Shusen Wang
29 papers · 2012–2024 · 10 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (14) π Interdisciplinary Bridge π Renaissance Researcher (5) π£ Hot Topic Early Bird
π
Interdisciplinary Bridge
π£
Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(14)
π€
Dynamic Duo
(12)
π
Keyword Champion
(2)
π
Grand Slam
π
Century Club
(29)
ποΈ
Keyword Collector
(118)
π
Conference Pioneer
β‘
Prolific Year
(5)
π₯
Unstoppable
(13)
β
The Questioner
(2)
π
Trend Setter
Conferences
JMLR (7)
ICML (5)
AAAI (3)
EMNLP (3)
IJCAI (3)
AISTATS (2)
NAACL (2)
NIPS (2)
COLING (1)
ICLR (1)
Top co-authors
Research topics
Keywords
matrix approximation
(4)
nystrom method
(4)
low-rank approximation
(3)
empirical risk minimization
(3)
relative-error bound
(3)
nystrom approximation
(3)
large language model
(3)
ridge regression
(3)
cur matrix decomposition
(3)
randomized algorithm
(2)
matrix decomposition
(2)
nystrΓΆm method
(2)
relation classification
(2)
matrix sketching
(2)
communication efficiency
(2)
convex optimization
(2)
model averaging
(2)
instruction tuning
(2)
dimensionality reduction
(2)
distributed optimization
(2)
Papers
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
EMNLP 2024
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
AAAI 2024
RefGPT: Dialogue Generation of GPT, by GPT, and for GPT
EMNLP 2023
2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition
EMNLP 2023
Learning by Interpreting
IJCAI 2022
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
NAACL 2022
Federated Reinforcement Learning with Environment Heterogeneity
AISTATS 2022
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction
COLING 2022
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
NAACL 2022
Communication-Efficient Distributed SVD via Local Power Iterations
ICML 2021
Matrix Sketching for Secure Collaborative Machine Learning
ICML 2021
On the Convergence of FedAvg on Non-IID Data
ICLR 2020
Do Subsampled Newton Methods Work for High-Dimensional Data?
AAAI 2020
A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression
AAAI 2019
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication
JMLR 2019
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
JMLR 2019
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
ICML 2018
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
NIPS 2018
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
JMLR 2018
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
ICML 2017
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition
JMLR 2016
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions
JMLR 2016
Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach
IJCAI 2015
Efficient Algorithms and Error Analysis for the Modified Nystrom Method
AISTATS 2014
Making Fisher Discriminant Analysis Scalable
ICML 2014
Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling
JMLR 2013
Nonconvex Relaxation Approaches to Robust Matrix Recovery
IJCAI 2013
A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
NIPS 2012
EP-GIG Priors and Applications in Bayesian Sparse Learning
JMLR 2012