DEHRL extends HRL to handle multiple levels with diverse subpolicies.
problem Handling multiple levels of hierarchical reinforcement learning with diverse subpolicies.
method Extensible and scalable framework built levelwise, focusing on diversity of subpolicies.
result DEHRL outperforms state-of-the-art baselines in multiple domains.
Paper addresses instance-level label prediction in MIL, improving performance compared to existing methods.
problem Lack of instance-level label prediction in existing MIL approaches restricts their applicability.
method Proposes a novel algorithm with instance-level loss function, unbiasedly and consistently estimated.
result Empirically validated superior instance-level and bag-level performance compared to state-of-the-art methods.
In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple fe…
We introduce multiplicative LSTM (mLSTM), a recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition functions for each possible inp…
Paper proves Pareto efficient insurance for multiple entities.
problem Optimizing insurance for multiple policyholders and insurers.
method Sum-minimization characterization and pairwise implementability analysis.
result Characterization of Pareto efficient insurance arrangements.
NeurT-FDR controls FDR by incorporating auxiliary covariates in deep learning.
problem Controlling FDR in complex large-scale problems with indirect relations among covariates.
method NeurT-FDR uses a deep Black-Box framework that parametrizes test-level covariates as a neural network and adjusts auxiliary covariates through a regression framework.
result NeurT-FDR makes substantially more discoveries in real datasets compared to competitive baselines.
The paper develops methods for causal inference from single-cell RNA sequencing data with multiple outcomes.
problem Causal inference from single-cell RNA sequencing data with multiple heterogeneous outcomes.
method Generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes.
result Demonstrates the use of semiparametric inferential results for estimating causal effects in genomics.
We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort an…
Analyzes Kodaira-Iitaka dimension and multiplicity using intersection theory.
problem Understanding Kodaira-Iitaka dimension and multiplicity in analytic terms.
method Expresses dimensions and multiplicity in terms of intersection theory of plurisubharmonic envelopes.
result Introduces non-pluripolar numerical Kodaira-Iitaka dimension and shows it dominates the classical dimension.
Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process---the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters datasets on multiple levels. Our sei…
The nonzero level sets of a homogeneous, logarithmically homogeneous, or translationally homogeneous function are affine spheres if and only if the Hessian determinant of the function is a multiple of a power or an exponential of the function. In particular, the nonzero level sets of a homogeneous polynomial are proper…
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
problem Need for uncertainty quantification with multiple confidence levels in diverse applications.
method Online optimization perspective to enforce nestedness of prediction sets while controlling quantile estimation error.
result Achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency.
New method improves transductive learning predictions with multiplicative oracle inequalities.
problem Improving transductive learning predictions with known covariates.
method Median of Level-Set Aggregation (MLSA) for transductive LOO prediction.
result Proved multiplicative oracle inequality for LOO error.
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (…
A strategy for spectrum sharing in CRNs with multiple PT power levels.
problem Efficient spectrum usage for secondary users in CRNs with multiple PT power levels.
method Data-driven/machine learning based multi-level spectrum sensing and prediction-transmission structures.
result The proposed strategy effectively aligns the ST with the PT power levels, improving spectrum usage.
Approach for recovering shared structure from multiple networks with unknown noise.
problem Recovering shared structure from multiple networks with unknown edge distributions.
method Exploits shared mean structure to denoise edge-level measurements and estimate population-level parameters.
result Established a finite-sample concentration inequality for low-rank eigenvalue truncation of a random weighted adjacency matrix.
RLHC uses multiple critics at different levels to enhance RL performance.
problem Speed up RL learning and increase cumulative rewards in competition tasks.
method Introducing multiple cooperative critics at two levels of a hierarchy to provide global information.
result RLHC outperforms PPO in tennis and soccer competitions.
Develops a method to ensure accurate quantile forecasts across multiple levels.
problem Ensuring accurate quantile forecasts at multiple levels, even under distribution shifts.
method Multi-level quantile tracker (MultiQT) wraps around any forecaster to produce calibrated forecasts.
result Guaranteed calibration of quantile forecasts at multiple levels, even against adversarial shifts.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
Improved locally private sparse estimation with multiple samples per user.
problem Challenges in high-dimensional locally private sparse estimation.
method Proposes a framework for user-level locally private sparse linear regression with multiple samples per user.
result Eliminates the dependency of dimensionality on error bounds, achieving tighter error bounds.
Paper proposes a deep subspace clustering method using multi-level representations.
problem Deep subspace clustering of images.
method Convolutional autoencoders with multiple fully-connected layers for multi-level representations, loss minimization with iterative updates.
result The method outperforms state-of-the-art methods on real-world datasets.
GTC improves neural network compression and accuracy without multiplications.
problem Balancing accuracy and computational complexity in deep neural networks.
method Generalizes ternary connect to allow arbitrary levels and integer powers of two, learning optimal levels and weights end-to-end.
result GTC achieves comparable accuracy to binary networks with superior compression and hardware benefits.
The nonzero level sets in n-dimensional flat affine space of a translationally homogeneous function are improper affine spheres if and only if the Hessian determinant of the function is equal to a nonzero constant multiple of the nth power of the function. The exponentials of the characteristic polynomials of certa…
This work improves generative models by using feedback from multiple dependent models.
problem Improving the performance of generative models in multi-agent systems.
method Building a hierarchical set-up of multiple dependent generative models and using feedback to improve lower-level models.
result The technique improves the performance of lower-level generative models under certain conditions.
Deep Hedging learns optimal strategies for various risk levels.
problem Finding optimal hedging policies for diverse risk aversions.
method Continuous Reinforcement Learning with actor-critic algorithm.
result Demonstrated effectiveness in a stochastic volatility model.
One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained …
We describe arbitrary multiplicative differential forms on Lie groupoids infinitesimally, i.e., in terms of Lie algebroid data. This description is based on the study of linear differential forms on Lie algebroids and encompasses many known integration results related to Poisson geometry. We also revisit multiplicative…
MLCC clusters data at multiple significance levels, detecting anomalies without distributional assumptions.
problem Clustering and anomaly detection in data with unknown distributions.
method Hierarchical, conformal prediction-based clustering.
result MLCC automatically selects cluster number and detects anomalies robustly.
A new HRL algorithm learns and exploits multiple subgoals for faster exploration.
problem Sparse reward problem in reinforcement learning.
method Multi-goal HRL algorithm with Manager and Worker policies.
result Significantly improved exploration efficiency with reduced training time.
Constructs new elicitable risk measures with multiplicative scoring functions.
problem Defining new elicitable risk measures with specific properties.
method Constructs new elicitable risk measures using a multiplicative scoring function.
result Encompasses and allows construction of novel elicitable risk measures.
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.
problem Explaining and forecasting price movements in equity markets using OFI.
method Systematic approach to combine OFIs from multiple levels into an integrated variable, testing multi-asset models with and without cross-impact terms.
result Lagged cross-asset OFIs improve future return forecasting but not contemporaneous price impact.
NeurT-FDR controls FDR by incorporating feature hierarchy.
problem Controlling FDR in complex, large-scale hypothesis testing problems.
method NeurT-FDR uses a neural network to parametrize test-level covariates and a regression framework to adjust feature hierarchy.
result NeurT-FDR makes substantially more discoveries than competitive baselines.
Neural network classifies breast cancer lesions using global and local image features.
problem Classifying breast cancer lesions in medical images with high resolution and small regions of interest.
method Proposes a neural network that combines global saliency maps and local patches for pixel-level saliency maps.
result Achieves radiologist-level performance in screening mammography interpretation.
InstanceFlow visualizes classifier confusion over training epochs.
problem Limited model interpretability through aggregate performance measures.
method Dual-view visualization tool showing instance-level learning behavior.
result Allows temporal analysis of training process and instance-level performance.
RocketStack integrates predictions from multiple base learners using a recursive stacking architecture up to ten levels.
problem Feature redundancy, complexity, and computational burden in deep stacking.
method Level-aware recursive stacking with pruning and compression techniques.
result Increasing accuracy with depth and outperforming standalone ensembles at later levels.
Paper speeds up matrix multiplication on Intel PIII using SIMD.
problem Efficiently multiplying large matrices for faster algorithm performance.
method Implemented matrix-matrix multiply using Intel Pentium SIMD architecture.
result Average performance 2.09 times faster than public domain routines.
The paper develops tests for variable selection using LARS in high dimensions.
problem Variable selection and multiple testing in high-dimensional settings.
method Least Angle Regression (LARS) and post-selection joint law of knots.
result Exact non-asymptotic level testing procedures for variable selection.
Graph neural networks improve MIL performance without losing interpretability.
problem Learning bag-level labels from bags of instances.
method Proposed a GNN-based algorithm treating bags as graphs to learn embeddings and predict labels.
result Achieves state-of-the-art performance on MIL datasets.
New models improve language generation by sharing intermediate states.
problem Language models struggle with past mistakes in sequence generation.
method Integrate second-order terms in hidden-state update, sharing intermediate states.
result Shared parametrization improves language modeling performance.
The paper proposes a new method for modeling and quantifying uncertainty in multiple closed curves.
problem Modeling and uncertainty quantification of multiple closed curves.
method A multiple-output, multi-dimensional Gaussian process modeling framework.
result The proposed method provides meaningful uncertainty quantification for curve and shape-related tasks.
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
JIMA uses multi-level preference data to recommend composite items.
problem Recommending composite items efficiently with multi-level preference information.
method Joint Interaction Modeling (JIMA) approach that integrates multi-level preference data and interactions.
result JIMA outperforms advanced baselines in offline and online settings.
The study examines mass drop and multiplicity in mean curvature flow.
problem Analyzing mass drop and multiplicity in mean curvature flow.
method Defined Brakke flow with variational inequality, proved mass drop conditions.
result Mass drop and multiplicity one conjecture are equivalent for Brakke flows.
FedCluster accelerates federated learning convergence by cycling device groups.
problem Federated learning convergence issues with device-level data heterogeneity.
method FedCluster groups devices into clusters that cycle through learning rounds, boosting convergence with meta-updates.
result FedCluster achieves faster convergence in nonconvex optimization compared to FedAvg.
Paper proposes PRR network for better experience reuse in reinforcement learning.
problem Efficient experience reuse in reinforcement learning across multiple granularities.
method Proposes PRR network trained on multi-level architecture to extract and store experience.
result PRR network leads to better experience reuse and improved performance.
Proposes a method for explaining black-box models with nested feature attributions.
problem Making black-box models transparent and trustworthy.
method Model-agnostic local explanation method exploiting nested feature structure and consistency property.
result Accurate and consistent HiFAs and LoFAs estimated using fewer model queries.
Proposes a method to learn fair predictors for multiple subgroups with limited data.
problem Fairness and accuracy issues in learning from multiple subgroups with limited data.
method Formulates a bilevel objective to learn subgroup-specific predictors and a fair predictor that is close to all of them.
result The method effectively controls group sufficiency and generalization error, improving fairness and accuracy.