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Christoph Hertrich

22 papers hereh-index 10332 citations25 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author10
  • middle author10
  • last author2

Across the 22 of 22 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • math.CO5
  • cs.CC3
  • cs.DS2
  • math.OC2
  • cs.GT1
same name
  • Christoph Hertrich — 3 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedThe Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality

19 citations · 67 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

math.OC2023

A First Order Method for Linear Programming Parameterized by Circuit Imbalance

Richard Cole, Christoph Hertrich, Yixin Tao +1

Various first order approaches have been proposed in the literature to solve Linear Programming (LP) problems, recently leading to practically efficient solvers for large-scale LPs…

cs.GT2023★ 2 cited

Mode Connectivity in Auction Design

Christoph Hertrich, Yixin Tao, László A. Végh

Optimal auction design is a fundamental problem in algorithmic game theory. This problem is notoriously difficult already in very simple settings. Recent work in differentiable eco…

cs.CC2023★ 4 cited

Training Neural Networks is NP-Hard in Fixed Dimension

Vincent Froese, Christoph Hertrich

We study the parameterized complexity of training two-layer neural networks with respect to the dimension of the input data and the number of hidden neurons, considering ReLU and l…

cs.LG2023★ 3 cited

Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes

Christian Haase, Christoph Hertrich, Georg Loho

We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width. More precise…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.