Joseph E Gonzalez
25 papers · 2013–2024 · 4 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (11) π£ Hot Topic Early Bird
π
Academic Marathon
(11)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Conference Loyalist
(20)
π§¬
Topic Evolution
ποΈ
Keyword Collector
(125)
β‘
Prolific Year
(9)
π
Century Club
(25)
π₯
Unstoppable
(6)
π
Trend Setter
β
The Questioner
Conferences
NIPS (20)
CORL (3)
ECCV (1)
JMLR (1)
Top co-authors
Research topics
Keywords
large language model
(4)
reinforcement learning
(2)
multi-agent system
(2)
sparse reward
(2)
data augmentation
(2)
prompt engineering
(2)
diffusion model
(2)
neural network
(2)
sample efficiency
(1)
domain generalization
(1)
double descent
(1)
path planning
(1)
black-box optimization
(1)
graph classification
(1)
deep reinforcement learning
(1)
imitation learning
(1)
trajectory prediction
(1)
image generation
(1)
distributed learning
(1)
code generation
(1)
Papers
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
ECCV 2024
Stylus: Automatic Adapter Selection for Diffusion Models
NIPS 2024
SGLang: Efficient Execution of Structured Language Model Programs
NIPS 2024
Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages
NIPS 2024
Gorilla: Large Language Model Connected with Massive APIs
NIPS 2024
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
NIPS 2024
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
JMLR 2023
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
NIPS 2023
Diversify Your Vision Datasets with Automatic Diffusion-based Augmentation
NIPS 2023
Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty
CORL 2022
NovelD: A Simple yet Effective Exploration Criterion
NIPS 2021
Learning Space Partitions for Path Planning
NIPS 2021
Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL
NIPS 2021
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
NIPS 2021
MADE: Exploration via Maximizing Deviation from Explored Regions
NIPS 2021
Representing Long-Range Context for Graph Neural Networks with Global Attention
NIPS 2021
Taxonomizing local versus global structure in neural network loss landscapes
NIPS 2021
Accelerating Quadratic Optimization with Reinforcement Learning
NIPS 2021
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
CORL 2021
Boundary thickness and robustness in learning models
NIPS 2020
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
NIPS 2020
On-Policy Robot Imitation Learning from a Converging Supervisor
CORL 2019
ANODEV2: A Coupled Neural ODE Framework
NIPS 2019
Parallel Double Greedy Submodular Maximization
NIPS 2014
Optimistic Concurrency Control for Distributed Unsupervised Learning
NIPS 2013