Homepage
Pavel Kolev
Max Planck Institute for Informatics
Department 1: Algorithms and Complexity
Campus E1 4, Room 326
66123 Saarbrücken
Germany
Email:
pkolev
mpi-inf.mpg.de
Phone: +49 681 9325 1026
Since January 2014 to present, I'm a Ph.D. student in the Computer Science Department at Saarland University, under the supervision of Prof. Kurt Mehlhorn and Dr. Karl Bringmann. I am also affiliated with the Algorithms and Complexity Department at Max Planck Institute for Informatics and the Cluster of Excellence on "Multimodal Computing and Interaction".
- Spectral Graph Theory
- Randomized Algorithms
- Machine Learning
-
Frank Ban,
Vijay Bhattiprolu,
Karl Bringmann,
Pavel Kolev,
Euiwoong Lee,
David P. Woodruff
A PTAS for $\ell_{0}$-Low Rank Approximation
-
Karl Bringmann,
Pavel Kolev,
David P. Woodruff
Approximation Algorithms for $\ell_{0}$-Low Rank Approximation.
Advances in Neural Information Processing Systems 30 (NIPS 2017)
-
Ruben Becker,
Vincenzo Bonifaci,
Andreas Karrenbauer,
Pavel Kolev,
Kurt Mehlhorn
Two Results on Slime Mold Computations
Journal of Theoretical Computer Science (TCS), Section C (Theory of Natural Computing).
- Gorav Jindal, Pavel Kolev, Richard Peng,
Saurabh Sawlani
Density Independent Algorithms for Sparsifying k-Step Random Walks
20th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX/RANDOM 2017)
- Pavel Kolev, Kurt Mehlhorn
Approximate Spectral Clustering: Efficiency and Guarantees
A preliminary version
of this paper was presented at the 24th Annual European Symposium on Algorithms (ESA 2016)
- Gorav Jindal, Pavel Kolev.
An Efficient Parallel Algorithm for Spectral Sparsification of Laplacian and SDDM Matrix Polynomials
- Pavel Kolev, He Sun
Dirichlet Eigenvalues, Local Random Walks, and Analyzing Clusters in Graphs
25th International Symposium on Algorithms and Computation (ISAAC 2014).
- Own Work
- Two Results on Slime Mold Computations:
OR'17
- Seminars
- Gradient Descent only Converges to Minimizers:
ATML-SS17
- Faster spectral sparsification and numerical algorithms for SDD matrices:
RGA-SS17
- Approximate Undirected Maximum Flows in O(m polylog(n)) Time:
RGA-WS16
- Faster Online Matrix-Vector Multiplication:
RGA-SS16
- Streaming Lower Bounds for Approximating MAX-CUT:
ALB-WS15
- Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates:
RGA-WS15
- Spectral Sparsification of Random-Walk Matrix Polynomials:
RGA-SS15
- Multi-way spectral partitioning and higher-order Cheeger inequalities:
RGA-WS12
- Conflict Packing Yields Linear Vertex-Kernels for k-FAST, k-dense RTI and a Related Problem:
RGA-SS12
- 49th Annual ACM Symposium on the Theory of Computing: June 19 - 23, 2017, Montreal, Canada
- ACM-SIAM Symposium on Discrete Algorithms: January 10 – 29, 2016, Arlington, Virginia, USA
- MADALGO Summer School on Streaming Algorithms: August 10 – 13, 2015, Aarhus University, Denmark
- Conference on Learning Theory: July 3 - 6, 2015, University Pierre and Marie Curie, Paris, France
- Algorithmic Spectral Graph Theory Boot Camp: August 26 – 29, 2014, US Berkley, USA
- ADFOCS Current Trends in Network Optimization: August 11 - 15, 2014, Saarbrücken, Germany
- EADS Summer School on Hashing: Theory and Applications: July 14 – 17, 2014, University of Copenhagen, Denmark
- ADFOCS Current Trends in Theoretical Computer Science: August 13 - 17, 2012, Saarbrücken, Germany
- JMIV 18
- ITCS 2018
- ESA 2017
- ICALP 2017
- STACS 2017
- MFCS 2016
