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ORCIDMarkus Püschel
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- affiliation: ETH Zurich, Switzerland
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2020 – today
- 2024
[j41]Joao Rivera
, Franz Franchetti, Markus Püschel:
Floating-Point TVPI Abstract Domain. Proc. ACM Program. Lang. 8(PLDI): 442-466 (2024)- [c120]Panagiotis Misiakos, Vedran Mihal, Markus Püschel:
Learning Signals and Graphs from Time-Series Graph Data with Few Causes. ICASSP 2024: 9681-9685 - 2023
- [j40]Bastian Seifert, Chris Wendler, Markus Püschel:
Causal Fourier Analysis on Directed Acyclic Graphs and Posets. IEEE Trans. Signal Process. 71: 3805-3820 (2023) - [c119]Vedran Mihal, Markus Püschel:
Möbius Total Variation for Directed Acyclic Graphs. ICASSP 2023: 1-5 - [c118]Panagiotis Misiakos, Chris Wendler, Markus Püschel:
Learning DAGs from Data with Few Root Causes. NeurIPS 2023 - [i25]Panagiotis Misiakos, Chris Wendler, Markus Püschel:
Learning DAGs from Data with Few Root Causes. CoRR abs/2305.15936 (2023) - [i24]Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel:
QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. CoRR abs/2307.03738 (2023) - [i23]Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab:
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data. CoRR abs/2308.15395 (2023) - 2022
- [j39]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
PRIMA: general and precise neural network certification via scalable convex hull approximations. Proc. ACM Program. Lang. 6(POPL): 1-33 (2022) - [c117]Joao Rivera, Franz Franchetti, Markus Püschel:
A Compiler for Sound Floating-Point Computations using Affine Arithmetic. CGO 2022: 66-78 - [c116]Vedran Mihal, Bastian Seifert, Markus Püschel:
Porting Signal Processing from Undirected to Directed Graphs: Case Study Signal Denoising with Unrolling Networks. EUSIPCO 2022: 2076-2080 - [c115]Jakob Weissteiner, Chris Wendler, Sven Seuken, Benjamin Lubin, Markus Püschel:
Fourier Analysis-based Iterative Combinatorial Auctions. IJCAI 2022: 549-556 - [i22]Bastian Seifert, Chris Wendler, Markus Püschel:
Causal Fourier Analysis on Directed Acyclic Graphs and Posets. CoRR abs/2209.07970 (2022) - [i21]Tommaso Pegolotti, Bastian Seifert, Markus Püschel:
Fast Möbius and Zeta Transforms. CoRR abs/2211.13706 (2022) - 2021
- [j38]Markus Püschel, Chris Wendler:
Discrete Signal Processing with Set Functions. IEEE Trans. Signal Process. 69: 1039-1053 (2021) - [j37]Bastian Seifert, Markus Püschel:
Digraph Signal Processing With Generalized Boundary Conditions. IEEE Trans. Signal Process. 69: 1422-1437 (2021) - [j36]Markus Püschel, Bastian Seifert, Chris Wendler:
Discrete Signal Processing on Meet/Join Lattices. IEEE Trans. Signal Process. 69: 3571-3584 (2021) - [c114]Chris Wendler, Andisheh Amrollahi, Bastian Seifert, Andreas Krause, Markus Püschel:
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases. AAAI 2021: 10283-10292 - [c113]Joao Rivera, Franz Franchetti, Markus Püschel:
An Interval Compiler for Sound Floating-Point Computations. CGO 2021: 52-64 - [c112]Eliza Wszola, Martin Jaggi, Markus Püschel:
Faster Parallel Training of Word Embeddings. HiPC 2021: 31-41 - [c111]Bastian Seifert, Chris Wendler, Markus Püschel:
Wiener Filter on Meet/Join Lattices. ICASSP 2021: 5355-5359 - [c110]Christoph Müller, François Serre, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Scaling Polyhedral Neural Network Verification on GPUs. MLSys 2021 - [i20]Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Precise Multi-Neuron Abstractions for Neural Network Certification. CoRR abs/2103.03638 (2021) - 2020
- [j35]François Serre, Markus Püschel:
DSL-Based Hardware Generation with Scala: Example Fast Fourier Transforms and Sorting Networks. ACM Trans. Reconfigurable Technol. Syst. 13(1): 1:1-1:23 (2020) - [j34]Nezihe Merve Gürel, Kaan Kara, Alen Stojanov, Tyler M. Smith, Thomas Lemmin, Dan Alistarh, Markus Püschel, Ce Zhang:
Compressive Sensing Using Iterative Hard Thresholding With Low Precision Data Representation: Theory and Applications. IEEE Trans. Signal Process. 68: 4268-4282 (2020) - [c109]Soummya Kar, Markus Püschel, José M. F. Moura:
Finite-Time In-Network Computation of Linear Transforms. ACSSC 2020: 455-459 - [c108]Panagiotis Misiakos, Chris Wendler, Markus Püschel:
Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition. ICASSP 2020: 5635-5639 - [c107]Jingxuan He, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Learning fast and precise numerical analysis. PLDI 2020: 1112-1127 - [i19]Markus Püschel, Chris Wendler:
Discrete Signal Processing with Set Functions. CoRR abs/2001.10290 (2020) - [i18]Bastian Seifert, Markus Püschel:
Digraph Signal Processing with Generalized Boundary Conditions. CoRR abs/2005.09762 (2020) - [i17]Christoph Müller, Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Neural Network Robustness Verification on GPUs. CoRR abs/2007.10868 (2020) - [i16]Jakob Weissteiner, Chris Wendler, Sven Seuken, Benjamin Lubin, Markus Püschel:
Fourier Analysis-based Iterative Combinatorial Auctions. CoRR abs/2009.10749 (2020) - [i15]Chris Wendler, Andisheh Amrollahi, Bastian Seifert, Andreas Krause, Markus Püschel:
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases. CoRR abs/2010.00439 (2020) - [i14]Markus Püschel, Bastian Seifert, Chris Wendler:
Discrete Signal Processing on Meet/Join Lattices. CoRR abs/2012.04358 (2020)
2010 – 2019
- 2019
- [j33]Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3(POPL): 41:1-41:30 (2019) - [c106]François Serre, Markus Püschel:
DSL-Based Modular IP Core Generators: Example FFT and Related Structures. ARITH 2019: 190-191 - [c105]Chris Wendler, Markus Püschel:
Sampling Signals On Meet/Join Lattices. GlobalSIP 2019: 1-5 - [c104]Alen Stojanov, Tiark Rompf, Markus Püschel:
A stage-polymorphic IR for compiling MATLAB-style dynamic tensor expressions. GPCE 2019: 34-47 - [c103]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. HiPC 2019: 184-194 - [c102]François Serre, Markus Püschel:
In Search of the Optimal Walsh-hadamard Transform for Streamed Parallel Processing. ICASSP 2019: 1532-1536 - [c101]Markus Püschel:
A Discrete Signal Processing Framework for Meet/join Lattices with Applications to Hypergraphs and Trees. ICASSP 2019: 5371-5375 - [c100]Gagandeep Singh, Timon Gehr, Markus Püschel, Martin T. Vechev:
Boosting Robustness Certification of Neural Networks. ICLR (Poster) 2019 - [c99]Chris Wendler, Markus Püschel, Dan Alistarh:
Powerset Convolutional Neural Networks. NeurIPS 2019: 927-938 - [c98]Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin T. Vechev:
Beyond the Single Neuron Convex Barrier for Neural Network Certification. NeurIPS 2019: 15072-15083 - [i13]Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel:
On Linear Learning with Manycore Processors. CoRR abs/1905.00626 (2019) - [i12]Daniele G. Spampinato, Diego Fabregat-Traver, Markus Püschel, Paolo Bientinesi:
Program Generation for Linear Algebra Using Multiple Layers of DSLs. CoRR abs/1906.08613 (2019) - [i11]Chris Wendler, Dan Alistarh, Markus Püschel:
Powerset Convolutional Neural Networks. CoRR abs/1909.02253 (2019) - 2018
- [j32]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
A practical construction for decomposing numerical abstract domains. Proc. ACM Program. Lang. 2(POPL): 55:1-55:28 (2018) - [j31]Franz Franchetti, Tze Meng Low, Doru-Thom Popovici, Richard Michael Veras, Daniele G. Spampinato, Jeremy R. Johnson, Markus Püschel, James C. Hoe, José M. F. Moura:
SPIRAL: Extreme Performance Portability. Proc. IEEE 106(11): 1935-1968 (2018) - [c97]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast Numerical Program Analysis with Reinforcement Learning. CAV (1) 2018: 211-229 - [c96]Alen Stojanov, Ivaylo Toskov, Tiark Rompf, Markus Püschel:
SIMD intrinsics on managed language runtimes. CGO 2018: 2-15 - [c95]Daniele G. Spampinato, Diego Fabregat-Traver, Paolo Bientinesi, Markus Püschel:
Program generation for small-scale linear algebra applications. CGO 2018: 327-339 - [c94]François Serre, Markus Püschel:
Memory-Efficient Fast Fourier Transform on Streaming Data by Fusing Permutations. FPGA 2018: 219-228 - [c93]François Serre, Markus Püschel:
A DSL-Based FFT Hardware Generator in Scala. FPL 2018: 315-322 - [c92]Markus Püschel:
A Discrete Signal Processing Framework for Set Functions. ICASSP 2018: 4359-4363 - [c91]Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev:
Fast and Effective Robustness Certification. NeurIPS 2018: 10825-10836 - [c90]Alen Stojanov, Tyler Michael Smith, Dan Alistarh, Markus Püschel:
Fast Quantized Arithmetic on x86: Trading Compute for Data Movement. SiPS 2018: 349-354 - [i10]Daniele G. Spampinato, Diego Fabregat-Traver, Paolo Bientinesi, Markus Püschel:
Program Generation for Small-Scale Linear Algebra Applications. CoRR abs/1805.04775 (2018) - 2017
- [c89]François Serre, Markus Püschel:
Optimal Streamed Linear Permutations. ARITH 2017: 60-61 - [c88]Georg Ofenbeck, Tiark Rompf, Markus Püschel:
Staging for generic programming in space and time. GPCE 2017: 15-28 - [c87]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Fast polyhedra abstract domain. POPL 2017: 46-59 - [i9]François Serre, Markus Püschel:
Characterizing and Enumerating Walsh-Hadamard Transform Algorithms. CoRR abs/1710.08029 (2017) - 2016
- [j30]Marcela Zuluaga, Andreas Krause, Markus Püschel:
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem. J. Mach. Learn. Res. 17: 104:1-104:32 (2016) - [j29]Marcela Zuluaga, Peter A. Milder, Markus Püschel:
Streaming Sorting Networks. ACM Trans. Design Autom. Electr. Syst. 21(4): 55:1-55:30 (2016) - [c86]Daniele G. Spampinato, Markus Püschel:
A basic linear algebra compiler for structured matrices. CGO 2016: 117-127 - [c85]François Serre, Thomas Holenstein, Markus Püschel:
Optimal Circuits for Streamed Linear Permutations Using RAM. FPGA 2016: 215-223 - [c84]Markus Püschel:
Program generation for performance. ASE 2016: 1 - [c83]Georg Ofenbeck, Tiark Rompf, Markus Püschel:
RandIR: differential testing for embedded compilers. SCALA@SPLASH 2016: 21-30 - 2015
- [j28]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed Optimization With Local Domains: Applications in MPC and Network Flows. IEEE Trans. Autom. Control. 60(7): 2004-2009 (2015) - [c82]Nikolaos Kyrtatas, Daniele G. Spampinato, Markus Püschel:
A basic linear algebra compiler for embedded processors. DATE 2015: 1054-1059 - [c81]Gagandeep Singh, Markus Püschel, Martin T. Vechev:
Making numerical program analysis fast. PLDI 2015: 303-313 - [c80]Tiark Rompf, Kevin J. Brown, HyoukJoong Lee, Arvind K. Sujeeth, Manohar Jonnalagedda, Nada Amin, Georg Ofenbeck, Alen Stojanov, Yannis Klonatos, Mohammad Dashti, Christoph Koch, Markus Püschel, Kunle Olukotun:
Go Meta! A Case for Generative Programming and DSLs in Performance Critical Systems. SNAPL 2015: 238-261 - 2014
- [c79]Daniele G. Spampinato, Markus Püschel:
A Basic Linear Algebra Compiler. CGO 2014: 23 - [c78]Benjamin Hess, Thomas R. Gross, Markus Püschel:
Automatic locality-friendly interface extension of numerical functions. GPCE 2014: 83-92 - [c77]Victoria Caparrós Cabezas, Markus Püschel:
Extending the roofline model: Bottleneck analysis with microarchitectural constraints. IISWC 2014: 222-231 - [c76]Georg Ofenbeck, Ruedi Steinmann, Victoria Caparrós Cabezas, Daniele G. Spampinato, Markus Püschel:
Applying the roofline model. ISPASS 2014: 76-85 - [c75]Alen Stojanov, Georg Ofenbeck, Tiark Rompf, Markus Püschel:
Abstracting Vector Architectures in Library Generators: Case Study Convolution Filters. ARRAY@PLDI 2014: 14-19 - [c74]Jörn Schumacher, Markus Püschel:
High-performance sparse fast Fourier transforms. SiPS 2014: 13-18 - 2013
- [j27]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization. IEEE Trans. Signal Process. 61(10): 2718-2723 (2013) - [c73]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
A unified algorithmic approach to distributed optimization. GlobalSIP 2013: 607-610 - [c72]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed compressed sensing algorithms: Completing the puzzle. GlobalSIP 2013: 629 - [c71]Georg Ofenbeck, Tiark Rompf, Alen Stojanov, Martin Odersky, Markus Püschel:
Spiral in scala: towards the systematic construction of generators for performance libraries. GPCE 2013: 125-134 - [c70]Marcela Zuluaga, Guillaume Sergent, Andreas Krause, Markus Püschel:
Active Learning for Multi-Objective Optimization. ICML (1) 2013: 462-470 - [i8]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed Optimization With Local Domains: Applications in MPC and Network Flows. CoRR abs/1305.1885 (2013) - 2012
- [j26]Peter A. Milder, Franz Franchetti, James C. Hoe, Markus Püschel:
Computer Generation of Hardware for Linear Digital Signal Processing Transforms. ACM Trans. Design Autom. Electr. Syst. 17(2): 15:1-15:33 (2012) - [j25]Aliaksei Sandryhaila, Samir Saba, Markus Püschel, Jelena Kovacevic:
Efficient Compression of QRS Complexes Using Hermite Expansion. IEEE Trans. Signal Process. 60(2): 947-955 (2012) - [j24]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed Basis Pursuit. IEEE Trans. Signal Process. 60(4): 1942-1956 (2012) - [j23]Aliaksei Sandryhaila, Jelena Kovacevic, Markus Püschel:
Algebraic Signal Processing Theory: 1-D Nearest Neighbor Models. IEEE Trans. Signal Process. 60(5): 2247-2259 (2012) - [c69]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed ADMM for model predictive control and congestion control. CDC 2012: 5110-5115 - [c68]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
ADMM for consensus on colored networks. CDC 2012: 5116-5121 - [c67]Marcela Zuluaga, Peter A. Milder, Markus Püschel:
Computer generation of streaming sorting networks. DAC 2012: 1245-1253 - [c66]Robert Koutsoyannis, Peter A. Milder, Christian R. Berger, Madeleine Glick, James C. Hoe, Markus Püschel:
Improving fixed-point accuracy of FFT cores in O-OFDM systems. ICASSP 2012: 1585-1588 - [c65]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
D-ADMM: A distributed algorithm for compressed sensing and other separable optimization problems. ICASSP 2012: 2869-2872 - [c64]Marcela Zuluaga, Andreas Krause, Peter A. Milder, Markus Püschel:
"Smart" design space sampling to predict Pareto-optimal solutions. LCTES 2012: 119-128 - [c63]Markus Püschel:
Compiling math to fast code. PEPM 2012: 1-2 - 2011
- [j22]Aliaksei Sandryhaila, Jelena Kovacevic, Markus Püschel:
Algebraic Signal Processing Theory: Cooley-Tukey-Type Algorithms for Polynomial Transforms Based on Induction. SIAM J. Matrix Anal. Appl. 32(2): 364-384 (2011) - [c62]Aliaksei Sandryhaila, Jelena Kovacevic, Markus Püschel:
Compression of QRS complexes using Hermite expansion. ICASSP 2011: 581-584 - [c61]Christian R. Berger, Volodymyr Arbatov, Yevgen Voronenko, Franz Franchetti, Markus Püschel:
Real-time software implementation of an IEEE 802.11a baseband receiver on Intel multicore. ICASSP 2011: 1693-1696 - [c60]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Basis Pursuit in sensor networks. ICASSP 2011: 2916-2919 - [c59]Daniel S. McFarlin, Volodymyr Arbatov, Franz Franchetti, Markus Püschel:
Automatic SIMD vectorization of fast fourier transforms for the larrabee and AVX instruction sets. ICS 2011: 265-274 - [c58]Markus Püschel:
Automatic performance programming. Onward! 2011: 1-2 - [r2]Franz Franchetti, Markus Püschel:
FFT (Fast Fourier Transform). Encyclopedia of Parallel Computing 2011: 658-671 - [r1]Markus Püschel, Franz Franchetti, Yevgen Voronenko:
Spiral. Encyclopedia of Parallel Computing 2011: 1920-1933 - 2010
- [j21]Jelena Kovacevic, Markus Püschel:
Algebraic signal processing theory: sampling for infinite and finite 1-D space. IEEE Trans. Signal Process. 58(1): 242-257 (2010) - [j20]Aliaksei Sandryhaila, Amina Chebira, Christina Milo, Jelena Kovacevic, Markus Püschel:
Systematic construction of real lapped tight frame transforms. IEEE Trans. Signal Process. 58(5): 2556-2567 (2010) - [c57]Frédéric de Mesmay, Srinivas Chellappa, Franz Franchetti, Markus Püschel:
Computer Generation of Efficient Software Viterbi Decoders. HiPEAC 2010: 353-368 - [c56]Peter A. Milder, Franz Franchetti, James C. Hoe, Markus Püschel:
Hardware implementation of the discrete fourier transform with non-power-of-two problem size. ICASSP 2010: 1546-1549 - [c55]Frédéric de Mesmay, Yevgen Voronenko, Markus Püschel:
Offline library adaptation using automatically generated heuristics. IPDPS 2010: 1-10 - [e1]Christoph W. Kessler, Welf Löwe, David A. Padua, Markus Püschel:
Program Composition and Optimization: Autotuning, Scheduling, Metaprogramming and Beyond, 09.05. - 12.05.2010. Dagstuhl Seminar Proceedings 10191, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2010 [contents] - [i7]Christoph W. Kessler, Welf Löwe, David A. Padua, Markus Püschel:
10191 Abstracts Collection - Program Composition and Optimization : Autotuning, Scheduling, Metaprogramming and Beyond. Program Composition and Optimization: Autotuning, Scheduling, Metaprogramming and Beyond 2010 - [i6]Christoph W. Kessler, Welf Löwe, David A. Padua, Markus Püschel:
10191 Executive Summary - Program Composition and Optimization : Autotuning, Scheduling, Metaprogramming and Beyond. Program Composition and Optimization: Autotuning, Scheduling, Metaprogramming and Beyond 2010 - [i5]Christoph W. Kessler, Welf Löwe, David A. Padua, Markus Püschel:
Program Composition and Optimization: An Introduction. Program Composition and Optimization: Autotuning, Scheduling, Metaprogramming and Beyond 2010 - [i4]Aliaksei Sandryhaila, Jelena Kovacevic, Markus Püschel:
Algebraic Signal Processing Theory: Cooley-Tukey Type Algorithms for Polynomial Transforms Based on Induction. CoRR abs/1008.2972 (2010) - [i3]João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel:
Distributed Basis Pursuit. CoRR abs/1009.1128 (2010)
2000 – 2009
- 2009
- [j19]Markus Püschel, Peter A. Milder, James C. Hoe:
Permuting streaming data using RAMs. J. ACM 56(2): 10:1-10:34 (2009) - [j18]Franz Franchetti, Markus Püschel, Yevgen Voronenko, Srinivas Chellappa, José M. F. Moura:
Discrete fourier transform on multicore. IEEE Signal Process. Mag. 26(6): 90-102 (2009) - [j17]Yevgen Voronenko, Markus Püschel:
Algebraic Signal Processing Theory: Cooley-Tukey Type Algorithms for Real DFTs. IEEE Trans. Signal Process. 57(1): 205-222 (2009) - [c54]Basilio B. Fraguela, Yevgen Voronenko, Markus Püschel:
Automatic Tuning of Discrete Fourier Transforms Driven by Analytical Modeling. PACT 2009: 271-280 - [c53]