Ke Tang
33 papers · 2015–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (9) π£ Hot Topic Early Bird π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (10)
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Hot Topic Early Bird
π
Cross-Pollinator
(9)
πΊοΈ
Taxonomy Completionist
(50)
π€
Dynamic Duo
(11)
π
Grand Slam
π¬
Deep Specialist
(12)
π
Century Club
(33)
π
Conference Pioneer
ποΈ
Keyword Collector
(132)
β‘
Prolific Year
(8)
π₯
Unstoppable
(11)
Conferences
IJCAI (13)
AAAI (6)
NIPS (6)
ICML (3)
ACL (1)
CVPR (1)
ICCV (1)
ICLR (1)
JMLR (1)
Top co-authors
Keywords
subset selection
(4)
greedy algorithm
(3)
combinatorial optimization
(3)
model compression
(3)
pareto optimization
(3)
submodular function
(3)
adversarial robustness
(3)
approximation guarantee
(2)
deep neural network
(2)
stochastic gradient descent
(2)
stochastic optimization
(2)
generalization bound
(2)
multi-objective optimization
(2)
robustness evaluation
(2)
reconstruction error
(2)
mirror descent
(2)
submodular optimization
(2)
adversarial attack
(2)
approximation algorithm
(2)
approximation ratio
(2)
Papers
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
AAAI 2025
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets
ICML 2025
Approximation to Smooth Functions by Low-Rank Swish Networks
ICML 2025
SOO-Bench: Benchmarks for Evaluating the Stability of Offline Black-Box Optimization
ICLR 2025
Mitigating Catastrophic Overfitting in Fast Adversarial Training via Label Information Elimination
ICCV 2025
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
CVPR 2025
Efficient Robustness Evaluation via Constraint Relaxation
AAAI 2025
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
ICML 2024
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
ACL 2024
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles
AAAI 2023
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning
NIPS 2022
Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions
JMLR 2021
Towards Robust Dynamic Network Embedding
IJCAI 2021
On Performance Estimation in Automatic Algorithm Configuration
AAAI 2020
Automatic Construction of Parallel Portfolios via Explicit Instance Grouping
AAAI 2019
Explicit Planning for Efficient Exploration in Reinforcement Learning
NIPS 2019
Optimal Stochastic and Online Learning with Individual Iterates
NIPS 2019
Unsupervised Feature Selection by Pareto Optimization
AAAI 2019
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
NIPS 2018
A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms
IJCAI 2018
Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection
IJCAI 2018
Sequence Selection by Pareto Optimization
IJCAI 2018
Distributed Pareto Optimization for Subset Selection
IJCAI 2018
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
IJCAI 2018
Generalization Bounds for Regularized Pairwise Learning
IJCAI 2018
Optimization based Layer-wise Magnitude-based Pruning for DNN Compression
IJCAI 2018
Optimizing Ratio of Monotone Set Functions
IJCAI 2017
On Subset Selection with General Cost Constraints
IJCAI 2017
Subset Selection under Noise
NIPS 2017
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
NIPS 2017
Parallel Pareto Optimization for Subset Selection
IJCAI 2016
Increasingly Cautious Optimism for Practical PAC-MDP Exploration
IJCAI 2015
Active Learning from Crowds with Unsure Option
IJCAI 2015