Aleksander Mądry

47 papers receiving 1.1k citations

Hit Papers

Dataset Security for Machine Learning: Data Poisoning, Ba...202220262023202420224080120

Peers

Aleksander Mądry
Comparison fields: 5 of 100
  • Artificial Intelligence 684
  • Computer Networks and Communications 325
  • Computational Theory and Mathematics 291
  • Computer Vision and Pattern Recognition 213
  • Signal Processing 142
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Countries citing papers authored by Aleksander Mądry

Since Specialization
Citations

This map shows the geographic impact of Aleksander Mądry'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 Aleksander Mądry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aleksander Mądry more than expected).

Fields of papers citing papers by Aleksander Mądry

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aleksander Mądry. 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 Aleksander Mądry. The network helps show where Aleksander Mądry may publish in the future.

Co-authorship network of co-authors of Aleksander Mądry

This figure shows the co-authorship network connecting the top 25 collaborators of Aleksander Mądry. A scholar is included among the top collaborators of Aleksander Mądry 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 Aleksander Mądry. Aleksander Mądry 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
#WorkIndexed citations
1 2
2 2
3 2
4 13
5 22
6
Implementation Matters in Deep RL: A Case Study on PPO and TRPO
48
7
Learning Perceptually-Aligned Representations via Adversarial Robustness.
15
8
Image Synthesis with a Single (Robust) Classifier
9
9
Computer Vision with a Single (Robust) Classifier.
8
10
There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)
22
11
Adversarially Robust Generalization Requires More Data
56
12
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms
17
13
Spectral Signatures in Backdoor Attacks
53
14
Prior convictions: Black-box adversarial attacks with bandits and priors
17
15
Training for faster adversarial robustness verification via inducing Relu stability
13
16
Towards Understanding the Dynamics of Generative Adversarial Networks.
8
17
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
99
18 20
19 29
20 17

About Aleksander Mądry

Aleksander Mądry is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Networks and Communications, having authored 47 papers that have together received 1.2k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (15 papers), Complexity and Algorithms in Graphs (12 papers) and Optimization and Search Problems (8 papers). The work is most often cited by research in Artificial Intelligence (684 citations), Computational Theory and Mathematics (291 citations) and Computer Networks and Communications (325 citations). Aleksander Mądry has collaborated with scholars based in United States, Switzerland and Israel. Frequent co-authors include Dimitris Tsipras, Logan Engstrom, Brandon Tran, Shibani Santurkar, Jonathan A. Kelner, Ludwig Schmidt, Andrew Ilyas, Alexander Turner, Paul F. Christiano and Daniel A. Spielman. Their work appears in journals such as Nature Neuroscience, IEEE Transactions on Pattern Analysis and Machine Intelligence and Operations Research.

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.

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