NeurIPS 2024PastMath & reasoning
NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning
M3L
- Submission deadline
- Oct 2, 2024, 19:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (81)
Fetched from OpenReview (v2) on 2026-06-10.
A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
A Theoretical Framework for Federated Domain Generalization with Gradient Alignment
A Theory of Initialisation's Impact on Specialisation
Accumulating Data Avoids Model Collapse
Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs
Adversarial Attacks as Near-Zero Eigenvalues in the Empirical Kernel of Neural Networks
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
Algorithmic Stability of Minimum-Norm Interpolating Deep Neural Networks
An empirical study of the $(L_0, L_1)$-smoothness condition
Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks
Benign Overfitting in Out-of-Distribution Generalization of Linear Models
Benign Overfitting in Single-Head Attention
Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
Can Bayesian Neural Networks Make Confident Predictions?
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model
Classifier-Free Guidance is a Predictor-Corrector
Commute Your Domains: Trajectory Optimality Criterion for Multi-Domain Learning
Comparing Implicit and Denoising Score-Matching Objectives
Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
Composing Global Optimizers to Reasoning Tasks via Algebraic Objects in Neural Nets
Continuous-Time Analysis of Adaptive Optimization and Normalization
Convergence of Distributed Adaptive Optimization with Local Updates
Convergence Properties of Hyperbolic Neural Networks on Riemannian Manifolds
Declarative characterizations of direct preference alignment algorithms
Depth Extrapolation of Decoders Trained on Nested Structures
Diffusion Model Learns Low-Dimensional Distributions via Subspace Clustering
Diffusion Models With Learned Adaptive Noise Processes
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers
Does Machine Bring in Extra Bias in Learning? Approximating Discrimination Within Models Quickly
Dynamics of Concept Learning and Compositional Generalization
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Exploring Task Affinities through NTK Alignment and Early Training Dynamics in Multi-Task Learning
Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency
Geometric Deep Learning with Quasiconformal Neural Networks: An Introduction
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift
Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
How do students become teachers: A dynamical analysis for two-layer neural networks
Implicit Bias of Adam versus Gradient Descent in One-Hidden-Layer Neural Networks
Improving the Gaussian Approximation in Neural Networks: Para-Gaussians and Edgeworth Expansions
In-Context Learning by Linear Attention: Exact Asymptotics and Experiments
Increasing Fairness via Combination with Learning Guarantees
Information-Theoretic Foundations for Neural Scaling Laws
Information-Theoretic Generalization Bounds for Batch Reinforcement Learning
Label Noise: Ignorance Is Bliss
Leveraging Intermediate Neural Collapse with Simplex ETFs for Efficient Deep Neural Networks
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
Misspecified $Q$ -Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Mixture of Parrots: Mixtures of experts improve memorization more than reasoning
On the Implicit Relation between Low-Rank Adaptation and Differential Privacy
On Your Mark, Get Set, Warmup!
Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection
Optimizing Fine-Tuning Efficiency: Gradient Subspace Tracking on Grassmann Manifolds for Large Language Models
Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
Progressive distillation induces an implicit curriculum
Provable unlearning in topic modeling and downstream tasks
Provable weak-to-strong generalization via benign overfitting
Robust Feature Learning for Multi-Index Models in High Dimensions
Sample compression unleashed : New generalization bounds for real valued losses
Self-Improvement in Language Models: The Sharpening Mechanism
SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Simple and Effective Masked Diffusion Language Models
The Crucial Role of Samplers in Online Direct Preference Optimization
The GAN is dead; long live the GAN! A Modern GAN Baseline
Towards characterizing the value of edge embeddings in Graph Neural Networks
Towards Principled Graph Transformers
Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study
Transformers are Efficient Compilers, Provably
Transformers Provably Solve Parity Efficiently with Chain of Thought
Understanding Diffusion-based Representation Learning via Low-Dimensional Modeling
Understanding Factual Recall in Transformers via Associative Memories
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues