Paper proposes a fair auto-encoder using hierarchical VampPrior and mutual information.
problem Learning fair representations to remove biases in decision-making.
method Hierarchical Variational Auto-Encoder with mutual information regularization.
result The approach either outperforms or performs on par with the current best model in experiments.
New framework combines imitation and reinforcement learning for faster, cheaper decision-making.
problem Sequential decision-making with sparse rewards and long time horizons.
method Hierarchical guidance framework integrating imitation and reinforcement learning at different levels.
result Significantly faster and more label-efficient learning compared to existing methods.
Unified framework integrates symbolic planning and HRL for robust decision-making.
problem Combining reinforcement learning and symbolic planning for robust decision-making in dynamic environments.
method Integrates symbolic planning with hierarchical reinforcement learning to guide task execution and improve planning.
result Unified framework leads to rapid policy search and robust symbolic plans in complex domains.
Framework improves resilience in operations through joint long-term and short-term decision-making.
problem Resilient operations in global markets require adaptive decision rules.
method Developed a two-timescale hierarchical reinforcement learning framework.
result Framework increases mean profit by 9.2% under joint demand-supply shocks and 11.8% under prolonged shocks.
HierLPR ranks labels hierarchically for multi-label classification, optimizing a new eAUC metric.
problem Hierarchical multi-label classification with emphasis on first call accuracy.
method Introduces HierLPR algorithm optimizing eAUC metric under tree constraint.
result HierLPR outperforms other methods in early precision-recall curve stages.
Model predicts crop yields to help farmers choose varieties that balance risk and reward.
problem Optimally selecting seed varieties to increase crop yield while managing risk.
method Hierarchical machine learning for yield prediction, integrated with weather forecasting, and decision-making models.
result Achieved a median absolute error of 3.74 bushels per acre in yield predictions.
A framework uses POMDPs to assess hierarchical clustering quality.
problem No agreed methodology for evaluating hierarchical clustering without ground-truth labels.
method Modelled as a POMDP, assessing search support for hierarchical structures.
result Proposes a novel measure for assessing hierarchical clustering quality.
Specialized hierarchies improve decision-making by partitioning tasks.
problem Division of labor and specialization in hierarchical systems.
method Information-theoretic on-line learning rule for partitioning tasks.
result Enforces specialization and optimal combination of experts.
Hierarchical RL system for robotic manipulation with explainable decision-making.
problem Interpretability of robot decision-making for human operators.
method Dot-to-Dot: Hierarchical Deep Reinforcement Learning.
result Efficient learning of complex actions/states by low-level agent and interpretable high-level representation.
A principle for specialized decision-making divides complex problems into manageable parts.
problem Complex decision-making problems beyond individual capabilities.
method An on-line learning rule that learns a partitioning of the problem space for specialized linear policies.
result The approach solves problems that exceed individual decision-makers' capabilities.
Simplifies neural networks into easier-to-explain decision trees.
problem Difficulty in explaining neural network decisions.
method Converts neural network into a soft decision tree.
result The resulting soft decision tree generalizes better.
AI learns to design chemical processes efficiently.
problem Designing efficient chemical processes.
method Hierarchical reinforcement learning and graph neural networks.
result Quick learning in various decision spaces.
New method optimizes hierarchical multi-label classification results.
problem Optimizing classification results respecting class hierarchy and classifier scores.
method Introducing CATCH objective function and mLPR metric to rank multi-label classification results.
result HierRank algorithm optimizes CATCH, improving decision accuracy.
This work proposes optimal decision rules for hierarchical classifiers to better align with evaluation metrics.
problem Heuristic decision rules in hierarchical classification do not align with evaluation metrics.
method Derives optimal decision rules for various prediction settings, focusing on hierarchical hFβ scores. result Optimal decision rules enhance the performance and reliability of hierarchical classifiers.
Hybrid Bayesian-conformal framework improves uncertainty quantification in healthcare predictions.
problem Jointly satisfying distribution-free coverage guarantees and risk-adaptive precision in clinical decision-making.
method Integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores.
result Achieves target coverage (94.3% vs 95% target) with adaptive precision, 21% narrower intervals for low-uncertainty cases.
A new reinforcement learning approach using competitive primitives that specialize and specialize based on information needs.
problem Complex environments require efficient and specialized decision-making.
method Decomposes policy into competitive primitives that decide based on information needs, regularized to use minimal information.
result Improves generalization over flat and hierarchical policies.
Develops a framework for decision-making abstractions under computational limitations.
problem Decision-making by agents with limited computational resources.
method Information-theoretic signal compression and optimization problem formulation.
result Generates a hierarchy of abstractions for a non-trivial environment.
The paper introduces a method to optimize decision-making with computational constraints.
problem Optimizing decision-making under computational limitations.
method Introduces a free-energy principle that considers both value and computational cost.
result Solves the variational equations using DP and rate distortion theory.
This paper learns actionable representations for reinforcement learning.
problem Learning comprehensive representations in reinforcement learning.
method Focuses on goal-conditioned policies to learn salient, actionable representations.
result Actionable representations improve exploration and hierarchical reinforcement learning.
HiPPO adapts skills and higher-level policies together for better transfer in hierarchical RL.
problem Sub-optimality in skill transfer when lower-level skills are fixed.
method HiPPO: a novel hierarchical policy gradient method that trains all levels of the hierarchy jointly.
result Improved robustness of skills to environment changes through training time-abstractions.
A method for fast, accurate cross-temporal forecasts using machine learning.
problem Inconsistent forecasts across different levels of platform data.
method Non-linear hierarchical forecast reconciliation using machine learning.
result Automated direct production of reconciled forecasts for high-frequency decision making.
Paper proposes a new method for predicting DER adoption with hierarchical guarantees.
problem Accurately predicting DER adoption in electric grids with uncertainty and spatial disparity.
method Multivariate Hawkes process for modeling DER adoption dynamics and split conformal prediction algorithm for hierarchical validity.
result Empirical evaluation shows superior predictive accuracy and uncertainty calibration compared to existing methods.
Study compares local and global models for hierarchical forecasting accuracy.
problem Challenges in hierarchical time series forecasting, especially in accuracy and information utilisation.
method Developed and evaluated local and global forecasting models (GFMs) to exploit cross-series and cross-hierarchies information.
result Global Forecasting Models (GFMs) outperform local models in hierarchical forecasting accuracy and computational efficiency.
New framework identifies worst-case shifts for predictive resource allocation models.
problem Identifying harmful shifts in predictive models for resource allocation.
method Hierarchical model structure and submodular optimization for worst-case loss.
result Empirical evidence shows divergent worst-case shifts identified by different metrics.
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or …
SplineNets improve CNNs by using continuous functions that can be conditioned on inputs.
problem Efficiency and accuracy trade-offs in CNNs.
method Continuous generalization of neural decision graphs using B-splines.
result SplineNets can significantly increase accuracy with minimal runtime cost.
New dropout method prevents overfitting in hierarchical models.
problem Overfitting in hierarchical mixture of experts.
method Variant of dropout applied hierarchically, not unitwise.
result Prevents overfitting and improves generalization in hierarchical models.
The paper develops a decision support system for hierarchical text classification of conference proceedings.
problem Classifying documents with a fixed hierarchical structure of topics.
method Developed a weighted hierarchical similarity function to calculate topic relevance, using entropy of words to estimate weights.
result The weighted hierarchical similarity function improves ranking accuracy compared to other methods.
Proposes mGBDTs for learning hierarchical representations in gradient boosting decision trees.
problem Inability of gradient boosting decision trees to learn hierarchical representations.
method Introduces multi-layered GBDT forest (mGBDTs) with explicit emphasis on hierarchical learning.
result Jointly trained mGBDTs can learn hierarchical representations effectively without backpropagation.
The study reveals decision trees' limitations in fitting data from additive models, proving a generalization lower bound.
problem Understanding the generalization performance of decision trees on additive models.
method Analyzing decision tree algorithms with sparse additive models, proving generalization lower bounds.
result Generalization lower bounds for decision trees on sparse additive models are much worse than minimax rates.
New model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
Hierarchical hidden Markov models predict market trends in financial time series.
problem Misinterpretation of short-term price fluctuations as long-term trend changes.
method Hierarchical hidden Markov models to capture both short- and long-term trends.
result Hierarchical models provide a comprehensive picture of financial markets.
Free Random Projection enhances reinforcement learning by naturally incorporating hierarchical structure.
problem Improving reinforcement learning algorithms for better generalization and adaptability.
method Introduces Free Random Projection, a method that uses free probability theory to create random orthogonal matrices encoding hierarchical structure.
result Empirically shows consistent improvement in generalization over standard methods on multi-environment benchmarks.
INVERT connects neural representations to human-understandable concepts.
problem Lack of understanding and statistical significance in existing explainability methods.
method Inverse Recognition (INVERT) approach that connects learned representations to human-understandable concepts.
result INVERT provides interpretable metrics and statistical significance for representation alignment.
Machine learning improves hierarchical forecasting of sales time series.
problem Hierarchical forecasting of sales time series is challenging due to dynamic changes.
method Used artificial neural networks, extreme gradient boosting, and support vector regression to disaggregate time series.
result Machine learning models outperform traditional methods in predicting sales time series with high volatility.
Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network. Even though this continuity …
Proposes efficient algorithm for system-level I&M decisions under uncertainty.
problem Optimal management strategies for deteriorating civil engineering systems.
method Factored partially observable Markov decision process with Bayesian networks and DDMAC reinforcement learning.
result DDMAC policies offer substantial benefits over heuristic approaches in system-level cost optimization.
A new hierarchical forecasting method using machine learning improves forecast accuracy.
problem Improving forecast accuracy in hierarchical forecasting systems.
method Non-linear combination of base forecasts, focusing on both accuracy and coherence.
result The proposed method outperforms existing approaches, especially for diverse series.
Improves RL planning by proposing sub-goals hierarchically.
problem Sequential planning assumption in RL.
method Divide-and-Conquer Monte Carlo Tree Search (DC-MCTS).
result Improves navigation and control tasks.
A novel framework combines LLMs and RL for financial portfolio optimization.
problem Optimizing financial portfolios using sentiment analysis and market indicators.
method Hierarchical RL structure with base, meta, and super-agents.
result Achieved a 26% annualized return and Sharpe ratio of 1.2.
Enhanced financial trading system using multi-agent LLMs with layered memory.
problem Inefficient prioritization of tasks in LLMs due to their memory processing.
method Introducing a multi-agent framework with layered memories and inter-agent debate.
result Superior automated trading accuracy and decision robustness.
A new method for solving complex sequential decision-making problems by decomposing them into multiple levels.
problem Sequential decision-making with natural multi-level structure.
method Multi-level meta-reinforcement learning with skill-based curriculum.
result Efficiently reduces stochasticity and policy search space, leading to fewer iterations and computations.
MPHD transfers knowledge across different domains for Bayesian optimization.
problem Optimizing functions with unknown or diverse domains.
method MPHD uses neural nets to map domain-specific contexts to GP specifications, enabling transfer learning across heterogeneous search spaces.
result MPHD improves black-box function optimization performance on diverse domains.
Bayesian model estimates feature values of premium products.
problem Understanding the monetary value of product features.
method Bayesian Hierarchical Logit Model
result Successfully recovers true feature valuations from data.
The paper predicts human-like driving behavior of other vehicles for safer AVs.
problem Safe and efficient interaction of AVs with other vehicles.
method Hierarchical inverse reinforcement learning considering both discrete and continuous decisions.
result The proposed approach accurately predicts both discrete and continuous driving behaviors.
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
BICauseTree improves causal effect estimation by identifying clusters and balancing treatment allocation.
problem Improving interpretability and transparency in causal effect models from observational data.
method Hierarchical bias-driven stratification using decision trees with a customized objective function.
result BICauseTree provides interpretable causal effect estimation and is comparable to existing methods.
Paper proposes a new decision strategy for open set recognition.
problem Existing OSR methods are limited in recognizing unknown classes and setting decision thresholds.
method Introduces a collective decision-based OSR framework (CD-OSR) using Hierarchical Dirichlet process (HDP).
result CD-OSR can simultaneously implement open set recognition and new class discovery.