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A Fully Dynamic Algorithm for k-Regret Minimizing Sets [article]

Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan
2020 arXiv   pre-print
The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery.  ...  Although the k-RMS problem has been extensively studied in the literature, existing methods are designed for the static setting and cannot maintain the result efficiently when the database is updated.  ...  ACKNOWLEDGMENT We thank anonymous reviewers for their helpful comments to improve this research. Yanhao Wang has been supported by the MLDB project of Academy of Finland (decision number: 322046).  ... 
arXiv:2005.14493v3 fatcat:pqhzgqzrorc5jksqpcntub5tw4

Finding Average Regret Ratio Minimizing Set in Database [article]

Sepanta Zeighami, Raymong Chi-Wing Wong
2018 arXiv   pre-print
In this paper, we would like to find a set of k points such that on average, the satisfaction (ratio) of a user is maximized.  ...  Motivated by this, in this paper, we propose algorithms for this problem. Finally, we conducted experiments to show the effectiveness and the efficiency of the algorithms.  ...  Therefore, we need to compute an integral when computing the average regret ratio for a given set.  ... 
arXiv:1810.08047v1 fatcat:vqn6dwxrlvgcjpkmfz2c5rurca

Diversifying with Few Regrets, But too Few to Mention

Zaeem Hussain, Hina A. Khan, Mohamed A. Sharaf
2015 Proceedings of the Second International Workshop on Databases and the Web - ExploreDB'15  
Motivated by that need, in this work we propose a novel scheme called ReDi, which aims to generate representative data that balance the tradeoff between regret minimization and diversity maximization.  ...  We perform extensive experimental evaluation to measure the tradeoff between the effectiveness and efficiency provided by the different ReDi algorithms.  ...  In general, a local search algorithm Algorithm 3 ReDi-SWAP Algorithm Input: A set of D dimensional points P , a set V div , a set Vreg, an integer k and λ Output: A subset of P of size k denoted by S  ... 
doi:10.1145/2795218.2795225 dblp:conf/sigmod/HussainKS15 fatcat:ugisu5u76ncrpauxjvaagtafcq

GRMR: Generalized Regret-Minimizing Representatives [article]

Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan
2020 arXiv   pre-print
The regret-minimizing set (RMS) problem is recently proposed for representative discovery from databases.  ...  For two-dimensional databases, we propose an optimal algorithm for GRMR via a transformation into the shortest cycle problem in a directed graph.  ...  The average regret minimization [34, 37, 47] problem has also been investigated recently.  ... 
arXiv:2007.09634v1 fatcat:oh2nswugbfd4hpj3f6udauvja4

Efficient Algorithms for k-Regret Minimizing Sets [article]

Pankaj K. Agarwal and Nirman Kumar and Stavros Sintos and Subhash Suri
2017 arXiv   pre-print
In particular, a k-regret minimizing set has the property that the regret ratio between the score of the top-1 item in Q and the score of the top-k item in P is minimized, where the score of an item is  ...  We also carry out extensive experimental evaluation, and show that our schemes compute regret-minimizing sets comparable in size to the greedy algorithm proposed in [VLDB 14] but our schemes are significantly  ...  1 O(d) can be very large even for moderately small d (say d > 20), thus severely limiting the practical utility of these algorithms.  ... 
arXiv:1702.01446v2 fatcat:okpys2hycfejfizkntjum7byem

Sorting-based Interactive Regret Minimization [article]

Jiping Zheng, Chen Chen
2020 arXiv   pre-print
As an important tool for multi-criteria decision making in database systems, the regret minimization query is shown to have the merits of top-k and skyline queries: it controls the output size while does  ...  In this paper, we study how to enhance current interactive regret minimization query by sorting mechanism.  ...  Acknowledgments This work is partially supported by the National Natural Science Foundation of China under grants U1733112, 61702260 and the Fundamental Research Funds for the Central Universities under  ... 
arXiv:2006.10949v1 fatcat:lkhz5d5b3zb77hnmjy5b2rlwqi

K-Regret Queries Using Multiplicative Utility Functions

Jianzhong Qi, Fei Zuo, Hanan Samet, Jia Cheng Yao
2018 ACM Transactions on Database Systems  
The k-regret query aims to return a size-k subset S of a database D such that, for any query user that selects a data object from this size-k subset S rather than from database D, her regret ratio is minimized  ...  Unlike traditional top-k queries, the k-regret query does not minimize the regret ratio for a specific utility function.  ...  Ashwin Lall for sharing the code of MinWidth and Area-Greedy, and providing feedback on our work.  ... 
doi:10.1145/3230634 fatcat:dhzruitgj5dbfopxtxl26z5osu

Approximating Regret Minimizing Sets: A Happiness Perspective [article]

Phoomraphee Luenam, Yau Pun Chen, Raymond Chi-Wing Wong
2022 arXiv   pre-print
In this paper, we study the k-Regret Minimizing Sets (k-RMS) and Average Regret Minimizing Sets (ARMS) problems. k-RMS selects r records from a database such that the maximum regret ratio between the k-th  ...  best score in the database and the best score in the selected records for any possible utility function is minimized.  ...  Another approach is Average Regret Minimizing Sets (ARMS), where instead of minimizing the maximum regret ratio, the objective is instead minimizing the average regret ratio across a distribution of (possibly  ... 
arXiv:2102.03578v3 fatcat:hdlvnqkwa5dihhvyj7ofuvm7nu

Regret-minimizing representative databases

Danupon Nanongkai, Atish Das Sarma, Ashwin Lall, Richard J. Lipton, Jun Xu
2010 Proceedings of the VLDB Endowment  
In particular, for any number k and any class of utility functions, the k-regret query outputs k tuples from the database and tries to minimize the maximum regret ratio.  ...  We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making.  ...  Acknowledgement: We thank anonymous reviewers for very helpful comments on the paper.  ... 
doi:10.14778/1920841.1920980 fatcat:jlvj3fl2v5ggtpd2lxmwhjqy4m


Ios Kotsogiannis, Ashwin Machanavajjhala, Michael Hay, Gerome Miklau
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
Differential privacy has emerged as a preferred standard for ensuring privacy in analysis tasks on sensitive datasets.  ...  The result is an end-to-end differentially private system: Pythia, which we show offers improvements over using any single algorithm alone.  ...  However, the FAS must be penalized for selecting algorithms that are not in the same cluster as A in the θGC. θ-group clusterings can be efficiently computed due to the following property: for all i ≤  ... 
doi:10.1145/3035918.3035945 dblp:conf/sigmod/KotsogiannisMHM17 fatcat:ff5r7hxsq5g3xj6ztrv7dnuyra

Online Spectral Learning on a Graph with Bandit Feedback

Quanquan Gu, Jiawei Han
2014 2014 IEEE International Conference on Data Mining  
We show that it attains O(cd √ T log T ) regret bound, which is only a √ T factor worse than the proposed algorithm in the full information setting.  ...  First, we present an online multiclass classification algorithm in the full information setting. It is based on function learning on a graph using the spectral information of the graph Laplacian.  ...  Coauthor 2 is an undirected co-author graph data set extracted from the DBLP database in four areas: machine learning, data mining, information retrieval and databases.  ... 
doi:10.1109/icdm.2014.72 dblp:conf/icdm/GuH14 fatcat:ni7mhvykozh2bn7rridxgmombm

New Oracle-Efficient Algorithms for Private Synthetic Data Release [article]

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
2020 arXiv   pre-print
All three algorithms are oracle-efficient in the sense that they are computationally efficient when given access to an optimization oracle.  ...  For all three algorithms, we provide theoretical guarantees for both accuracy and privacy.  ...  No-Regret Dynamics To compute such an equilibrium privately, we will simulate no-regret dynamics between the two players.  ... 
arXiv:2007.05453v1 fatcat:dvpdniiminavlmqrtk5sphatwy

How to Use Heuristics for Differential Privacy [article]

Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018 arXiv   pre-print
We show that there is an efficient algorithm for privately constructing synthetic data for any such class, given a non-private learning oracle.  ...  This in particular gives the first oracle-efficient algorithm for privately generating synthetic data for contingency tables.  ...  Acknowledgements We thank Michael Kearns, Adam Smith, Jon Ullman and Salil Vadhan for insightful conversations about this work.  ... 
arXiv:1811.07765v1 fatcat:vdrfkvxevjc73dmdpnfr6ikaha

Learning optimal classifier chains for real-time big data mining

Jie Xu, Cem Tekin, Mihaela van der Schaar
2013 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
For this, chains of classifiers which can detect various concepts need to be constructed in real-time.  ...  A plethora of emerging Big Data applications re quire processing and analyzing streams of data to extract valu able information in real-time.  ...  In this subsection, we propose an efficient learning algorithm when the following assumption holds.  ... 
doi:10.1109/allerton.2013.6736568 dblp:conf/allerton/XuTS13 fatcat:tepw456d75dlhf5ljizdvpvxxa

Differentially Private Online Submodular Maximization [article]

Sebastian Perez-Salazar, Rachel Cummings
2020 arXiv   pre-print
In the full-information setting, we develop an (ε,δ)-DP algorithm with expected (1-1/e)-regret bound of 𝒪( k^2log |U|√(T log k/δ)/ε).  ...  In the bandit setting, we provide an (ε,δ+ O(e^-T^1/3))-DP algorithm with expected (1-1/e)-regret bound of 𝒪( √(log k/δ)/ε (k (|U| log |U|)^1/3)^2 T^2/3).  ...  The aim often is to design algorithms with sublinear regret, i.e., o(T ), so that the average payoff over time of the algorithm is comparable with the best average fixed profit in hindsight.  ... 
arXiv:2010.12816v1 fatcat:jbciay4wxregjf6csjkpg7ouiu
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