default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSushant Sachdeva
Person information
- affiliation: University of Toronto, Canada
- affiliation (former): Yale University, New Haven, Connecticut, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c40]Cella Florescu, Rasmus Kyng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Optimal Electrical Oblivious Routing on Expanders. ICALP 2024: 65:1-65:19- [c39]Sushant Sachdeva, Anvith Thudi, Yibin Zhao:
Better Sparsifiers for Directed Eulerian Graphs. ICALP 2024: 119:1-119:20 - [c38]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. ITCS 2024: 13:1-13:24 - [c37]Gramoz Goranci, Monika Henzinger, Harald Räcke, Sushant Sachdeva, A. R. Sricharan:
Electrical Flows for Polylogarithmic Competitive Oblivious Routing. ITCS 2024: 55:1-55:22 - [c36]Jan van den Brand
, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. SODA 2024: 2980-2998 - [c35]Sally Dong, Gramoz Goranci, Lawrence Li, Sushant Sachdeva, Guanghao Ye:
Fast Algorithms for Separable Linear Programs. SODA 2024: 3558-3604 - [i51]Cella Florescu, Rasmus Kyng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Optimal Electrical Oblivious Routing on Expanders. CoRR abs/2406.07252 (2024) - [i50]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Simon Meierhans, Maximilian Probst Gutenberg, Sushant Sachdeva:
Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality. CoRR abs/2407.10830 (2024) - [i49]Arun Jambulapati, Sushant Sachdeva, Aaron Sidford, Kevin Tian, Yibin Zhao:
Eulerian Graph Sparsification by Effective Resistance Decomposition. CoRR abs/2408.10172 (2024) - 2023
- [j8]Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Almost-Linear-Time Algorithms for Maximum Flow and Minimum-Cost Flow. Commun. ACM 66(12): 85-92 (2023) - [j7]Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang:
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation via Short Cycle Decompositions. SIAM J. Comput. 52(6): S18-85 (2023) - [c34]Jan van den Brand, Li Chen, Richard Peng, Rasmus Kyng, Yang P. Liu, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. FOCS 2023: 503-514 - [c33]Lawrence Li, Sushant Sachdeva:
A New Approach to Estimating Effective Resistances and Counting Spanning Trees in Expander Graphs. SODA 2023: 2728-2745 - [c32]Li Chen, Rasmus Kyng, Maximilian Probst Gutenberg, Sushant Sachdeva:
A Simple Framework for Finding Balanced Sparse Cuts via APSP. SOSA 2023: 42-55 - [c31]Sushant Sachdeva, Yibin Zhao:
A Simple and Efficient Parallel Laplacian Solver. SPAA 2023: 315-325 - [i48]Gramoz Goranci, Monika Henzinger, Harald Räcke, Sushant Sachdeva, A. R. Sricharan:
Electrical Flows for Polylogarithmic Competitive Oblivious Routing. CoRR abs/2303.02491 (2023) - [i47]Sushant Sachdeva, Yibin Zhao:
A Simple and Efficient Parallel Laplacian Solver. CoRR abs/2304.14345 (2023) - [i46]Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff:
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra. CoRR abs/2305.05826 (2023) - [i45]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. CoRR abs/2309.16629 (2023) - [i44]Sally Dong, Gramoz Goranci, Lawrence Li, Sushant Sachdeva, Guanghao Ye:
Fast Algorithms for Separable Linear Programs. CoRR abs/2310.16351 (2023) - [i43]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. CoRR abs/2311.03174 (2023) - [i42]Sushant Sachdeva, Anvith Thudi, Yibin Zhao:
Better Sparsifiers for Directed Eulerian Graphs. CoRR abs/2311.06232 (2023) - 2022
- [c30]Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. FOCS 2022: 612-623 - [c29]Vijay Keswani, Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi:
A Convergent and Dimension-Independent Min-Max Optimization Algorithm. ICML 2022: 10939-10973 - [c28]Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye:
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. SODA 2022: 124-153 - [i41]Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva:
Maximum Flow and Minimum-Cost Flow in Almost-Linear Time. CoRR abs/2203.00671 (2022) - [i40]Sally Dong, Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Guanghao Ye:
Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time. CoRR abs/2205.01562 (2022) - [i39]Deeksha Adil, Brian Bullins, Arun Jambulapati, Sushant Sachdeva:
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities. CoRR abs/2205.06167 (2022) - [i38]Li Chen, Rasmus Kyng, Maximilian Probst Gutenberg, Sushant Sachdeva:
A Simple Framework for Finding Balanced Sparse Cuts via APSP. CoRR abs/2209.08845 (2022) - [i37]Lawrence Li, Sushant Sachdeva:
A New Approach to Estimating Effective Resistances and Counting Spanning Trees in Expander Graphs. CoRR abs/2211.01468 (2022) - [i36]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Fast Algorithms for 𝓁p-Regression. CoRR abs/2211.03963 (2022) - 2021
- [c27]Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva:
Almost-Linear-Time Weighted 𝓁p-Norm Solvers in Slightly Dense Graphs via Sparsification. ICALP 2021: 9:1-9:15 - [c26]Deeksha Adil, Brian Bullins, Sushant Sachdeva:
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. NeurIPS 2021: 19122-19133 - [i35]Deeksha Adil, Brian Bullins, Rasmus Kyng, Sushant Sachdeva:
Almost-linear-time Weighted 𝓁p-norm Solvers in Slightly Dense Graphs via Sparsification. CoRR abs/2102.06977 (2021) - [i34]Deeksha Adil, Brian Bullins, Sushant Sachdeva:
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization. CoRR abs/2107.02432 (2021) - 2020
- [c25]Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang:
Faster Graph Embeddings via Coarsening. ICML 2020: 2953-2963 - [c24]Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse:
Regularized linear autoencoders recover the principal components, eventually. NeurIPS 2020 - [c23]Deeksha Adil, Sushant Sachdeva:
Faster p-norm minimizing flows, via smoothed q-norm problems. SODA 2020: 892-910 - [i33]Oren Mangoubi, Sushant Sachdeva, Nisheeth K. Vishnoi:
A Provably Convergent and Practical Algorithm for Min-max Optimization with Applications to GANs. CoRR abs/2006.12376 (2020) - [i32]Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, Chi Wang:
Faster Graph Embeddings via Coarsening. CoRR abs/2007.02817 (2020) - [i31]Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse:
Regularized linear autoencoders recover the principal components, eventually. CoRR abs/2007.06731 (2020)
2010 – 2019
- 2019
- [c22]Krishnamurthy Viswanathan, Sushant Sachdeva, Andrew Tomkins, Sujith Ravi:
Improved Semi-Supervised Learning with Multiple Graphs. AISTATS 2019: 3032-3041 - [c21]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS 2019: 8194-8205 - [c20]Deeksha Adil, Richard Peng, Sushant Sachdeva:
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. NeurIPS 2019: 14166-14177 - [c19]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Iterative Refinement for ℓp-norm Regression. SODA 2019: 1405-1424 - [c18]Yang P. Liu, Sushant Sachdeva, Zejun Yu:
Short Cycles via Low-Diameter Decompositions. SODA 2019: 2602-2615 - [c17]Rasmus Kyng, Richard Peng, Sushant Sachdeva, Di Wang:
Flows in almost linear time via adaptive preconditioning. STOC 2019: 902-913 - [i30]Deeksha Adil, Rasmus Kyng, Richard Peng, Sushant Sachdeva:
Iterative Refinement for 𝓁p-norm Regression. CoRR abs/1901.06764 (2019) - [i29]Rasmus Kyng, Richard Peng, Sushant Sachdeva, Di Wang:
Flows in Almost Linear Time via Adaptive Preconditioning. CoRR abs/1906.10340 (2019) - [i28]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR abs/1907.04164 (2019) - [i27]Deeksha Adil, Richard Peng, Sushant Sachdeva:
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression. CoRR abs/1907.07167 (2019) - [i26]Deeksha Adil, Sushant Sachdeva:
Faster p-norm minimizing flows, via smoothed q-norm problems. CoRR abs/1910.10571 (2019) - 2018
- [c16]Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang:
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions. FOCS 2018: 361-372 - [c15]Rina Panigrahy, Ali Rahimi, Sushant Sachdeva, Qiuyi Zhang:
Convergence Results for Neural Networks via Electrodynamics. ITCS 2018: 22:1-22:19 - [c14]Amey Bhangale, Subhash Khot, Swastik Kopparty, Sushant Sachdeva, Devanathan Thiruvenkatachari:
Near-optimal approximation algorithm for simultaneous Max-Cut. SODA 2018: 1407-1425 - [i25]Amey Bhangale, Subhash Khot, Swastik Kopparty, Sushant Sachdeva, Devanathan Thiruvenkatachari:
Near-optimal approximation algorithm for simultaneous Max-Cut. CoRR abs/1801.04497 (2018) - [i24]Timothy Chu, Yu Gao, Richard Peng, Sushant Sachdeva, Saurabh Sawlani, Junxing Wang:
Graph Sparsification, Spectral Sketches, and Faster Resistance Computation, via Short Cycle Decompositions. CoRR abs/1805.12051 (2018) - [i23]Yang P. Liu, Sushant Sachdeva, Zejun Yu:
Short Cycles via Low-Diameter Decompositions. CoRR abs/1810.05143 (2018) - 2017
- [c13]Rasmus Kyng, Jakub Pachocki, Richard Peng, Sushant Sachdeva:
A Framework for Analyzing Resparsification Algorithms. SODA 2017: 2032-2043 - [c12]David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva:
Sampling random spanning trees faster than matrix multiplication. STOC 2017: 730-742 - [i22]Qiuyi Zhang, Rina Panigrahy, Sushant Sachdeva:
Electron-Proton Dynamics in Deep Learning. CoRR abs/1702.00458 (2017) - 2016
- [j6]Sushant Sachdeva, Nisheeth K. Vishnoi:
The mixing time of the Dikin walk in a polytope - A simple proof. Oper. Res. Lett. 44(5): 630-634 (2016) - [c11]Rasmus Kyng, Sushant Sachdeva:
Approximate Gaussian Elimination for Laplacians - Fast, Sparse, and Simple. FOCS 2016: 573-582 - [c10]Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman:
Sparsified Cholesky and multigrid solvers for connection laplacians. STOC 2016: 842-850 - [i21]Rasmus Kyng, Sushant Sachdeva:
Approximate Gaussian Elimination for Laplacians: Fast, Sparse, and Simple. CoRR abs/1605.02353 (2016) - [i20]Rasmus Kyng, Jakub Pachocki, Richard Peng, Sushant Sachdeva:
A Framework for Analyzing Resparsification Algorithms. CoRR abs/1611.06940 (2016) - [i19]David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva:
Sampling Random Spanning Trees Faster than Matrix Multiplication. CoRR abs/1611.07451 (2016) - 2015
- [j5]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. Algorithmica 72(1): 215-236 (2015) - [j4]Venkatesan Guruswami, Sushant Sachdeva, Rishi Saket:
Inapproximability of Minimum Vertex Cover on k-Uniform k-Partite Hypergraphs. SIAM J. Discret. Math. 29(1): 36-58 (2015) - [c9]Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman:
Algorithms for Lipschitz Learning on Graphs. COLT 2015: 1190-1223 - [c8]Amey Bhangale, Swastik Kopparty, Sushant Sachdeva:
Simultaneous Approximation of Constraint Satisfaction Problems. ICALP (1) 2015: 193-205 - [c7]Rasmus Kyng, Anup Rao, Sushant Sachdeva:
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms. NIPS 2015: 2719-2727 - [i18]Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman:
Algorithms for Lipschitz Learning on Graphs. CoRR abs/1505.00290 (2015) - [i17]Rasmus Kyng, Anup Rao, Sushant Sachdeva:
Fast, Provable Algorithms for Isotonic Regression in all ℓp-norms. CoRR abs/1507.00710 (2015) - [i16]Sushant Sachdeva, Nisheeth K. Vishnoi:
A Simple Analysis of the Dikin Walk. CoRR abs/1508.01977 (2015) - [i15]Rasmus Kyng, Yin Tat Lee, Richard Peng, Sushant Sachdeva, Daniel A. Spielman:
Sparsified Cholesky and Multigrid Solvers for Connection Laplacians. CoRR abs/1512.01892 (2015) - 2014
- [j3]Frédéric Cazals, Tom Dreyfus, Sushant Sachdeva, N. Shah:
Greedy Geometric Algorithms for Collection of Balls, with Applications to Geometric Approximation and Molecular Coarse-Graining. Comput. Graph. Forum 33(6): 1-17 (2014) - [j2]Sushant Sachdeva, Nisheeth K. Vishnoi:
Faster Algorithms via Approximation Theory. Found. Trends Theor. Comput. Sci. 9(2): 125-210 (2014) - [i14]Amey Bhangale, Swastik Kopparty, Sushant Sachdeva:
Simultaneous Approximation of Constraint Satisfaction Problems. CoRR abs/1407.7759 (2014) - [i13]Amey Bhangale, Swastik Kopparty, Sushant Sachdeva:
Simultaneous Approximation of Constraint Satisfaction Problems. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [b1]Sushant Sachdeva:
New Results in the Theory of Approximation: Fast Graph Algorithms and Inapproximability. Princeton University, USA, 2013 - [c6]Sushant Sachdeva, Rishi Saket:
Optimal Inapproximability for Scheduling Problems via Structural Hardness for Hypergraph Vertex Cover. CCC 2013: 219-229 - [i12]Pooya Hatami, Sushant Sachdeva, Madhur Tulsiani:
An Arithmetic Analogue of Fox's Triangle Removal Argument. CoRR abs/1304.4921 (2013) - [i11]Sushant Sachdeva, Nisheeth K. Vishnoi:
Matrix Inversion Is As Easy As Exponentiation. CoRR abs/1305.0526 (2013) - [i10]Sushant Sachdeva, Nisheeth K. Vishnoi:
Approximation Theory and the Design of Fast Algorithms. CoRR abs/1309.4882 (2013) - [i9]Venkatesan Guruswami, Sushant Sachdeva, Rishi Saket:
Inapproximability of Minimum Vertex Cover on k-uniform k-partite Hypergraphs. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [c5]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles - Revisited. APPROX-RANDOM 2012: 362-373 - [c4]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". NIPS 2012: 2384-2392 - [c3]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding overlapping communities in social networks: toward a rigorous approach. EC 2012: 37-54 - [c2]Lorenzo Orecchia, Sushant Sachdeva, Nisheeth K. Vishnoi:
Approximating the exponential, the lanczos method and an Õ(m)-time spectral algorithm for balanced separator. STOC 2012: 1141-1160 - [i8]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. CoRR abs/1206.5349 (2012) - [i7]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles -- Revisited. CoRR abs/1207.4783 (2012) - [i6]Sanjeev Arora, Arnab Bhattacharyya, Rajsekar Manokaran, Sushant Sachdeva:
Testing Permanent Oracles - Revisited. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [j1]Sébastien Loriot, Sushant Sachdeva, Karine Bastard, Chantal Prévost, Frédéric Cazals:
On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking. IEEE ACM Trans. Comput. Biol. Bioinform. 8(2): 487-498 (2011) - [c1]Sushant Sachdeva, Rishi Saket:
Nearly Optimal NP-Hardness of Vertex Cover on k-Uniform k-Partite Hypergraphs. APPROX-RANDOM 2011: 327-338 - [i5]Sanjeev Arora, James R. Lee, Sushant Sachdeva:
A Reformulation of the Arora-Rao-Vazirani Structure Theorem. CoRR abs/1102.1456 (2011) - [i4]Sushant Sachdeva, Madhur Tulsiani:
Cuts in Cartesian Products of Graphs. CoRR abs/1105.3383 (2011) - [i3]Sushant Sachdeva, Rishi Saket:
Nearly Optimal NP-Hardness of Vertex Cover on k-Uniform k-Partite Hypergraphs. CoRR abs/1105.4175 (2011) - [i2]Lorenzo Orecchia, Sushant Sachdeva, Nisheeth K. Vishnoi:
Approximating the Exponential, the Lanczos Method and an \tilde{O}(m)-Time Spectral Algorithm for Balanced Separator. CoRR abs/1111.1491 (2011) - [i1]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach. CoRR abs/1112.1831 (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 2024-10-07 22:22 CEST 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:
