Guannan Zhang
12 papers · 2019–2026 · 11 venues · across top CS/AI venues
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Machine Learning
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Learning Types Β· Machine Learning
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(3)
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(2)
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large language model
(3)
model compression
(2)
mixture of expert
(2)
neural network
(2)
multi-task learning
(1)
model selection
(1)
dimensionality reduction
(1)
function approximation
(1)
catastrophic forgetting
(1)
uncertainty quantification
(1)
knowledge distillation
(1)
data augmentation
(1)
text generation
(1)
hierarchical text classification
(1)
mutual information
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variational inference
(1)
global optimization
(1)
bayesian experimental design
(1)
nonlinear transformation
(1)
incremental learning
(1)
Papers
PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents
ACL 2026
MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts
AAAI 2024
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning
EMNLP 2024
Towards Efficient Replay in Federated Incremental Learning
CVPR 2024
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model
COLING 2024
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
IJCAI 2023
Towards Better Hierarchical Text Classification with Data Generation
ACL 2023
Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack
ECCV 2022
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
ICLR 2022
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models
AISTATS 2021
Enabling long-range exploration in minimization of multimodal functions
UAI 2021
Learning nonlinear level sets for dimensionality reduction in function approximation
NeurIPS 2019