Yuekai Sun
41 papers · 2012–2025 · 6 conferences · across top CS/AI conferences
Achievements
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Interdisciplinary Bridge
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(17)
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Hot Topic Early Bird
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Keyword Trendsetter Combo
(3)
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Keyword Champion
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Triple Crown
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Deep Specialist
(11)
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Dynamic Duo
(25)
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(7)
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(137)
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Century Club
(41)
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(8)
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Trend Setter
Conferences
ICLR (14)
NIPS (13)
ICML (6)
AISTATS (4)
JMLR (3)
EMNLP (1)
Top co-authors
Research topics
Keywords
algorithmic fairness
(4)
individual fairness
(4)
high-dimensional regression
(4)
distribution shift
(3)
sparse regression
(3)
convex optimization
(3)
domain adaptation
(3)
domain generalization
(2)
statistical inference
(2)
probabilistic model
(2)
communication efficiency
(2)
distributed learning
(2)
high-dimensional statistics
(2)
large language model
(2)
graph laplacian
(1)
benchmark evaluation
(1)
variable selection
(1)
image classification
(1)
stochastic optimization
(1)
model selection
(1)
Papers
LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content
ICLR 2025
Learning the Distribution Map in Reverse Causal Performative Prediction
AISTATS 2025
Microfoundation inference for strategic prediction
AISTATS 2025
A transfer learning framework for weak to strong generalization
ICLR 2025
Aligners: Decoupling LLMs and Alignment
EMNLP 2024
Learning in reverse causal strategic environments with ramifications on two sided markets
ICLR 2024
An Investigation of Representation and Allocation Harms in Contrastive Learning
ICLR 2024
tinyBenchmarks: evaluating LLMs with fewer examples
ICML 2024
Efficient multi-prompt evaluation of LLMs
NIPS 2024
Distributionally Robust Performative Prediction
NIPS 2024
Weak Supervision Performance Evaluation via Partial Identification
NIPS 2024
Fusing Models with Complementary Expertise
ICLR 2024
Understanding new tasks through the lens of training data via exponential tilting
ICLR 2023
Simple Disentanglement of Style and Content in Visual Representations
ICML 2023
Conditional independence testing under misspecified inductive biases
NIPS 2023
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
ICLR 2023
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
ICLR 2023
Minimax optimal approaches to the label shift problem in non-parametric settings
JMLR 2022
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
NIPS 2022
Domain Adaptation meets Individual Fairness. And they get along.
NIPS 2022
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
JMLR 2022
Statistical inference for individual fairness
ICLR 2021
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
ICLR 2021
Individually Fair Gradient Boosting
ICLR 2021
Post-processing for Individual Fairness
NIPS 2021
Does enforcing fairness mitigate biases caused by subpopulation shift?
NIPS 2021
On sensitivity of meta-learning to support data
NIPS 2021
Outlier-Robust Optimal Transport
ICML 2021
Individually Fair Rankings
ICLR 2021
Auditing ML Models for Individual Bias and Unfairness
AISTATS 2020
Two Simple Ways to Learn Individual Fairness Metrics from Data
ICML 2020
Training individually fair ML models with sensitive subspace robustness
ICLR 2020
Federated Learning with Matched Averaging
ICLR 2020
Dirichlet Simplex Nest and Geometric Inference
ICML 2019
Precision Matrix Estimation with Noisy and Missing Data
AISTATS 2019
Communication-efficient Sparse Regression
JMLR 2017
Feature-distributed sparse regression: a screen-and-clean approach
NIPS 2016
Evaluating the statistical significance of biclusters
NIPS 2015
Learning Mixtures of Linear Classifiers
ICML 2014
On model selection consistency of penalized M-estimators: a geometric theory
NIPS 2013
Proximal Newton-type methods for convex optimization
NIPS 2012