Co-occurring keywords · papers mentioning both
Papers
Cubing for Tuning
AAAI 2026
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
AAAI 2025
Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning
ICML 2025
DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem
AAAI 2025
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
ICML 2025
Accelerating Non-Maximum Suppression: A Graph Theory Perspective
NeurIPS 2024
A Divide and Conquer Approach for Solving Structural Causal Models
PGM 2024
Control Large Language Models via Divide and Conquer
EMNLP 2024
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
NeurIPS 2024
Divide-Conquer-Reasoning for Consistency Evaluation and Automatic Improvement of Large Language Models
EMNLP 2024
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
ICML 2024
Embarrassingly Parallel GFlowNets
ICML 2024
Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences
ICML 2024
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
JMLR 2024
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
NeurIPS 2024
Distributed Algorithms for U-statistics-based Empirical Risk Minimization
JMLR 2023
Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time
ICLR 2023
Least Squares Model Averaging for Distributed Data
JMLR 2023
MathNAS: If Blocks Have a Role in Mathematical Architecture Design
NeurIPS 2023
Variance-Aware Sparse Linear Bandits
ICLR 2023
A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems
AAAI 2022
Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions
JMLR 2022
Learning Logic Programs Though Divide, Constrain, and Conquer
AAAI 2022
Divide and Conquer for Single-Frame Temporal Action Localization
ICCV 2021
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
ICLR 2021