On the Simultaneous Minimum Spanning Trees Problem. - Matej Konecný; Stanislav Kucera; Jana Novotná 001; Jakub Pekárek; Martin Smolík; Jakub Tetek; Martin Töpfer 002 (CALDAM, 2018)

Compact I/O-Efficient Representation of Separable Graphs and Optimal Tree Layouts. - Tomas Gavenciak; Jakub Tetek (TAMC, 2019)

Theoretical Model of Computation and Algorithms for FPGA-Based Hardware Accelerators. - Martin Hora; Václav Koncický; Jakub Tetek (TAMC, 2019)

Sampling an Edge Uniformly in Sublinear Time. - Jakub Tetek (CoRR, 2020)

Performance of Bounded-Rational Agents With the Ability to Self-Modify. - Jakub Tetek; Marek Sklenka; Tomas Gavenciak (SafeAI@AAAI, 2021)

CountSketches, Feature Hashing and the Median of Three. - Kasper Green Larsen; Rasmus Pagh; Jakub Tetek (ICML, 2021)

Sampling and Counting Edges via Vertex Accesses. - Jakub Tetek; Mikkel Thorup (CoRR, 2021)

Approximate Triangle Counting via Sampling and Fast Matrix Multiplication. - Jakub Tetek (ICALP, 2022)

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations. - Maciej Besta; Cesare Miglioli; Paolo Sylos Labini; Jakub Tetek; Patrick Iff; Raghavendra Kanakagiri; Saleh Ashkboos; Kacper Janda; Michal Podstawski; Grzegorz Kwasniewski; Niels Gleinig; Flavio Vella; et al. (SC, 2022)

Edge sampling and graph parameter estimation via vertex neighborhood accesses. - Jakub Tetek; Mikkel Thorup (STOC, 2022)

Massively Parallel Computation and Sublinear-Time Algorithms for Embedded Planar Graphs. - Jacob Holm; Jakub Tetek (CoRR, 2022)

Bias Reduction for Sum Estimation. - Talya Eden; Jakob Bæk Tejs Houen; Shyam Narayanan; Will Rosenbaum; Jakub Tetek (APPROX/RANDOM, 2023)

A Nearly Tight Analysis of Greedy k-means++. - Christoph Grunau; Ahmet Alper Özüdogru; Václav Rozhon; Jakub Tetek (SODA, 2023)

Massively Parallel Computation on Embedded Planar Graphs. - Jacob Holm; Jakub Tetek (SODA, 2023)

Sampling an Edge in Sublinear Time Exactly and Optimally. - Talya Eden; Shyam Narayanan; Jakub Tetek (SOSA, 2023)

Estimating the Effective Support Size in Constant Query Complexity. - Shyam Narayanan; Jakub Tetek (SOSA, 2023)

Additive Noise Mechanisms for Making Randomized Approximation Algorithms Differentially Private. - Jakub Tetek (APPROX/RANDOM, 2024)

Universal Optimality of Dijkstra Via Beyond-Worst-Case Heaps. - Bernhard Haeupler; Richard Hladík; Václav Rozhon; Robert E. Tarjan; Jakub Tetek (FOCS, 2024)

Instance-Optimality in I/O-Efficient Sampling and Sequential Estimation. - Shyam Narayanan; Václav Rozhon; Jakub Tetek; Mikkel Thorup (FOCS, 2024)

Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch. - Christian Janos Lebeda; Jakub Tetek (SIGMOD Rec., 2024)

Better Sum Estimation via Weighted Sampling. - Lorenzo Beretta 001; Jakub Tetek (ACM Trans. Algorithms, 2024)

Smooth Sensitivity Revisited: Towards Optimality. - Richard Hladík; Jakub Tetek (FORC, 2025)

Near-Universally-Optimal Differentially Private Minimum Spanning Trees. - Richard Hladík; Jakub Tetek (FORC, 2025)

Fast and Simple Sorting Using Partial Information. - Bernhard Haeupler; Richard Hladík; John Iacono; Václav Rozhon; Robert E. Tarjan; Jakub Tetek (SODA, 2025)

Bidirectional Dijkstra's Algorithm is Instance-Optimal. - Bernhard Haeupler; Richard Hladík; Václav Rozhon; Robert E. Tarjan; Jakub Tetek (SOSA, 2025)

Testing Identity of Distributions under Kolmogorov Distance in Polylogarithmic Space. - Christian Janos Lebeda; Jakub Tetek (SOSA, 2025)

SplineSketch: Even More Accurate Quantiles with Error Guarantees. - Aleksander Lukasiewicz; Jakub Tetek; Pavel Veselý 001 (CoRR, 2025)