Statistics. ACM Trans. Algorithms 14(4): 52:1-52:40 (2018) - [c81]Edith Cohen, Shiri Chechik, Haim Kaplan:
Clustering Small Samples With Quality Guarantees: Adaptivity With One2all PPS. AAAI 2018: 2884-2891 - [c80]Eliav Buchnik, Edith Cohen:
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity. SIGMETRICS (Abstracts) 2018: 8-10 - [r5]Edith Cohen:
Decay Models. Encyclopedia of Database Systems (2nd ed.) 2018 - [i31]Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias:
LSH Microbatches for Stochastic Gradients: Value in Rearrangement. CoRR abs/1803.05389 (2018) - 2017
- [c79]Shir Landau Feibish, Yehuda Afek, Anat Bremler-Barr, Edith Cohen, Michal Shagam:
Mitigating DNS random subdomain DDoS attacks by distinct heavy hitters sketches. HotWeb 2017: 8:1-8:6 - [c78]Edith Cohen:
HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics. KDD 2017: 105-114 - [i30]Eliav Buchnik, Edith Cohen:
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity. CoRR abs/1703.02618 (2017) - [i29]Edith Cohen, Shiri Chechik, Haim Kaplan:
Clustering over Multi-Objective Samples: The one2all Sample. CoRR abs/1706.03607 (2017) - 2016
- [j48]Edith Cohen, Graham Cormode, Nick G. Duffield, Carsten Lund:
On the Tradeoff between Stability and Fit. ACM Trans. Algorithms 13(1): 7:1-7:24 (2016) - [c77]Edith Cohen:
Greedy Maximization Framework for Graph-Based Influence Functions. HotWeb 2016: 29-35 - [c76]Eliav Buchnik, Edith Cohen:
Reverse Ranking by Graph Structure: Model and Scalable Algorithms. SIGMETRICS 2016: 51-62 - [r4]Edith Cohen:
All-Distances Sketches. Encyclopedia of Algorithms 2016: 59-64 - [r3]Edith Cohen:
Coordinated Sampling. Encyclopedia of Algorithms 2016: 449-454 - [r2]Edith Cohen:
Min-Hash Sketches. Encyclopedia of Algorithms 2016: 1282-1287 - [i28]Edith Cohen:
Semi-Supervised Learning for Asymmetric Graphs through Reach and Distance Diffusion. CoRR abs/1603.09064 (2016) - [i27]Edith Cohen:
Estimating Frequency Statistics through Distinct Count Measurements. CoRR abs/1607.06517 (2016) - [i26]Edith Cohen:
Greedy Maximization Framework for Graph-based Influence Functions. CoRR abs/1608.04036 (2016) - [i25]Yehuda Afek, Anat Bremler-Barr, Edith Cohen, Shir Landau Feibish, Michal Shagam:
Efficient Distinct Heavy Hitters for DNS DDoS Attack Detection. CoRR abs/1612.02636 (2016) - 2015
- [j47]Edith Cohen:
All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis. IEEE Trans. Knowl. Data Eng. 27(9): 2320-2334 (2015) - [c75]Shiri Chechik, Edith Cohen, Haim Kaplan:
Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees. APPROX-RANDOM 2015: 659-679 - [c74]Edith Cohen:
Multi-objective Weighted Sampling. HotWeb 2015: 13-18 - [c73]Edith Cohen:
Stream Sampling for Frequency Cap Statistics. KDD 2015: 159-168 - [i24]Edith Cohen:
Stream Sampling for Frequency Cap Statistics. CoRR abs/1502.05955 (2015) - [i23]Shiri Chechik, Edith Cohen, Haim Kaplan:
Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees. CoRR abs/1503.08528 (2015) - [i22]Eliav Buchnik, Edith Cohen:
Reverse Ranking by Graph Structure: Model and Scalable Algorithms. CoRR abs/1506.02386 (2015) - [i21]Edith Cohen:
Multi-Objective Weighted Sampling. CoRR abs/1509.07445 (2015) - 2014
- [j46]Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, Mikkel Thorup:
Algorithms and estimators for summarization of unaggregated data streams. J. Comput. Syst. Sci. 80(7): 1214-1244 (2014) - [j45]Edith Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Danny Raz, Yoav Tzur:
Probe scheduling for efficient detection of silent failures. Perform. Evaluation 79: 73-89 (2014) - [c72]Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck:
Sketch-based Influence Maximization and Computation: Scaling up with Guarantees. CIKM 2014: 629-638 - [c71]Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck:
Computing classic closeness centrality, at scale. COSN 2014: 37-50 - [c70]Qin Lv, Pei Cao, Edith Cohen, Kai Li, Scott Shenker:
Author retrospective for search and replication in unstructured peer-to-peer networks. ICS 25th Anniversary 2014: 64-82 - [c69]Edith Cohen:
Distance queries from sampled data: accurate and efficient. KDD 2014: 681-690 - [c68]Edith Cohen:
Estimation for monotone sampling: competitiveness and customization. PODC 2014: 124-133 - [c67]Edith Cohen:
All-distances sketches, revisited: HIP estimators for massive graphs analysis. PODS 2014: 88-99 - [i20]Edith Cohen:
Variance Competitiveness for Monotone Estimation: Tightening the Bounds. CoRR abs/1406.6490 (2014) - [i19]Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck:
Sketch-based Influence Maximization and Computation: Scaling up with Guarantees. CoRR abs/1408.6282 (2014) - [i18]Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck:
Computing Classic Closeness Centrality, at Scale. CoRR abs/1409.0035 (2014) - [i17]Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck:
Timed Influence: Computation and Maximization. CoRR abs/1410.6976 (2014) - 2013
- [c66]Edith Cohen, Haim Kaplan, Yishay Mansour:
Scheduling Subset Tests: One-Time, Continuous, and How They Relate. APPROX-RANDOM 2013: 81-95 - [c65]Edith Cohen, Haim Kaplan:
What You Can Do with Coordinated Samples. APPROX-RANDOM 2013: 452-467 - [c64]Edith Cohen, Daniel Delling, Fabian Fuchs, Andrew V. Goldberg, Moisés Goldszmidt, Renato F. Werneck:
Scalable similarity estimation in social networks: closeness, node labels, and random edge lengths. COSN 2013: 131-142 - [i16]Edith Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Danny Raz, Yoav Tzur:
Probe Scheduling for Efficient Detection of Silent Failures. CoRR abs/1302.0792 (2013) - [i15]Edith Cohen, Graham Cormode, Nick G. Duffield, Carsten Lund:
On the Tradeoff between Stability and Fit. CoRR abs/1302.2137 (2013) - [i14]Edith Cohen:
Scalable Neighborhood Sketching and Distance Distribution Estimation in Graph Datasets: Revisited, Unified, and Improved. CoRR abs/1306.3284 (2013) - [i13]Edith Cohen, Amos Fiat, Haim Kaplan, Liam Roditty:
A Labeling Approach to Incremental Cycle Detection. CoRR abs/1310.8381 (2013) - 2012
- [j44]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
Envy-Free Makespan Approximation. SIAM J. Comput. 41(1): 12-25 (2012) - [c63]Edith Cohen, Graham Cormode, Nick G. Duffield:
Don't let the negatives bring you down: sampling from streams of signed updates. SIGMETRICS 2012: 343-354 - [i12]Edith Cohen, Haim Kaplan:
How to Estimate Change from Samples. CoRR abs/1203.4903 (2012) - [i11]Edith Cohen, Haim Kaplan:
What you can do with Coordinated Samples. CoRR abs/1206.5637 (2012) - [i10]Edith Cohen, Haim Kaplan:
A Case for Customizing Estimators: Coordinated Samples. CoRR abs/1212.0243 (2012) - 2011
- [j43]Edith Cohen, Graham Cormode, Nick G. Duffield:
Structure-Aware Sampling: Flexible and Accurate Summarization. Proc. VLDB Endow. 4(11): 819-830 (2011) - [j42]Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, Mikkel Thorup:
Efficient Stream Sampling for Variance-Optimal Estimation of Subset Sums. SIAM J. Comput. 40(5): 1402-1431 (2011) - [c62]Edith Cohen, Haim Kaplan:
Get the most out of your sample: optimal unbiased estimators using partial information. PODS 2011: 13-24 - [c61]Edith Cohen, Graham Cormode, Nick G. Duffield:
Structure-aware sampling on data streams. SIGMETRICS 2011: 197-208 - [c60]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
Truth, Envy, and Truthful Market Clearing Bundle Pricing. WINE 2011: 97-108 - [i9]Edith Cohen, Graham Cormode, Nick G. Duffield:
Structure-Aware Sampling: Flexible and Accurate Summarization. CoRR abs/1102.5146 (2011) - [i8]Edith Cohen, Haim Kaplan:
Get the Most out of Your Sample: Optimal Unbiased Estimators using Partial Information. CoRR abs/1109.1325 (2011) - 2010
- [j41]Edith Cohen, Haim Kaplan, Tova Milo:
Labeling Dynamic XML Trees. SIAM J. Comput. 39(5): 2048-2074 (2010) - [c59]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
Envy-free makespan approximation: extended abstract. EC 2010: 159-166 - [i7]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
Truth and Envy in Capacitated Allocation Games. CoRR abs/1003.5326 (2010) - [i6]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
On the Interplay between Incentive Compatibility and Envy Freeness. CoRR abs/1003.5328 (2010)
2000 – 2009
- 2009
- [j40]Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, Mikkel Thorup:
Composable, Scalable, and Accurate Weight Summarization of Unaggregated Data Sets. Proc. VLDB Endow. 2(1): 431-442 (2009) - [j39]Edith Cohen, Haim Kaplan, Subhabrata Sen:
Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments. Proc. VLDB Endow. 2(1): 646-657 (2009) - [c58]Edith Cohen, Haim Kaplan:
Leveraging discarded samples for tighter estimation of multiple-set aggregates. SIGMETRICS/Performance 2009: 251-262 - [c57]Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, Mikkel Thorup:
Stream sampling for variance-optimal estimation of subset sums. SODA 2009: 1255-1264 - [r1]Edith Cohen:
Decay Models. Encyclopedia of Database Systems 2009: 757-761 - [i5]Edith Cohen, Haim Kaplan:
Leveraging Discarded Samples for Tighter Estimation of Multiple-Set Aggregates. CoRR abs/0903.0625 (2009) - [i4]Edith Cohen, Haim Kaplan, Subhabrata Sen:
Coordinated Weighted Sampling for Estimating Aggregates Over Multiple Weight Assignments. CoRR abs/0906.4560 (2009) - [i3]Edith Cohen, Michal Feldman, Amos Fiat, Haim Kaplan, Svetlana Olonetsky:
Envy-Free Makespan Approximation. CoRR abs/0909.1072 (2009) - 2008
- [j38]Edith Cohen, Nadav Grossaug, Haim Kaplan:
Processing top-k queries from samples. Comput. Networks 52(14): 2605-2622 (2008) - [j37]Edith Cohen, Haim Kaplan:
Tighter estimation using bottom k sketches. Proc. VLDB Endow. 1(1): 213-224 (2008) - [c56]Edith Cohen, Haim Kaplan:
Estimating Aggregates over Multiple Sets. ICDM 2008: 761-766 - [c55]Edith Cohen, Nick G. Duffield, Carsten Lund, Mikkel Thorup:
Confident estimation for multistage measurement sampling and aggregation. SIGMETRICS 2008: 109-120 - [i2]Edith Cohen, Haim Kaplan:
Sketch-Based Estimation of Subpopulation-Weight. CoRR abs/0802.3448 (2008) - [i1]Edith Cohen, Nick G. Duffield, Haim Kaplan, Carsten Lund, Mikkel Thorup:
Variance optimal sampling based estimation of subset sums. CoRR abs/0803.0473 (2008) - 2007
- [j36]Edith Cohen, Amos Fiat, Haim Kaplan:
Associative search in peer to peer networks: Harnessing latent semantics. Comput. Networks