Introduces info intervention to handle causal questions and check counterfactual variables.
problem Controversial interpretation of causal questions for non-manipulable variables and lack of power to check counterfactual variables.
method Intervenes input/output information of causal mechanisms, providing causal diagrams for communication and theoretical focus.
result Causal diagrams based on info intervention provide a new perspective on information transfer as causality.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of k-sparse signals b…
Paper tackles multi-objective contextual bandit with similarity info.
problem Extending classical contextual bandit with multiple conflicting objectives.
method Developed PCZ algorithm that learns arms close to Pareto front.
result Proved nearly optimal regret bound for PCZ algorithm.
Deep RL model learns 2.5D fighting games with height ambiguity.
problem Ambiguity in character height/depth and sequential action orders in 2.5D fighting games.
method Modified A3C network with Recurrent Info network for combo skill observation.
result Successfully learned and played Little Fighter 2 (LF2) 2.5D fighting game.
New matrix reveals cluster info in sparse directed graphs.
problem Analyzing cluster information in directed graphs.
method Proposed complex non-backtracking matrix integrating Hermitian adjacency matrix and non-backtracking matrix properties.
result The complex non-backtracking matrix holds cluster information, especially for sparse directed graphs.
We characterize the performance of sequential information guided sensing, Info-Greedy Sensing, when there is a mismatch between the true signal model and the assumed model, which may be a sample estimate. In particular, we consider a setup where the signal is low-rank Gaussian and the measurements are taken in the dire…
New algorithms learn online without prior data info.
problem Lack of prior data info hinders optimization algorithms.
method Developed new lower bounds and constructed algorithms.
result Achieved optimal performance on data without prior info.
Paper accelerates L-BFGS with second-order info and stochastic batches.
problem Optimizing finite-sum minimization problems.
method Proposes L-BFGS with smooth gradient difference estimates and well-scaling initial Hessians.
result Achieves acceleration in both convex and nonconvex cases.
HybridSVD combines user and item info for efficient, flexible recommendations.
problem Lack of effective methods for incorporating both user and item side information in collaborative filtering.
method Hybrid algorithm using PureSVD with generalized singular value decomposition and cold start solution.
result Superior performance compared to similar hybrid models on various datasets.
Study multiplayer bandits without collision info, achieving regret bounds.
problem Multiplayer bandits without collision info.
method Two feedback models considered; algorithms for both models.
result First theoretical guarantees for second model with square-root regret.
Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific a…
Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consi…
Bayesian truth discovery uses social network info to improve reliability estimates.
problem Truth discovery from unreliable or biased agents with correlated biases in the same community.
method Laplace variational inference and stochastic variational inference for large networks.
result Our methods outperform other inference methods in sparse observation scenarios.
Estimates non-parametric logistic model using case-control data and external summary info.
problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.
Algorithms compute topological info about curves in surfaces.
problem Computing topological information about curves in surfaces.
method Algorithms using triangulations or handle structures to compute geometric intersection number and isotopy.
result Polynomial-time algorithms for topological curve analysis.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
problem Lack of infrastructure and pooled vehicle info in existing AV datasets.
method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.
result VTrackIt reduces high-risk edge cases in AV trajectory predictions.
Study achieves fairness without demographic info, improving regression tasks.
problem Achieve fairness in models without prior demographic info.
method VFair method to minimize training loss variance, dynamic update approach.
result Regression tasks can achieve significant fairness improvement without prior demographics.
Enhances network embedding with auxiliary info using matrix factorization.
problem Lack of flexible incorporation of auxiliary info (content and labels) in network embedding.
method Explicit matrix factorization incorporating structure, content, and label info.
result Unified framework for learning network embedding with structure, content, and label info.
ViterbiNet uses deep learning to detect symbols without channel info.
problem Symbol detection in digital receivers without channel state info.
method Integrates DNNs into Viterbi algorithm, leaving structure intact.
result Performance of ViterbiNet approaches CSI-based Viterbi, tracks dynamic channels.
β-VAEs retain label info even at high compression.
problem Retaining label information in compressed representations.
method Investigation of β-VAE architectures on Binary Static MNIST and Omniglot. result β-VAEs can retain label information even at high compression.
MFAI uses gradient boosted trees to leverage auxiliary info for scalable Bayesian matrix factorization.
problem Matrix factorization struggles with poor data quality, especially high sparsity and low SNR.
method Integrates gradient boosted trees into probabilistic matrix factorization framework.
result MFAI effectively leverages auxiliary information, improving model performance.
Improved semantic segmentation accuracy by addressing class imbalance.
problem Class imbalance in training data leads to misclassification of rare classes.
method Localized weighting of posterior class probabilities with pixel-wise priors.
result Significant improvement in recall and reduction of non-detection rate for rare classes.
Proposes online compressive robust PCA with prior info.
problem Online video foreground-background separation from sparse and low-rank components.
method Combines prior info and compressive sensing for recursive separation.
result Improves separation using multiple prior info and theoretical bounds.
Paper tackles circularity issues in machine learning predictions.
problem Circularity problems in machine learning predictions.
method Not specified in the abstract.
result Not specified in the abstract.
Transformer learns graph structure better with subgraph info.
problem Transformer struggles with structural similarity in graph learning.
method Structure-Aware Transformer with subgraph attention.
result Improves graph prediction benchmarks significantly.
New FGSPCA method captures grouping and sparse structures in PCA without prior info.
problem Capture grouping and sparse structures in PCA without prior info.
method Truncated regularization with alternating algorithm.
result FGSPCA method reduces model complexity and increases interpretability.
Bayesian optimization with binary auxiliary info for faster target function optimization.
problem Optimizing target functions with expensive binary auxiliary information.
method Mixed-type Gaussian process (MOGP) and information-based acquisition functions (MT-ES, MT-PES).
result Efficient approximation of mixed-type predictive ES via random features.
New method controls linear systems with partial info and disturbances.
problem Controlling linear dynamical systems under partial observation and adversarial disturbances.
method Double Spectral Control (DSC) using two-level spectral approximation strategy.
result Matches best known regret guarantees with exponential runtime improvement.
MLM models match or exceed RN in generating wind power time series without location info.
problem Generating accurate long-term wind power time series without location information.
method Applied neural networks to MERRA2 wind speed data with and without location info.
result MLM models produce time series of equal or better quality than RN.
New method uses mutual info and network science to explain deep learning models.
problem Interpreting deep neural networks for understanding their decision-making process.
method Coupling mutual information with network science to quantify information flow in deep learning models.
result Proposed NIF technique for codifying information flow in deep learning models.
Paper learns robot's end-effector position without prior info.
problem Lack of prior robot structure or sensor info.
method Generates internal end-effector config from raw data.
result Can control robot without prior kinematic info.
AMM models complex data with minimal labeled info.
problem Clustering complex data with little labeled info.
method Adversarial optimization of mixture models.
result Achieves 2.86% unsupervised clustering error on MNIST.
Study mutual info for community detection with covariate and correlated networks.
problem Community detection with covariate and correlated networks.
method Asymptotic upper bound and MMSE matrix heuristic analysis.
result Explicit characterization of combined information effects.
MO-GP models fill gaps in biophysical data with across-domain info transfer.
problem Gap filling of biophysical parameters LAI and fAPAR over rice areas.
method Multi-output Gaussian Processes (MO-GP) based on Linear Model of Coregionalization (LMC).
result MO-GP models successfully predict biophysical variables even in high missing data regimes.
System separates sounds from mixtures without ground truth info.
problem Sound separation from multi-channel mixtures without labeled data.
method Deep clustering on multi-channel mixtures, projecting bins to spatially correlated clusters.
result Performance matches ground truth separation using only multi-channel mixtures.
PLUS pre-trains protein sequences with structural info, improving performance.
problem Lack of labeled protein sequences for training models.
method PLUS combines masked language modeling with same-family prediction for pre-training.
result PLUS-RNN outperforms other models in protein biology tasks.
PHI Scrubber removes sensitive patient info from medical notes.
problem Protecting patient privacy in electronic health records.
method Deep learning model with de-convolutional neural network and bi-directional LSTM-CNN.
result Eliminates individually identifiable information from physician notes.
Optimal algorithm selects biological models without prior info.
problem Determining the correct biological model without prior knowledge.
method Systems biology models and likelihood-free inference.
result Improved model selection performance over conventional methods.
Improved GANs model geological facies with diversity and unbiased distribution.
problem Generating unbiased and representative geological models from training images.
method Info-WGAN combining InfoGAN, Wasserstein distance, and Gradient Penalty.
result Generated samples have equal probability distribution as training data.
Capsule Networks improve clothing retrieval without landmark info.
problem In-shop clothing retrieval performance improvement.
method Triplet-based Capsule Network architecture with SC and RC blocks.
result Triplet Capsule Networks outperform FashionNet and SOTA architectures.
TOGL adds topological info to GNNs, improving graph and node classification.
problem Graph neural networks lack substructure awareness, especially cycles.
method Integrates global topological information using persistent homology.
result Improves predictive performance for graph and node classification.
Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this rev…
Trading floors need to be twice as deep as electronic markets to compete.
problem Informed traders prefer fast electronic markets over slow trading floors.
method Examined the performance of trading floors and electronic markets in a hybrid system.
result Trading floors need to be twice as deep as electronic markets to compete.
Paper proposes method to recover quantized data with missing info.
problem Recovering quantized data with missing information.
method Regularized convex cost function with Bi-factorization and Augmented Lagrangian Method.
result The method finds global minimizer of the cost function.
RPO uses past and future state-action info for better policy optimization.
problem Sample inefficiency in on-policy reinforcement learning methods.
method Reflective Policy Optimization (RPO) integrates past and future state-action info for policy improvement.
result RPO improves policy performance and contracts the solution space, leading to faster convergence.
Inequalities linking entropy, Fisher info, Stein discrepancy, and Wasserstein distance on Riemannian manifolds.
problem Linking entropy, Fisher info, Stein discrepancy, and Wasserstein distance on Riemannian manifolds.
method Deriving inequalities linking these measures on Riemannian manifolds.
result Strengthening and extending existing inequalities to Riemannian manifolds.
Optimistic algorithms and Thompson sampling use info-theory for better reinforcement learning.
problem Designing algorithms that balance exploration and exploitation in reinforcement learning.
method Integrating information-theoretic concepts into optimistic algorithms and Thompson sampling.
result Cumulative regret bound depends on uncertainty and quantifies prior information value.
Proposes a robust VIB approach using soft labels and mutual info estimation.
problem Improving robustness of VIB to adversarial perturbations.
method Refines categorical class information with soft labels from a reference network, relaxes Gaussian posterior assumption.
result Significantly outperforms benchmarked models on MNIST and CIFAR-10.