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Alex Kulesza
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2020 – today
- 2024
- [c38]Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang:
Mean Estimation in the Add-Remove Model of Differential Privacy. ICML 2024 - [i16]Kareem Amin, Alex Kulesza, Sergei Vassilvitskii:
Practical Considerations for Differential Privacy. CoRR abs/2408.07614 (2024) - 2023
- [c37]Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. ICML 2023: 7992-8014 - [i15]Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh:
Subset-Based Instance Optimality in Private Estimation. CoRR abs/2303.01262 (2023) - [i14]Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang:
Mean estimation in the add-remove model of differential privacy. CoRR abs/2312.06658 (2023) - 2022
- [i13]Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii:
Plume: Differential Privacy at Scale. CoRR abs/2201.11603 (2022) - 2021
- [c36]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. ICML 2021: 3713-3722 - [c35]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. NeurIPS 2021: 12466-12479 - [i12]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. CoRR abs/2102.08244 (2021) - [i11]Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh:
Learning with User-Level Privacy. CoRR abs/2102.11845 (2021) - [i10]Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza:
Combining Public and Private Data. CoRR abs/2111.00115 (2021)
2010 – 2019
- 2019
- [c34]Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML 2019: 263-271 - [c33]Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii:
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. ICML 2019: 2260-2268 - [c32]Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Differentially Private Covariance Estimation. NeurIPS 2019: 14190-14199 - 2018
- [c31]Nan Jiang, Alex Kulesza, Satinder Singh:
Completing State Representations using Spectral Learning. NeurIPS 2018: 4333-4342 - [c30]Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet:
Maximizing Induced Cardinality Under a Determinantal Point Process. NeurIPS 2018: 6911-6920 - 2016
- [c29]Nan Jiang, Alex Kulesza, Satinder Singh:
Improving Predictive State Representations via Gradient Descent. AAAI 2016: 1709-1715 - [c28]Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis:
The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI 2016: 4180-4189 - 2015
- [c27]Alex Kulesza, Nan Jiang, Satinder Singh:
Spectral Learning of Predictive State Representations with Insufficient Statistics. AAAI 2015: 2715-2721 - [c26]Alex Kulesza, Nan Jiang, Satinder Singh:
Low-Rank Spectral Learning with Weighted Loss Functions. AISTATS 2015 - [c25]Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis:
The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS 2015: 1181-1189 - [c24]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Information extraction from large multi-layer social networks. ICASSP 2015: 5451-5455 - [c23]Nan Jiang, Alex Kulesza, Satinder Singh:
Abstraction Selection in Model-based Reinforcement Learning. ICML 2015: 179-188 - [c22]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Socio-Spatial Pareto Frontiers of Twitter Networks. SBP 2015: 388-393 - [i9]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Socio-Spatial Pareto Frontiers of Twitter Networks. CoRR abs/1506.08916 (2015) - [i8]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Information Extraction from Larger Multi-layer Social Networks. CoRR abs/1507.00087 (2015) - 2014
- [j6]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Multi-Layer Graph Analysis for Dynamic Social Networks. IEEE J. Sel. Top. Signal Process. 8(4): 514-523 (2014) - [j5]Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III:
Social Collaborative Retrieval. IEEE J. Sel. Top. Signal Process. 8(4): 680-689 (2014) - [c21]Alex Kulesza, N. Raj Rao, Satinder Singh:
Low-Rank Spectral Learning. AISTATS 2014: 522-530 - [c20]Kai Hong, John M. Conroy, Benoît Favre, Alex Kulesza, Hui Lin, Ani Nenkova:
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization. LREC 2014: 1608-1616 - [c19]Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar:
Expectation-Maximization for Learning Determinantal Point Processes. NIPS 2014: 3149-3157 - [c18]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Multi-objective Optimization for Multi-level Networks. SBP 2014: 129-136 - [c17]Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III:
Social collaborative retrieval. WSDM 2014: 293-302 - [i7]Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III:
Social Collaborative Retrieval. CoRR abs/1404.2342 (2014) - [i6]Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar:
Expectation-Maximization for Learning Determinantal Point Processes. CoRR abs/1411.1088 (2014) - [i5]Nematollah Kayhan Batmanghelich, Gerald T. Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn:
Diversifying Sparsity Using Variational Determinantal Point Processes. CoRR abs/1411.6307 (2014) - 2013
- [j4]Koby Crammer, Alex Kulesza, Mark Dredze
:
Adaptive regularization of weight vectors. Mach. Learn. 91(2): 155-187 (2013) - [c16]Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox, Ben Taskar:
Nystrom Approximation for Large-Scale Determinantal Processes. AISTATS 2013: 85-98 - [c15]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Multi-layer graph analytics for social networks. CAMSAP 2013: 284-287 - [i4]Brandon Oselio, Alex Kulesza, Alfred O. Hero III:
Multi-layer graph analytics for dynamic social networks. CoRR abs/1309.5124 (2013) - 2012
- [j3]Alex Kulesza, Ben Taskar:
Determinantal Point Processes for Machine Learning. Found. Trends Mach. Learn. 5(2-3): 123-286 (2012) - [c14]Jennifer Gillenwater, Alex Kulesza, Ben Taskar:
Discovering Diverse and Salient Threads in Document Collections. EMNLP-CoNLL 2012: 710-720 - [c13]Koby Crammer, Alex Kulesza, Mark Dredze
New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure. ICASSP 2012: 2017-2020 - [c12]Jennifer Gillenwater, Alex Kulesza, Ben Taskar:
Near-Optimal MAP Inference for Determinantal Point Processes. NIPS 2012: 2744-2752 - [c11]Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox:
Markov Determinantal Point Processes. UAI 2012: 26-35 - [i3]Alex Kulesza, Ben Taskar:
Learning Determinantal Point Processes. CoRR abs/1202.3738 (2012) - [i2]Alex Kulesza, Ben Taskar:
Determinantal point processes for machine learning. CoRR abs/1207.6083 (2012) - [i1]Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox:
Markov Determinantal Point Processes. CoRR abs/1210.4850 (2012) - 2011
- [c10]Alex Kulesza, Ben Taskar:
k-DPPs: Fixed-Size Determinantal Point Processes. ICML 2011: 1193-1200 - [c9]Alex Kulesza, Ben Taskar:
Learning Determinantal Point Processes. UAI 2011: 419-427 - 2010
- [j2]Mark Dredze
Multi-domain learning by confidence-weighted parameter combination. Mach. Learn. 79(1-2): 123-149 (2010) - [j1]Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan:
A theory of learning from different domains. Mach. Learn. 79(1-2): 151-175 (2010) - [c8]Alex Kulesza, Ben Taskar:
Structured Determinantal Point Processes. NIPS 2010: 1171-1179 - [c7]Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira:
Exploiting Feature Covariance in High-Dimensional Online Learning. AISTATS 2010: 493-500
2000 – 2009
- 2009
- [c6]Koby Crammer, Mark Dredze, Alex Kulesza:
Multi-Class Confidence Weighted Algorithms. EMNLP 2009: 496-504 - [c5]Koby Crammer, Alex Kulesza, Mark Dredze:
Adaptive Regularization of Weight Vectors. NIPS 2009: 414-422 - 2007
- [c4]John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman:
Learning Bounds for Domain Adaptation. NIPS 2007: 129-136 - [c3]Alex Kulesza, Fernando Pereira:
Structured Learning with Approximate Inference. NIPS 2007: 785-792 - [c2]Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns:
Empirical Price Modeling for Sponsored Search. WINE 2007: 541-548 - 2004
- [c1]John Blatz, Erin Fitzgerald, George F. Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchís, Nicola Ueffing:
Confidence Estimation for Machine Translation. COLING 2004
Coauthor Index
aka: Jennifer A. Gillenwater
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last updated on 2024-09-25 01:35 CEST by the dblp team
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