Jacob R. Gardner
9 papers · 2023–2025 · 3 venues · across top CS/AI venues
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Achievements
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🌍
Conference Polyglot · Bronze
🌉
Interdisciplinary Bridge · Bronze
🗺️
Taxonomy Completionist
(22)
👑
Triple Crown
⚡
Prolific Year · Productive
Venues
Top co-authors
Research topics
Machine Learning
(9)
Optimization & Theory
(9)
Learning Types
(3)
Artificial Intelligence
(2)
Deep Learning
(2)
Mathematics & Optimization
(2)
Bayesian & Probabilistic · Machine Learning
(2)
Models
(2)
Optimization
(2)
Natural Language Processing
(1)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Core AI
(1)
Core Methods
(1)
Generation
(1)
Probability
(1)
Keywords
variational inference
(3)
black-box variational inference
(2)
large language model
(2)
gaussian process
(2)
transfer learning
(1)
hyperparameter optimization
(1)
few-shot learning
(1)
weight quantization
(1)
natural gradient
(1)
sequential monte carlo
(1)
hierarchical model
(1)
convergence
(1)
bayesian optimization
(1)
acquisition function
(1)
parameter-efficient fine-tuning
(1)
zeroth-order optimization
(1)
iteration complexity
(1)
random reshuffling
(1)
uncertainty quantification
(1)
model selection
(1)
Papers
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
ICML 2025
Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity
ICLR 2025
Approximation-Aware Bayesian Optimization
NeurIPS 2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
NeurIPS 2024
Demystifying SGD with Doubly Stochastic Gradients
ICML 2024
Learning Performance-Improving Code Edits
ICLR 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families
ICML 2024
Understanding Stochastic Natural Gradient Variational Inference
ICML 2024
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
ICML 2023