Search Results for author:
Found 24 papers, 17 papers with code
PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
Despite notable advancements in Retrieval-Augmented Generation (RAG) systems that expand large language model (LLM) capabilities through external retrieval, these systems often struggle to meet the complex and diverse needs of real-world industrial applications.
Monte Carlo Tree Search based Space Transfer for Black-box Optimization
MCTS-transfer can not only provide a well-performing search space for warm-start but also adaptively identify and leverage the information of similar source tasks to reconstruct the search space during the optimization process.
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences.
Enhancing Cross-domain Pre-Trained Decision Transformers with Adaptive Attention
Although this cross-domain pre-training approach achieves superior performance compared to training from scratch in environments required short-term planning ability, the mechanisms by which pre-training benefits the fine-tuning phase remain unclear.
Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
The combination of photometric augmentation, TFL loss, and MC-dropout produced the best results, although dropout alone led to more false negatives in subsequent year predictions.
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses.
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
A graph neural network (GNN) is then leveraged to exploit the relationships between passages and improve the retrieval of supporting passages.
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edge being part of the optimal solution, to guide MCTS in solution finding.
TimeX++: Learning Time-Series Explanations with Information Bottleneck
The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues.
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Reinforced In-Context Black-Box Optimization
In this paper, we propose RIBBO, a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective.
Pre-Trained Large Language Models for Industrial Control
2)~How well can GPT-4 generalize to different scenarios for HVAC control?
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management
Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment.
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time.
RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration
Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows.
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP).
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+1
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology.
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios.
Symmetric Transformer-based Network for Unsupervised Image Registration
Medical image registration is a fundamental and critical task in medical image analysis.
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management
We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.
