← Authors

Lenka Zdeborová

44 papers · 2013–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🏃 Academic Marathon (12) 🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (6) 🏠 Conference Loyalist (24) 🧬 Topic Evolution 🏆 Keyword Champion (2) 👑 Triple Crown 🤝 Dynamic Duo (40) 🔬 Deep Specialist (16) 🔥 Unstoppable (8) ⚡ Prolific Year (7) 📈 Trend Setter 💎 Century Club (44) 🗃️ Keyword Collector (140) ❓ The Questioner

Conferences

NIPS (24) ICML (10) COLT (3) UAI (3) AISTATS (2) ICLR (1) JMLR (1)

Top co-authors

Florent Krzakala (40) Bruno Loureiro (14) Hugo Cui (8) Antoine Maillard (5) Yatin Dandi (5) Emanuele Troiani (4) jean barbier (4) Pierfrancesco Urbani (4) Stefano Sarao Mannelli (4) Lucas Clarte (4)

Keywords

approximate message passing (10) generalization error (9) neural network (7) phase transition (5) gaussian mixture (5) statistical learning (4) compressed sensing (4) high-dimensional analysis (4) gradient descent (3) message passing (3) stochastic gradient descent (3) state evolution (3) principal component analysis (2) signal recovery (2) double descent (2) kernel regression (2) mean squared error (2) non-convex optimization (2) uncertainty quantification (2) high-dimensional statistics (2)

Papers

Fundamental computational limits of weak learnability in high-dimensional multi-index models AISTATS 2025
Building Conformal Prediction Intervals with Approximate Message Passing UAI 2025
Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds ICML 2025
Counting in Small Transformers: The Delicate Interplay between Attention and Feed-Forward Layers ICML 2025
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications COLT 2025
Fundamental Limits of Non-Linear Low-Rank Matrix Estimation COLT 2024
A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention NIPS 2024
Bayes-optimal learning of an extensive-width neural network from quadratically many samples NIPS 2024
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity ICLR 2024
Asymptotics of feature learning in two-layer networks after one gradient-step ICML 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents ICML 2024
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression UAI 2024
Universality laws for Gaussian mixtures in generalized linear models NIPS 2023
On double-descent in uncertainty quantification in overparametrized models AISTATS 2023
High-dimensional Asymptotics of Denoising Autoencoders NIPS 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width ICML 2023
Expectation consistency for calibration of neural networks UAI 2023
Tree-AMP: Compositional Inference with Tree Approximate Message Passing JMLR 2023
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks NIPS 2022
Multi-layer State Evolution Under Random Convolutional Design NIPS 2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap NIPS 2022
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed ICML 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model NIPS 2021
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NIPS 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime NIPS 2021
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization NIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions NIPS 2020
Generalisation error in learning with random features and the hidden manifold model ICML 2020
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture ICML 2020
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval NIPS 2020
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NIPS 2020
Phase retrieval in high dimensions: Statistical and computational phase transitions NIPS 2020
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models ICML 2019
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models NIPS 2019
The spiked matrix model with generative priors NIPS 2019
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup NIPS 2019
Entropy and mutual information in models of deep neural networks NIPS 2018
The committee machine: Computational to statistical gaps in learning a two-layers neural network NIPS 2018
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models COLT 2018
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula NIPS 2016
Swept Approximate Message Passing for Sparse Estimation ICML 2015
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation NIPS 2015
Spectral Clustering of graphs with the Bethe Hessian NIPS 2014
Blind Calibration in Compressed Sensing using Message Passing Algorithms NIPS 2013