default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDNisarg Shah 0001
Person information
- affiliation: University of Toronto
- affiliation: Harvard University
- affiliation: Carnegie Mellon University
Other persons with the same name
- Nisarg Shah 0002 — Indian Institute of Technology, Kharagpur, West Bengal, India
- Nisarg Shah 0003 — Indian Institute of Technology Gandhinagar, Gujarat, India
- Nisarg Shah 0004 — University of Wisconsin-Madison, WI, USA
- Nisarg Shah 0005 — Indiana Bloomington University, IN, USA
Other persons with a similar name
- Nisarg A. Shah
![[0000-0001-9113-3965]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-9113-3965")
— Indian Institute of Technology Jodhpur, Department of Electrical Engineering, India
SPARQL queries 
🛈 Please note that only 69% of the records listed on this page have a DOI. Therefore, DOI-based queries can only provide partial results.
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j21]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Primarily about primaries. Artif. Intell. 329: 104095 (2024)- [j20]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class fairness in online matching. Artif. Intell. 335: 104177 (2024) - [j19]Haris Aziz, Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation. Oper. Res. 72(4): 1674-1688 (2024) - [j18]Soroush Ebadian, Anson Kahng, Dominik Peters, Nisarg Shah:
Optimized Distortion and Proportional Fairness in Voting. ACM Trans. Economics and Comput. 12(1): 3:1-3:39 (2024) - [c77]Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian, Nisarg Shah:
Computational Aspects of Distortion. AAMAS 2024: 499-507 - [c76]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Harm Ratio: A Novel and Versatile Fairness Criterion. EAAMO 2024: 3:1-3:14 - [c75]Evi Micha, Shreyas Sekar, Nisarg Shah:
What Is Best for Students, Numerical Scores or Letter Grades? IJCAI 2024: 2949-2957 - [i25]Evi Micha, Shreyas Sekar, Nisarg Shah:
What is Best for Students, Numerical Scores or Letter Grades? CoRR abs/2406.15405 (2024) - [i24]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Harm Ratio: A Novel and Versatile Fairness Criterion. CoRR abs/2410.02977 (2024) - 2023
- [c74]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class Fairness in Online Matching. AAAI 2023: 5673-5680 - [c73]Anson Kahng, Mohamad Latifian, Nisarg Shah:
Voting with Preference Intensities. AAAI 2023: 5697-5704 - [c72]Lily Li, Evi Micha, Aleksandar Nikolov, Nisarg Shah:
Partitioning Friends Fairly. AAAI 2023: 5747-5754 - [c71]Soroush Ebadian, Mohamad Latifian, Nisarg Shah:
The Distortion of Approval Voting with Runoff. AAMAS 2023: 1752-1760 - [c70]Haris Aziz, Evi Micha, Nisarg Shah:
Group Fairness in Peer Review. AAMAS 2023: 2889-2891 - [c69]Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, Nisarg Shah:
Proportionally Fair Online Allocation of Public Goods with Predictions. IJCAI 2023: 20-28 - [c68]Nisarg Shah:
Pushing the Limits of Fairness in Algorithmic Decision-Making. IJCAI 2023: 7051-7056 - [c67]Haris Aziz, Evi Micha, Nisarg Shah:
Group Fairness in Peer Review. NeurIPS 2023 - [c66]Soroush Ebadian, Aris Filos-Ratsikas, Mohamad Latifian, Nisarg Shah:
Explainable and Efficient Randomized Voting Rules. NeurIPS 2023 - [c65]Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah:
Best of Both Distortion Worlds. EC 2023: 738-758 - [i23]Vasilis Gkatzelis, Mohamad Latifian, Nisarg Shah:
Best of Both Distortion Worlds. CoRR abs/2305.19453 (2023) - 2022
- [j17]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The metric distortion of multiwinner voting. Artif. Intell. 313: 103802 (2022) - [c64]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The Metric Distortion of Multiwinner Voting. AAAI 2022: 4900-4907 - [c63]Ioannis Caragiannis, Evi Micha, Nisarg Shah:
A Little Charity Guarantees Fair Connected Graph Partitioning. AAAI 2022: 4908-4916 - [c62]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Little House (Seat) on the Prairie: Compactness, Gerrymandering, and Population Distribution. AAMAS 2022: 154-162 - [c61]Soroush Ebadian, Dominik Peters, Nisarg Shah:
How to Fairly Allocate Easy and Difficult Chores. AAMAS 2022: 372-380 - [c60]Allan Borodin, Daniel Halpern, Mohamad Latifian, Nisarg Shah:
Distortion in Voting with Top-t Preferences. IJCAI 2022: 116-122 - [c59]Soroush Ebadian, Rupert Freeman, Nisarg Shah:
Efficient Resource Allocation with Secretive Agents. IJCAI 2022: 272-278 - [c58]Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah:
Is Sortition Both Representative and Fair? NeurIPS 2022 - [c57]Soroush Ebadian, Anson Kahng, Dominik Peters, Nisarg Shah:
Optimized Distortion and Proportional Fairness in Voting. EC 2022: 563-600 - [i22]Ioannis Caragiannis, Nisarg Shah, Alexandros A. Voudouris:
The Metric Distortion of Multiwinner Voting. CoRR abs/2201.13332 (2022) - [i21]Hadi Hosseini, Zhiyi Huang, Ayumi Igarashi, Nisarg Shah:
Class Fairness in Online Matching. CoRR abs/2203.03751 (2022) - [i20]Soroush Ebadian, Anson Kahng, Nisarg Shah, Dominik Peters:
Optimized Distortion and Proportional Fairness in Voting. CoRR abs/2205.15760 (2022) - [i19]Siddhartha Banerjee, Vasilis Gkatzelis, Safwan Hossain, Billy Jin, Evi Micha, Nisarg Shah:
Proportionally Fair Online Allocation of Public Goods with Predictions. CoRR abs/2209.15305 (2022) - 2021
- [j16]Safwan Hossain, Nisarg Shah:
The effect of strategic noise in linear regression. Auton. Agents Multi Agent Syst. 35(2): 21 (2021) - [j15]Gerdus Benadè, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Preference Elicitation for Participatory Budgeting. Manag. Sci. 67(5): 2813-2827 (2021) - [j14]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in social choice problems: an annotated reading list. SIGecom Exch. 19(1): 12-14 (2021) - [c56]Daniel Halpern, Gregory Kehne, Dominik Peters, Ariel D. Procaccia, Nisarg Shah, Piotr Skowron:
Aggregating Binary Judgments Ranked by Accuracy. AAAI 2021: 5456-5463 - [c55]Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar:
Necessarily Optimal One-Sided Matchings. AAAI 2021: 5481-5488 - [c54]Dominik Peters, Grzegorz Pierczynski, Nisarg Shah, Piotr Skowron:
Market-Based Explanations of Collective Decisions. AAAI 2021: 5656-5663 - [c53]Rupert Freeman, Evi Micha, Nisarg Shah:
Two-Sided Matching Meets Fair Division. IJCAI 2021: 203-209 - [c52]Daniel Halpern, Nisarg Shah:
Fair and Efficient Resource Allocation with Partial Information. IJCAI 2021: 224-230 - [c51]Hadi Hosseini, Debmalya Mandal, Nisarg Shah, Kevin Shi:
Surprisingly Popular Voting Recovers Rankings, Surprisingly! IJCAI 2021: 245-251 - [c50]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in Social Choice Problems: The First 15 Years and Beyond. IJCAI 2021: 4294-4301 - [c49]Safwan Hossain, Evi Micha, Nisarg Shah:
Fair Algorithms for Multi-Agent Multi-Armed Bandits. NeurIPS 2021: 24005-24017 - [i18]Elliot Anshelevich, Aris Filos-Ratsikas, Nisarg Shah, Alexandros A. Voudouris:
Distortion in Social Choice Problems: The First 15 Years and Beyond. CoRR abs/2103.00911 (2021) - [i17]Hadi Hosseini, Debmalya Mandal, Nisarg Shah, Kevin Shi:
Surprisingly Popular Voting Recovers Rankings, Surprisingly! CoRR abs/2105.09386 (2021) - [i16]Daniel Halpern, Nisarg Shah:
Fair and Efficient Resource Allocation with Partial Information. CoRR abs/2105.10064 (2021) - [i15]Rupert Freeman, Evi Micha, Nisarg Shah:
Two-Sided Matching Meets Fair Division. CoRR abs/2107.07404 (2021) - [i14]Soroush Ebadian, Dominik Peters, Nisarg Shah:
How to Fairly Allocate Easy and Difficult Chores. CoRR abs/2110.11285 (2021) - 2020
- [j13]Arpit Agarwal, Debmalya Mandal, David C. Parkes, Nisarg Shah:
Peer Prediction with Heterogeneous Users. ACM Trans. Economics and Comput. 8(1): 2:1-2:34 (2020) - [c48]Safwan Hossain, Evi Micha, Nisarg Shah:
The Surprising Power of Hiding Information in Facility Location. AAAI 2020: 2168-2175 - [c47]Evi Micha, Nisarg Shah:
Can We Predict the Election Outcome from Sampled Votes? AAAI 2020: 2176-2183 - [c46]Safwan Hossain, Nisarg Shah:
The Effect of Strategic Noise in Linear Regression. AAMAS 2020: 511-519 - [c45]Vasilis Gkatzelis, Daniel Halpern, Nisarg Shah:
Resolving the Optimal Metric Distortion Conjecture. FOCS 2020: 1427-1438 - [c44]Evi Micha, Nisarg Shah:
Proportionally Fair Clustering Revisited. ICALP 2020: 85:1-85:16 - [c43]Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation. EC 2020: 21-22 - [c42]Debmalya Mandal, Nisarg Shah, David P. Woodruff:
Optimal Communication-Distortion Tradeoff in Voting. EC 2020: 795-813 - [c41]Siddharth Barman, Umang Bhaskar, Nisarg Shah:
Optimal Bounds on the Price of Fairness for Indivisible Goods. WINE 2020: 356-369 - [c40]Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, Nisarg Shah:
Fair Division with Binary Valuations: One Rule to Rule Them All. WINE 2020: 370-383 - [c39]Safwan Hossain, Andjela Mladenovic, Nisarg Shah:
Designing Fairly Fair Classifiers Via Economic Fairness Notions. WWW 2020: 1559-1569 - [i13]Haris Aziz, Nisarg Shah:
Participatory Budgeting: Models and Approaches. CoRR abs/2003.00606 (2020) - [i12]Vasilis Gkatzelis, Daniel Halpern, Nisarg Shah:
Resolving the Optimal Metric Distortion Conjecture. CoRR abs/2004.07447 (2020) - [i11]Rupert Freeman, Nisarg Shah, Rohit Vaish:
Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation. CoRR abs/2005.14122 (2020) - [i10]Daniel Halpern, Ariel D. Procaccia, Alexandros Psomas, Nisarg Shah:
Fair Division with Binary Valuations: One Rule to Rule Them All. CoRR abs/2007.06073 (2020) - [i9]Siddharth Barman, Umang Bhaskar, Nisarg Shah:
Settling the Price of Fairness for Indivisible Goods. CoRR abs/2007.06242 (2020) - [i8]Safwan Hossain, Evi Micha, Nisarg Shah:
Fair Algorithms for Multi-Agent Multi-Armed Bandits. CoRR abs/2007.06699 (2020) - [i7]Safwan Hossain, Nisarg Shah:
The Effect of Strategic Noise in Linear Regression. CoRR abs/2007.07316 (2020) - [i6]Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar:
Learning Desirable Matchings From Partial Preferences. CoRR abs/2007.09079 (2020)
2010 – 2019
- 2019
- [j12]Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, Junxing Wang:
The Unreasonable Fairness of Maximum Nash Welfare. ACM Trans. Economics and Comput. 7(3): 12:1-12:32 (2019) - [c38]Colleen Alkalay-Houlihan, Nisarg Shah:
The Pure Price of Anarchy of Pool Block Withholding Attacks in Bitcoin Mining. AAAI 2019: 1724-1731 - [c37]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Primarily about Primaries. AAAI 2019: 1804-1811 - [c36]Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan:
Group Fairness for the Allocation of Indivisible Goods. AAAI 2019: 1853-1860 - [c35]Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David P. Woodruff:
Efficient and Thrifty Voting by Any Means Necessary. NeurIPS 2019: 7178-7189 - [c34]Daniel Halpern, Nisarg Shah:
Fair Division with Subsidy. SAGT 2019: 374-389 - 2018
- [j11]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof linear regression in high dimensions: an overview. SIGecom Exch. 17(1): 54-60 (2018) - [j10]David Kurokawa, Ariel D. Procaccia, Nisarg Shah:
Leximin Allocations in the Real World. ACM Trans. Economics and Comput. 6(3-4): 11:1-11:24 (2018) - [c33]Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway:
Big City vs. the Great Outdoors: Voter Distribution and How It Affects Gerrymandering. IJCAI 2018: 98-104 - [c32]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof Linear Regression in High Dimensions. EC 2018: 9-26 - [c31]Brandon Fain, Kamesh Munagala, Nisarg Shah:
Fair Allocation of Indivisible Public Goods. EC 2018: 575-592 - [i5]Brandon Fain, Kamesh Munagala, Nisarg Shah:
Fair Allocation of Indivisible Public Goods. CoRR abs/1805.03164 (2018) - [i4]Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah:
Strategyproof Linear Regression in High Dimensions. CoRR abs/1805.10693 (2018) - 2017
- [j9]Nisarg Shah:
Making the world fairer. XRDS 24(1): 24-28 (2017) - [j8]Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Subset Selection Via Implicit Utilitarian Voting. J. Artif. Intell. Res. 58: 123-152 (2017) - [c30]Gerdus Benade, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Preference Elicitation For Participatory Budgeting. AAAI 2017: 376-382 - [c29]Nisarg Shah:
Optimal Social Decision Making. AAMAS 2017: 5 - [c28]Arpit Agarwal, Debmalya Mandal, David C. Parkes, Nisarg Shah:
Peer Prediction with Heterogeneous Users. EC 2017: 81-98 - [c27]Vincent Conitzer, Rupert Freeman, Nisarg Shah:
Fair Public Decision Making. EC 2017: 629-646 - 2016
- [j7]Ariel D. Procaccia, Nisarg Shah, Yair Zick:
Voting rules as error-correcting codes. Artif. Intell. 231: 1-16 (2016) - [j6]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
When Do Noisy Votes Reveal the Truth? ACM Trans. Economics and Comput. 4(3): 15:1-15:30 (2016) - [c26]Ariel D. Procaccia, Nisarg Shah:
Optimal Aggregation of Uncertain Preferences. AAAI 2016: 608-614 - [c25]Markus Brill, Vincent Conitzer, Rupert Freeman, Nisarg Shah:
False-Name-Proof Recommendations in Social Networks. AAMAS 2016: 332-340 - [c24]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
Truthful Univariate Estimators. ICML 2016: 127-135 - [c23]Ioannis Caragiannis, Swaprava Nath, Ariel D. Procaccia, Nisarg Shah:
Subset Selection via Implicit Utilitarian Voting. IJCAI 2016: 151-157 - [c22]Markus Brill, Vincent Conitzer, Rupert Freeman, Nisarg Shah:
False-Name-Proof Recommendations in Social Networks. ISAIM 2016 - [c21]Ioannis Caragiannis, David Kurokawa, Hervé Moulin, Ariel D. Procaccia, Nisarg Shah, Junxing Wang:
The Unreasonable Fairness of Maximum Nash Welfare. EC 2016: 305-322 - 2015
- [j5]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average case analysis of the classical algorithm for Markov decision processes with Büchi objectives. Theor. Comput. Sci. 573: 71-89 (2015) - [j4]David C. Parkes, Ariel D. Procaccia, Nisarg Shah:
Beyond Dominant Resource Fairness: Extensions, Limitations, and Indivisibilities. ACM Trans. Economics and Comput. 3(1): 3:1-3:22 (2015) - [c20]Ariel D. Procaccia, Nisarg Shah, Yair Zick:
Voting Rules As Error-Correcting Codes. AAAI 2015: 1000-1006 - [c19]Ariel D. Procaccia, Nisarg Shah, Eric Sodomka:
Ranked Voting on Social Networks. IJCAI 2015: 2040-2046 - [c18]Ariel D. Procaccia, Nisarg Shah:
Is Approval Voting Optimal Given Approval Votes? NIPS 2015: 1801-1809 - [c17]David Kurokawa, Ariel D. Procaccia, Nisarg Shah:
Leximin Allocations in the Real World. EC 2015: 345-362 - 2014
- [j3]Ian A. Kash, Ariel D. Procaccia, Nisarg Shah:
No Agent Left Behind: Dynamic Fair Division of Multiple Resources. J. Artif. Intell. Res. 51: 579-603 (2014) - [c16]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
Modal Ranking: A Uniquely Robust Voting Rule. AAAI 2014: 616-622 - [c15]Willemien Kets, David M. Pennock, Rajiv Sethi, Nisarg Shah:
Betting Strategies, Market Selection, and the Wisdom of Crowds. AAAI 2014: 735-741 - [c14]Ariel D. Procaccia, Nisarg Shah, Max Lee Tucker:
On the Structure of Synergies in Cooperative Games. AAAI 2014: 763-769 - [c13]Yoram Bachrach, Rahul Savani, Nisarg Shah:
Cooperative max games and agent failures. AAMAS 2014: 29-36 - [c12]Albert Xin Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe:
Diverse Randomized Agents Vote to Win. NIPS 2014: 2573-2581 - [c11]Sébastien Lahaie, Nisarg Shah:
Neutrality and geometry of mean voting. EC 2014: 333-350 - [c10]Edith Elkind, Nisarg Shah:
Electing the Most Probable Without Eliminating the Irrational: Voting Over Intransitive Domains. UAI 2014: 182-191 - 2013
- [j2]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic algorithms for qualitative analysis of Markov decision processes with Büchi objectives. Formal Methods Syst. Des. 42(3): 301-327 (2013) - [c9]Ian A. Kash, Ariel D. Procaccia, Nisarg Shah:
No agent left behind: dynamic fair division of multiple resources. AAMAS 2013: 351-358 - [c8]Albert Xin Jiang, Ariel D. Procaccia, Yundi Qian, Nisarg Shah, Milind Tambe:
Defender (Mis)coordination in Security Games. IJCAI 2013: 220-226 - [c7]Yoram Bachrach, Nisarg Shah:
Reliability Weighted Voting Games. SAGT 2013: 38-49 - [c6]Ioannis Caragiannis, Ariel D. Procaccia, Nisarg Shah:
When do noisy votes reveal the truth? EC 2013: 143-160 - 2012
- [j1]Manas Joglekar, Nisarg Shah, Ajit A. Diwan:
Balanced group-labeled graphs. Discret. Math. 312(9): 1542-1549 (2012) - [c5]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives. FSTTCS 2012: 461-473 - [c4]David C. Parkes, Ariel D. Procaccia, Nisarg Shah:
Beyond dominant resource fairness: extensions, limitations, and indivisibilities. EC 2012: 808-825 - [c3]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. UAI 2012: 695-704 - [c2]Yoram Bachrach, Ian A. Kash, Nisarg Shah:
Agent Failures in Totally Balanced Games and Convex Games. WINE 2012: 15-29 - [i3]Krishnendu Chatterjee, Manas Joglekar, Nisarg Shah:
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with Büchi Objectives. CoRR abs/1202.4175 (2012) - [i2]Ariel D. Procaccia, Sashank Jakkam Reddi, Nisarg Shah:
A Maximum Likelihood Approach For Selecting Sets of Alternatives. CoRR abs/1210.4882 (2012) - 2011
- [c1]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives. CAV 2011: 260-276 - [i1]Krishnendu Chatterjee, Monika Henzinger, Manas Joglekar, Nisarg Shah:
Symbolic Algorithms for Qualitative Analysis of Markov Decision Processes with Büchi Objectives. CoRR abs/1104.3348 (2011)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-07 20:32 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
