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Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study Adaptive Tuning of Parameterized Traffic Controllers via Multi-Agent Reinforcement Learning Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories PhysInOne: Visual Physics Learning and Reasoning in One Suite Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image 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Practical Perspective A Mathematical Framework for Temporal Modeling and Counterfactual Policy Simulation of Student Dropout Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis Post-Hoc Guidance for Consistency Models by Joint Flow Distribution Learning SenBen: Sensitive Scene Graphs for Explainable Content Moderation R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII Policy-Aware Design of Large-Scale Factorial Experiments $p1$: Better Prompt Optimization with Fewer Prompts A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition Unified Multimodal Uncertain Inference EfficientSign: An Attention-Enhanced Lightweight Architecture for Indian Sign Language Recognition Skip-Connected Policy Optimization for Implicit Advantage PRAGMA: Revolut Foundation Model 3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning Generative 3D Gaussian Splatting for Arbitrary-ResolutionAtmospheric Downscaling and Forecasting From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach Semantic Intent Fragmentation: A Single-Shot Compositional Attack on Multi-Agent AI Pipelines Joint Interference Detection and Identification via Adversarial Multi-task Learning HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models R3PM-Net: Real-time, Robust, Real-world Point Matching Network From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs Act or Escalate? Evaluating Escalation Behavior in Automation with Language Models Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment Structured Exploration and Exploitation of Label Functions for Automated Data Annotation MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention Reinforcement-aware Knowledge Distillation for LLM Reasoning SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework A Horizon-Aware Decision-Support Framework for Demand Forecasting Model Selection in Resilient Production Planning Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers Multi-agent Adaptive Mechanism Design When & How to Write for Personalized Demand-aware Query Rewriting in Video Search Relational Visual Similarity From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting OmniPrism: Learning Disentangled Visual Concept for Image Generation FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening
Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal
Leah Chrestien, Tomás Pevný, Stefan Edelkamp, Antonín Komenda · 2023-10-30 · via cs.LG updates on arXiv.org

In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient conditions of strictly optimally efficient heuristics for forward search algorithms, mainly A* and greedy best-first search, which expand only states on the returned optimal path. It then proposes a family of loss functions based on ranking tailored for a given variant of the forward search algorithm. Furthermore, from a learning theory point of view, it discusses why optimizing cost-to-goal \hstar\ is unnecessarily difficult. The experimental comparison on a diverse set of problems unequivocally supports the derived theory.

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