Simon Lacoste-Julien
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 10%
- Signal Processing top 10%
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Michael I. JordanBen TaskarFei ShaDan KleinYoshua BengioAaron CourvilleTegan MaharajNicolas Ballas
- Topics
- Stochastic Gradient Optimization Techniques (8 papers)Machine Learning and Algorithms (7 papers)Sparse and Compressive Sensing Techniques (5 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceMachine LearningJournal of Machine Learning Research
- Partner nations
- CanadaFranceUnited States
In The Last Decade
Simon Lacoste-Julien
32 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 850
- Computer Vision and Pattern Recognition 430
- Information Systems 64
- Signal Processing 59
- Statistical and Nonlinear Physics 45
Countries citing papers authored by Simon Lacoste-Julien
This map shows the geographic impact of Simon Lacoste-Julien'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 Simon Lacoste-Julien with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Lacoste-Julien more than expected).
Fields of papers citing papers by Simon Lacoste-Julien
This network shows the impact of papers produced by Simon Lacoste-Julien. 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 Simon Lacoste-Julien. The network helps show where Simon Lacoste-Julien may publish in the future.
Co-authorship network of co-authors of Simon Lacoste-Julien
This figure shows the co-authorship network connecting the top 25 collaborators of Simon Lacoste-Julien. A scholar is included among the top collaborators of Simon Lacoste-Julien 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 Simon Lacoste-Julien. Simon Lacoste-Julien is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 21 | |
| 2 | 4 | |
| 3 | A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games. | 7 |
| 4 | Accelerating Smooth Games by Manipulating Spectral Shapes. | 0 |
| 5 | A Closer Look at the Optimization Landscapes of Generative Adversarial Networks | 4 |
| 6 | A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games. | 2 |
| 7 | Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. | 6 |
| 8 | Reducing Noise in GAN Training with Variance Reduced Extragradient | 5 |
| 9 | 40 | |
| 10 | A Variational Inequality Perspective on Generative Adversarial Networks | 8 |
| 11 | Frank-Wolfe Splitting via Augmented Lagrangian Method | 1 |
| 12 | Improved asynchronous parallel optimization analysis for stochastic incremental methods | 2 |
| 13 | Frank-Wolfe Algorithms for Saddle Point Problems | 4 |
| 14 | Barrier Frank-Wolfe for marginal inference | 1 |
| 15 | Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 24 |
| 16 | Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 9 |
| 17 | Approximate inference for the loss-calibrated Bayesian | 11 |
| 18 | Discriminative machine learning with structure | 6 |
| 19 | 61 | |
| 20 | Structured Prediction via the Extragradient Method | 39 |
About Simon Lacoste-Julien
Simon Lacoste-Julien is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Numerical Analysis, having authored 33 papers that have together received 1.2k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (8 papers), Machine Learning and Algorithms (7 papers) and Sparse and Compressive Sensing Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (850 citations), Computer Vision and Pattern Recognition (430 citations) and Signal Processing (59 citations). Simon Lacoste-Julien has collaborated with scholars based in Canada, France and United States. Frequent co-authors include Michael I. Jordan, Ben Taskar, Fei Sha, Dan Klein, Yoshua Bengio, Aaron Courville, Tegan Maharaj, Nicolas Ballas, Emmanuel Bengio and David Krueger. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Machine Learning and Journal of Machine Learning 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.