New method tackles noisy and incomplete observations in reinforcement learning.
problem Noisy and incomplete observations in reinforcement learning with continuous control.
method Model-based approach using surrogate loss function and belief imputation.
result The method outperforms compared methods on benchmark tasks.
DVRL learns a generative model for partially observable environments.
problem Learning in partially observable environments with unknown models.
method Introduces a deep variational approach to learn a generative model and perform inference.
result DVRL outperforms previous methods in partially observable environments.
New method discovers causal structures from incomplete data.
problem Discovering causal structure from incomplete data.
method Encoder and reinforcement learning integrated approach.
result Our method outperforms existing methods by 43.2%.
New method for IRL with missing data.
problem Recovering reward function with missing data.
method Direct computation of log-likelihood with linear equations.
result Efficient handling of missing segments in trajectories.
Paper proposes NAC for efficient network discovery in incomplete networks.
problem Efficiently discover vertices with specific attributes in incomplete networks.
method Formulates network discovery as a reinforcement learning problem, uses deep reinforcement learning with task-specific network embeddings.
result Offline planning leads to significantly improved performance compared to online discovery algorithms.
This paper tackles sample-efficient reinforcement learning for partially observable Markov games.
problem Learning in partially observable Markov games with incomplete information.
method A simple algorithm combining optimism and Maximum Likelihood Estimation (MLE) for self-play, and a variant of optimistic MLE for adversarial opponents.
result The proposed algorithms achieve approximate Nash, correlated, and coarse correlated equilibria in polynomial samples for weakly revealing POMGs.
This survey categorizes IRL challenges and methods.
problem Inferring reward functions from observed behavior.
method Categorizes and discusses existing IRL methods.
result Challenges and solutions in IRL.
This paper optimizes tracking portfolios in incomplete markets using reinforcement learning.
problem Optimizing tracking portfolios in incomplete markets with capital injection.
method Reinforcement learning approach for optimal control in reflected diffusion processes.
result Satisfactory performance of the q-learning algorithm in numerical examples.
Accumulator module improves reinforcement learning by delaying decisions based on evidence.
problem Incomplete information, limited sensing, and stochastic environments lead to risky decisions.
method Integrates evidence for each action, delays action until confident, using dynamic competition.
result Accumulator module outperforms traditional reinforcement learning methods in a guessing game.
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missi…
Enhances RL in partially observable, noisy environments by uncovering causal states.
problem Making decisions based on incomplete and noisy observations in partially observable Markov decision processes (P2OMDPs). method Causal State Representation under Asynchronous Diffusion Model (CaDiff) framework, incorporating a novel asynchronous diffusion model (ADM) and a new bisimulation metric.
result Enhances returns by at least 14.18% compared to baselines on Roboschool tasks.
PEARL uses reinforcement learning to improve matrix preconditioners.
problem Learning effective preconditioners for iterative solvers is challenging.
method PEARL employs an actor-critic reinforcement learning framework to learn preconditioners dynamically.
result PEARL outperforms traditional and neural preconditioners in flexibility and solving speed.
This paper detects Markov violations in RL with noise, improving policy development.
problem Partial observability and sensor/actuator noise invalidate Markovian assumptions in RL.
method Combines PCMCI causal discovery with Markov Violation score (MVS).
result Even substantial noise doesn't always disrupt multi-step dependencies.
Algorithm recovers sparse PCA support from incomplete data.
problem Sparse PCA with incomplete and noisy data.
method Semidefinite program (SDP) relaxation of non-convex l1-regularized PCA. result SDP enables exact recovery of true support of sparse leading eigenvector.
Bayesian networks handle incomplete and changing data.
problem Handling incomplete and dynamic data.
method Review and application of Bayesian networks to incomplete and dynamic data.
result Bayesian networks can model dynamic and incomplete data.
A new criterion HBIC improves model selection for factor analysis with missing data.
problem Model selection for factor analysis with incomplete data.
method Proposes a novel criterion HBIC that uses actual observed information in the penalty term.
result HBIC is more accurate than BIC when missing data rates are high.
New algorithms learn sparse dictionaries from incomplete data.
problem Learning dictionaries from incomplete data.
method Iterative descent algorithm with initialization using extra samples.
result Provable polynomial-time algorithms for dictionary learning from incomplete data.
A new method for state estimation in state-space models using incomplete data.
problem State estimation in nonlinear state-space models with incomplete observations.
method Statistical analysis of incomplete observations, score function, observed information matrices, EM-gradient-particle filtering.
result Maximum likelihood estimation of state-vector with explicit form of observed information matrix.
This research tackles balancing exploration and exploitation in deep RL for partially observable systems.
problem Balancing exploration and exploitation in deep RL for partially observable systems.
method Deployed and tested several techniques including adaptive and deterministic exploration strategies, and a modified quadratic loss function.
result Adaptive methods better approximate the trade-off between exploration and exploitation.
We analyze incomplete ranking data, modeling coarsening and studying rank aggregation methods.
problem Statistical inference for incomplete ranking data, especially under rank-dependent coarsening.
method Modeling rank-dependent coarsening, studying Plackett-Luce distribution, and analyzing rank aggregation methods.
result The ability to recover a target ranking from incomplete observations, despite coarsening bias, is theoretically addressed.
Agents learning to act autonomously in real-world domains must acquire a model of the dynamics of the domain in which they operate. Learning domain dynamics can be challenging, especially where an agent only has partial access to the world state, and/or noisy external sensors. Even in standard STRIPS domains, existing …
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
problem Learning from incomplete graphs with missing node attributes.
method Introduces PaGNNs with novel partial aggregation functions for incomplete graph data.
result Demonstrates effectiveness and efficiency of PaGNNs on various datasets.
Robots learn tasks from a single demonstration using auxiliary video context.
problem Learning from demonstrations is challenging due to ambiguity and lack of labeled data.
method Metalearning to localize actions in auxiliary videos, learning reward functions, and reinforcement learning.
result Robots can learn multi-step tasks more effectively with auxiliary video context.
Analysis of three subspace estimation algorithms under incomplete data.
problem Estimating subspace from incomplete observations in high dimensions.
method High-dimensional analysis of Oja's method, GROUSE, and PETRELS.
result The time-varying principal angles converge weakly to deterministic processes with proper time scaling.
Improved VAE estimation from incomplete data using variational mixtures.
problem Estimating VAEs from incomplete data increases posterior complexity.
method Introducing variational mixtures based on finite and imputation distributions.
result Variational mixtures improve VAE estimation accuracy from incomplete data.
MissNODAG learns cyclic causal graphs from incomplete data.
problem Causal discovery in systems with feedback loops and missing data.
method Differentiable framework integrating additive noise model and expectation-maximization.
result MissNODAG uncovers cyclic structures and missingness mechanisms from partially observed data.
This work tackles maintenance planning with deep reinforcement learning under uncertainty.
problem Optimizing inspection and maintenance policies in deteriorating environments with incomplete information and constraints.
method Joint framework of constrained POMDPs and multi-agent DRL addressing challenges of state/action space, history, uncertainty, and constraints.
result The proposed framework outperforms existing methods in resource and risk-aware decision-making.
Estimates network structure from incomplete event data.
problem Estimating network structure from incomplete event data.
method Developed a novel approach using an unbiased estimator of the complete data log-likelihood function.
result Proposed a computationally efficient estimation algorithm.
Improved GAN performance with incomplete data using factorised discriminators.
problem Limited availability of labelled data for GAN training.
method Factorising data distribution into sub-distributions and training sub-discriminators.
result Improved performance in image generation, segmentation, and audio separation tasks.
In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al., 1993). This condition need not hold in complex environments, such as the incomplete-information equili…
Extends PD-NJ-ODE to noisy observations and dependent observation times.
problem Predicting continuous-time stochastic processes with irregular and noisy observations.
method Extends PD-NJ-ODE to handle conditional independence and noisy observations.
result Theoretical guarantees and empirical examples for handling noisy observations and dependent observation times.
Wasserstein dependency measure improves unsupervised representation learning.
problem Incomplete representations from mutual information maximization.
method Wasserstein dependency measure using Wasserstein distance instead of KL divergence.
result Improved results on tasks with high mutual information.
MUSIC learns coupled systems with sparse data and incomplete physics.
problem Learning coupled systems with incomplete physical constraints and missing data.
method Sparsity induced multitask neural network framework integrating partial physical constraints with data-driven learning.
result MUSIC accurately learns solutions to complex coupled systems under data-scarce and noisy conditions.
The paper explores how to learn from incomplete online social networks.
problem Learning from partially observed networks via node querying.
method Developed algorithms NOL* for sequential node querying to maximize network observability.
result It is possible to sequentially learn which nodes to query for maximal network observability.
GANs learn from incomplete data with missing data imputation.
problem Learning from incomplete data with GANs.
method Proposes a GAN framework with a complete data generator and a mask generator to model missing data.
result Demonstrates effective imputation of missing data using adversarial training.
Study on tensor signal estimation from incomplete data.
problem Estimating a rank-one tensor signal from noisy, incomplete data.
method Reduction to random matrix model for spectral analysis.
result Loss of performance due to incomplete data.
evo-RL combines evolutionary computation with reinforcement learning for better adaptability.
problem Improving reinforcement learning algorithms' adaptability and performance in environments with rewardless states.
method Embedding reinforcement learning in an evolutionary cycle, distinguishing instinctive from learnable behavior.
result evo-RL leads to state-of-the-art performance on OpenAI Gym control problems with rewardless states.
Paper improves ML estimation from incomplete data with robust M-estimator.
problem Estimating parameters from incomplete data with improved accuracy.
method Developed a robust M-estimator and a sandwich estimator for standard errors.
result Improved estimation accuracy with smaller standard errors than ML estimates.
Proposes a novel framework for citywide traffic volume inference using GPS and camera data.
problem Inaccurate traffic volume inference due to sparse GPS data and dynamic traffic conditions.
method Combines GPS and camera data, uses a simulator and reinforcement learning to recover missing data, constructs spatiotemporal graphs for inference.
result Effective citywide traffic volume inference validated by experiments.
New RBM method for missing data inference, comparing performance to existing methods.
problem Missing data inference and perception-distortion trade-off.
method Linearization of RBM effective energy function for missing data.
result Proposed method outperforms existing reconstruction procedures in missing data inference.
Proposes a criterion for selecting relevant auxiliary variables in incomplete data analysis.
problem Selecting useful auxiliary variables for incomplete data analysis.
method Formulates model selection problem, proposes an information criterion based on Kullback-Leibler divergence.
result Proposed information criterion is an asymptotically unbiased estimator of Kullback-Leibler divergence.
New method for tensor classification with missing data.
problem Handling incomplete tensor data in high-dimensional classification.
method High-dimensional tensor linear discriminant analysis with TGMM and Tensor LDA-MD.
result Established convergence rates and minimax optimal bounds for misclassification rate.
New algorithm improves reinforcement learning from partial observations.
problem Inferior performance of algorithms in real-world reinforcement learning due to partial observability.
method Representation-based approach to POMDPs, leading to a tractable algorithm.
result Empirically demonstrates superior performance with partial observations.
New method estimates model parameters from incomplete data.
problem Estimating model parameters from incomplete data.
method Variational Gibbs Inference (VGI)
result Competitive or better performance compared to existing methods.
Merlin improves robustness of MTSF models to missing data.
problem Suboptimal forecasting performance due to unfixed missing rates in MTSF models.
method Offline knowledge distillation and multi-view contrastive learning.
result Merlin enhances robustness of MTSF models while preserving accuracy.
Develops a statistical model for SOFR term structure in incomplete markets.
problem Incomplete liquidity and completeness in SOFR derivatives market.
method Statistical model incorporating macroeconomic factors and jumps in SOFR rates.
result Model is well-suited for risk management and derivatives pricing.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
This paper improves learning uncertain Bayesian networks from incomplete data.
problem Learning conditional probabilities in Bayesian networks with limited data.
method Develops methods to estimate and quantify uncertainty in conditional probabilities with incomplete data.
result Improves state-of-the-art approaches for handling uncertain Bayesian networks with incomplete data.