← Authors

Simon Lacoste-Julien

67 papers · 2005–2025 · 11 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (20) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (11) πŸ—ΊοΈ Taxonomy Completionist (22) 🏠 Conference Loyalist (20) πŸ† Keyword Champion (3) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (29) 🀝 Dynamic Duo (14) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (13) ⚑ Prolific Year (8) ❓ The Questioner πŸ’Ž Century Club (67) πŸ—ƒοΈ Keyword Collector (58)

Conferences

NIPS (20) AISTATS (16) ICML (12) ICLR (9) CLEAR (2) CVPR (2) JMLR (2) EMNLP (1) ICCV (1) NAACL (1) UAI (1)

Top co-authors

Gauthier Gidel (14) Pascal Vincent (7) Ioannis Mitliagkas (7) Francis Bach (7) Hugo Berard (6) SΓ©bastien Lachapelle (6) Yoshua Bengio (5) Nicolas Loizou (4) Juan Ramirez (4) Anton Osokin (4)

Keywords

stochastic gradient descent (7) convex optimization (6) frank-wolfe optimization (5) structured prediction (5) convergence rate (5) neural network (4) frank-wolfe algorithm (4) constrained optimization (4) independent component analysis (4) variance reduction (3) gradient descent (3) convergence analysis (3) incremental gradient (3) stochastic optimization (3) linear convergence (3) bilinear game (3) learning theory (2) representation learning (2) strong convexity (2) stochastic gradient (2)

Papers

Performative Prediction on Games and Mechanism Design AISTATS 2025
Accelerating Training with Neuron Interaction and Nowcasting Networks ICLR 2025
Feasible Learning AISTATS 2025
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization ICML 2024
Weight-Sharing Regularization AISTATS 2024
Balancing Act: Constraining Disparate Impact in Sparse Models ICLR 2024
On the Identifiability of Quantized Factors CLEAR 2024
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning ICML 2023
Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? ICML 2023
CrossSplit: Mitigating Label Noise Memorization through Data Splitting ICML 2023
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation NIPS 2023
Unlocking Slot Attention by Changing Optimal Transport Costs ICML 2023
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation ICLR 2022
Bayesian structure learning with generative flow networks UAI 2022
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints NIPS 2022
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution NIPS 2022
Data-Efficient Structured Pruning via Submodular Optimization NIPS 2022
On the Convergence of Continuous Constrained Optimization for Structure Learning AISTATS 2022
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA CLEAR 2022
Online Adversarial Attacks ICLR 2022
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning ICLR 2021
Structured Convolutional Kernel Networks for Airline Crew Scheduling ICML 2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity NIPS 2021
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence AISTATS 2021
Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets ICML 2021
Implicit Regularization via Neural Feature Alignment AISTATS 2021
An Analysis of the Adaptation Speed of Causal Models AISTATS 2021
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation AISTATS 2020
Adversarial Example Games NIPS 2020
Differentiable Causal Discovery from Interventional Data NIPS 2020
Accelerating Smooth Games by Manipulating Spectral Shapes AISTATS 2020
GAIT: A Geometric Approach to Information Theory AISTATS 2020
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games AISTATS 2020
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks ICLR 2020
Gradient-Based Neural DAG Learning ICLR 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games ICML 2020
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks NIPS 2019
Negative Momentum for Improved Game Dynamics AISTATS 2019
A Variational Inequality Perspective on Generative Adversarial Networks ICLR 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates NIPS 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient NIPS 2019
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods JMLR 2018
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates NIPS 2018
Frank-Wolfe Splitting via Augmented Lagrangian Method AISTATS 2018
SEARNN: Training RNNs with global-local losses ICLR 2018
Frank-Wolfe Algorithms for Saddle Point Problems AISTATS 2017
ASAGA: Asynchronous Parallel SAGA AISTATS 2017
Joint Discovery of Object States and Manipulation Actions ICCV 2017
A Closer Look at Memorization in Deep Networks ICML 2017
On Structured Prediction Theory with Calibrated Convex Surrogate Losses NIPS 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization NIPS 2017
PAC-Bayesian Theory Meets Bayesian Inference NIPS 2016
Unsupervised Learning From Narrated Instruction Videos CVPR 2016
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs ICML 2016
Beyond CCA: Moment Matching for Multi-View Models ICML 2016
Rethinking LDA: Moment Matching for Discrete ICA NIPS 2015
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering AISTATS 2015
On Pairwise Costs for Network Flow Multi-Object Tracking CVPR 2015
Barrier Frank-Wolfe for Marginal Inference NIPS 2015
Variance Reduced Stochastic Gradient Descent with Neighbors NIPS 2015
On the Global Linear Convergence of Frank-Wolfe Optimization Variants NIPS 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NIPS 2014
Block-Coordinate Frank-Wolfe Optimization for Structural SVMs ICML 2013
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification NIPS 2008
Structured Prediction, Dual Extragradient and Bregman Projections JMLR 2006
Word Alignment via Quadratic Assignment NAACL 2006
A Discriminative Matching Approach to Word Alignment EMNLP 2005