Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning Representations and Generative Models for 3D Point Clouds
2017259 citationsPanos Achlioptas, Olga Diamanti et al.arXiv (Cornell University)profile →
Citations per year, relative to Ioannis Mitliagkas Ioannis Mitliagkas (= 1×)
peers
Terry Windeatt
Countries citing papers authored by Ioannis Mitliagkas
Since
Specialization
Citations
This map shows the geographic impact of Ioannis Mitliagkas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ioannis Mitliagkas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ioannis Mitliagkas more than expected).
Fields of papers citing papers by Ioannis Mitliagkas
This network shows the impact of papers produced by Ioannis Mitliagkas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ioannis Mitliagkas. The network helps show where Ioannis Mitliagkas may publish in the future.
Co-authorship network of co-authors of Ioannis Mitliagkas
This figure shows the co-authorship network connecting the top 25 collaborators of Ioannis Mitliagkas.
A scholar is included among the top collaborators of Ioannis Mitliagkas based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ioannis Mitliagkas. Ioannis Mitliagkas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Jolicoeur‐Martineau, Alexia, et al.. (2021). Gotta Go Fast with Score-Based Generative Models. Radboud Repository (Radboud University).1 indexed citations
2.
Mitliagkas, Ioannis, et al.. (2020). A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.. International Conference on Artificial Intelligence and Statistics. 2863–2873.7 indexed citations
3.
Scieur, Damien, et al.. (2020). Accelerating Smooth Games by Manipulating Spectral Shapes.. International Conference on Artificial Intelligence and Statistics. 1705–1715.
4.
Gidel, Gauthier, et al.. (2020). Linear Lower Bounds and Conditioning of Differentiable Games. International Conference on Machine Learning. 1. 4583–4593.2 indexed citations
Mitliagkas, Ioannis, et al.. (2019). A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.. arXiv (Cornell University).2 indexed citations
7.
Lamb, Alex, Jonathan Binas, Anirudh Goyal, et al.. (2019). State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. International Conference on Machine Learning. 3622–3631.1 indexed citations
8.
Albuquerque, Isabela, et al.. (2019). Adversarial target-invariant representation learning for domain generalization. arXiv (Cornell University).14 indexed citations
9.
Arnold, Sébastien M. R., et al.. (2019). Reducing the variance in online optimization by transporting past gradients. Neural Information Processing Systems. 32. 5391–5402.2 indexed citations
10.
Xu, Peng, Bryan He, Christopher De, Ioannis Mitliagkas, & Christopher Ré. (2018). Accelerated Stochastic Power Iteration. International Conference on Artificial Intelligence and Statistics. 58–67.10 indexed citations
11.
Verma, Vikas, Alex Lamb, Christopher Beckham, et al.. (2018). Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.. arXiv (Cornell University).19 indexed citations
Zhang, Jian, Ioannis Mitliagkas, & Christopher Ré. (2017). YellowFin and the Art of Momentum Tuning. 1. 289–308.1 indexed citations
15.
Achlioptas, Panos, Olga Diamanti, Ioannis Mitliagkas, & Leonidas Guibas. (2017). Representation Learning and Adversarial Generation of 3D Point Clouds. arXiv (Cornell University).30 indexed citations
16.
Papailiopoulos, Dimitris, Ioannis Mitliagkas, Alexandros G. Dimakis, & Constantine Caramanis. (2014). Finding Dense Subgraphs via Low-Rank Bilinear Optimization. International Conference on Machine Learning. 1890–1898.13 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.