Antonio Orvieto
35 papers · 2019–2026 · 9 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+12 more ↓ Show less ↑
π Conference Polyglot (8) π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (11) π Interdisciplinary Bridge π Academic Marathon (6)
π
Academic Marathon
(6)
π
Cross-Pollinator
(13)
π€
Dynamic Duo
(16)
π¬
Deep Specialist
(13)
π§¬
Topic Evolution
β
The Questioner
β‘
Prolific Year
(8)
π
Conference Pioneer
π
Century Club
(34)
π
Trend Setter
ποΈ
Keyword Collector
(128)
π₯
Unstoppable
(7)
Conferences
NIPS (12)
ICML (8)
AISTATS (7)
ICLR (3)
ACL (1)
COLT (1)
CVPR (1)
EMNLP (1)
UAI (1)
Top co-authors
Research topics
Keywords
gradient descent
(6)
stochastic optimization
(5)
stochastic gradient descent
(5)
convex optimization
(3)
stochastic differential equation
(3)
ito calculus
(2)
gradient descent ascent
(2)
minimax optimization
(2)
non-convex optimization
(2)
recurrent neural network
(2)
noise injection
(2)
saddle point
(2)
variance reduction
(2)
vanishing gradient
(2)
convergence analysis
(2)
state-space model
(2)
ordinary differential equation
(2)
multimodal learning
(1)
in-context learning
(1)
catastrophic forgetting
(1)
Papers
GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
ACL 2026
Generalized Interpolating Discrete Diffusion
ICML 2025
Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture
ICLR 2025
When, Where and Why to Average Weights?
ICML 2025
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
ICLR 2025
(Almost) Free Modality Stitching of Foundation Models
EMNLP 2025
An uncertainty principle for Linear Recurrent Neural Networks
COLT 2025
Recurrent neural networks: vanishing and exploding gradients are not the end of the story
NIPS 2024
Loss Landscape Characterization of Neural Networks without Over-Parametrization
NIPS 2024
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
ICML 2024
Recurrent Distance Filtering for Graph Representation Learning
ICML 2024
SDEs for Minimax Optimization
AISTATS 2024
Super Consistency of Neural Network Landscapes and Learning Rate Transfer
NIPS 2024
Theoretical Foundations of Deep Selective State-Space Models
NIPS 2024
Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
NIPS 2024
Explicit Regularization in Overparametrized Models via Noise Injection
AISTATS 2023
Achieving a Better Stability-Plasticity Trade-Off via Auxiliary Networks in Continual Learning
CVPR 2023
An SDE for Modeling SAM: Theory and Insights
ICML 2023
Resurrecting Recurrent Neural Networks for Long Sequences
ICML 2023
Vanishing Curvature in Randomly Initialized Deep ReLU Networks
AISTATS 2022
Anticorrelated Noise Injection for Improved Generalization
ICML 2022
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
NIPS 2022
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution
NIPS 2022
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
AISTATS 2022
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse
NIPS 2022
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization
AISTATS 2021
Rethinking the Variational Interpretation of Accelerated Optimization Methods
NIPS 2021
Momentum Improves Optimization on Riemannian Manifolds
AISTATS 2021
Learning explanations that are hard to vary
ICLR 2021
On the Second-order Convergence Properties of Random Search Methods
NIPS 2021
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
AISTATS 2020
An Accelerated DFO Algorithm for Finite-sum Convex Functions
ICML 2020
The Role of Memory in Stochastic Optimization
UAI 2019
Shadowing Properties of Optimization Algorithms
NIPS 2019
Continuous-time Models for Stochastic Optimization Algorithms
NIPS 2019