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Found 21 papers, 6 papers with code
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering
Particle filtering is a Bayesian inference method and a fundamental tool in state estimation for dynamic systems, but its effectiveness is often limited by the constraints of the initial prior distribution, a phenomenon we define as the Prior Boundary Phenomenon.
Attention-Driven Hierarchical Reinforcement Learning with Particle Filtering for Source Localization in Dynamic Fields
In many real-world scenarios, such as gas leak detection or environmental pollutant tracking, solving the Inverse Source Localization and Characterization problem involves navigating complex, dynamic fields with sparse and noisy observations.
When Can Proxies Improve the Sample Complexity of Preference Learning?
In these cases, it is often unclear whether the addition of proxy data can improve policy learning.
Natural Language Reinforcement Learning
Reinforcement Learning (RL) mathematically formulates decision-making with Markov Decision Process (MDP).
Causal Representation Learning from Multimodal Biomedical Observations
Theoretically, we consider a nonparametric latent distribution (c. f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities.
Efficient Reinforcement Learning with Large Language Model Priors
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases.
Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
We address these challenges by first conceptualizing multimodal representations as comprising modality-invariant and modality-specific components.
Attaining Human`s Desirable Outcomes in Human-AI Interaction via Structural Causal Games
In human-AI interaction, a prominent goal is to attain human`s desirable outcome with the assistance of AI agents, which can be ideally delineated as a problem of seeking the optimal Nash Equilibrium that matches the human`s desirable outcome.
Natural Language Reinforcement Learning
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
Subsequently, we minimize the mutual information between the observation estimation and the relevance estimation conditioned on the input features.
Specify Robust Causal Representation from Mixed Observations
Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.
Invariant Learning via Probability of Sufficient and Necessary Causes
To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases.
ChessGPT: Bridging Policy Learning and Language Modeling
Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.
Generalizable Information Theoretic Causal Representation
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems.
Debiased Recommendation with User Feature Balancing
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
Informative Robust Causal Representation for Generalizable Deep Learning
In many real-world scenarios, such as image classification and recommender systems, it is evidence that representation learning can improve model's performance over multiple downstream tasks.
Top-N Recommendation with Counterfactual User Preference Simulation
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
The capability of imagining internally with a mental model of the world is vitally important for human cognition.
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
