HCL learns shared and modality-specific latent representations for multimodal data.
problem Binary shared-private decomposition inadequately represents shared information across subsets of modalities.
method Hierarchical Contrastive Learning framework combining latent-variable formulation, structural sparsity, and contrastive objective.
result HCL accurately recovers hierarchical structure and improves predictive performance on multimodal data.
PRISM-FCP improves federated prediction robustness against Byzantine attacks.
problem Byzantine attacks in federated learning.
method Partial model sharing and distance-based maliciousness scores.
result Maintains nominal coverage guarantees under Byzantine attacks.
Study optimizes shared singular subspace estimation from noisy matrices.
problem Estimating shared singular subspaces across multiple noisy matrices.
method Low-rank matrix denoising framework with Stack-SVD and novel estimators.
result Stack-SVD achieves minimax rate-optimality for identical shared subspaces, and novel estimators for partial sharing.
The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduct…
Model improves covariance estimation from shared and distinct datasets.
problem Limited sample sizes and shared covariance structure across related datasets.
method Spiked covariance model with shared subspace, closed-form pooling weight, and asymptotic guarantees.
result Improves estimation of high-dimensional covariance matrices from related datasets.
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of K generator distributions. As the generators are partially shared between the modeling of different true data distributions, shared ones ca…
Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across different domains. In this more general and practical scenario, a major challenge is how to select source instances in the shared classes acro…
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
problem Representing clinical concepts and patients in electronic health records
method Spectral-based unsupervised learning with flexible knowledge transfer
result Outperforms competing approaches in challenging scenarios
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes eve…
A new Federated Learning approach balances personalization and global training.
problem Breaking the curse of data heterogeneity in Federated Learning.
method Splitting variables into global and local parameters, using a simple algorithm.
result The approach allows each client to fit their data perfectly, breaking the curse of data heterogeneity.
Predicts destinations and routes from partial trajectory data.
problem Predicting destinations and routes from partial trajectory data for applications like parking suggestions and ride-sharing.
method Three-step procedure: k-d tree-based space discretization, recurrent neural network for destination prediction, and route calculation.
result Best models predict destinations with a mean error of 1.3 km and 1.43 km.
We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, w…
New algorithm extracts shared latent space for cortico-muscular interactions.
problem Challenges of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis.
method Structured and sparse partial least squares coherence (ssPLSC) algorithm.
result ssPLSC achieves competitive or better performance in scenarios with limited sample sizes and high noise levels.
Surveying joint Gaussian graphical models to identify shared structures across domains.
problem Estimating shared structures across different data sources.
method Statistical inference of joint Gaussian graphical models.
result Improved estimation power for high-dimensional data.
Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example. At one extreme, if e…
When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated share repurchase contracts, VWAP-minus profit-sharing contra…
There is growing interest in the design of pension annuities that insure against idiosyncratic longevity risk while pooling and sharing systematic risk. This is partially motivated by the desire to reduce capital and reserve requirements while retaining the value of mortality credits; see for example Piggott, Valdez an…
The paper analyzes PLS-SVD in high-dimensional data integration, revealing its strengths and limitations.
problem Understanding the behavior of PLS-SVD in high-dimensional data integration.
method Analysis using random matrix theory and singular value decomposition.
result PLS-SVD exhibits counter-intuitive or limiting behavior in certain regimes and outperforms PCA when detecting common latent subspace.
Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition m…
A new method for efficient online federated learning reduces communication overhead.
problem Real-world limitations in online federated learning, such as heterogeneous client participation and communication delays.
method Proposes a communication-efficient asynchronous online federated learning (PAO-Fed) strategy.
result Achieves the same convergence properties as online federated stochastic gradient while reducing communication overhead by 98 percent.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
problem Heterogeneous multi-agent optimization challenges in resource use and information sharing.
method ARCO-BO integrates a consensus mechanism, budget-aware sampling, and partial input sharing for heterogeneous design spaces.
result ARCO-BO outperforms independent and collaborative BO methods in complex multi-agent settings.
Develops a new method to compute risk-sharing allocations using Laplace transforms.
problem Complex integrals in computing conditional mean risk-sharing allocations.
method Uses Laplace-Stieltjes transforms to compute risk-sharing allocations from joint transforms.
result Provides closed-form or semi-analytic solutions for a broad class of distributions.
Hierarchical Partial-Order Models for Ranking
problem Rank aggregation combining ordered lists
method Hierarchical partial-order models
result Bayesian inference for latent poset hierarchy
CEDAR efficiently analyzes distributed EHR data without sharing patient-level info.
problem Analyzing patient-level data from multiple EHRs databases without sharing raw data.
method Tackles by turning problem into missing data, incorporating posterior samples.
result Improves efficiency and privacy of parameter estimates in sparse regressions.
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recove…
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for …
CONCERT improves transfer learning by borrowing partial information from auxiliary datasets.
problem Inefficiency of global similarity measures in transfer learning for high-dimensional data.
method Conditional spike-and-slab prior with covariate-specific priors for robust partial information transfer.
result CONCERT achieves variable selection and information transfer simultaneously, improving performance on the target.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
problem Identifying precise mouse brain microscopy images from the Allen atlas.
method Siamese Networks with contrastive learning to find corresponding atlas plates for partial images.
result Siamese CNNs achieved 25% TOP-1 and 100% TOP-5 accuracy in identifying brain slices from the Allen atlas.
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sha…
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.
FedAvg converges linearly to global minimum in federated learning with partial participation.
problem Challenges in federated learning with partial client participation.
method Federated averaging (FedAvg) method for over-parameterized neural networks.
result FedAvg converges to global minimum at a linear rate after t iterations.
New algorithm for online learning with noisy side observations.
problem Online learning with noisy side feedback and graph-structured dependencies.
method Proposes an algorithm using a weighted directed graph to model dependencies and guarantees a regret bound of O(√α* T).
result Guarantees a regret of O(√α* T) after T rounds, where α* is the effective independence number.
Paper tackles robust decision-making from multiple sites with shared structure.
problem Learning robust sequential decisions from heterogeneous multi-site datasets.
method Group-Robust MDPs with d-rectangular uncertainty sets, feature-wise worst-case aggregation, and cluster-level pooling.
result Proves suboptimality bound for robust planning policy under robust partial coverage assumption.
Probabilistic method combines space and time uncertainties in PDEs.
problem Separate treatment of space and time in PDE solvers obscures interactions and error quantification.
method Gaussian process interpretation of finite difference methods interacting with probabilistic ODE solvers.
result Joint quantification of space- and time-uncertainty possible without sacrificing ODE solver performance.
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models. We define the MVRL framework by extending partially observable Markov decision processes (POMDPs) to support more than one observation mod…
Lectures on surface evolution through singularities.
problem Analyzing the mean curvature flow of surfaces and their singularities.
method Analysis of neck and conical singularities, using monotonicity formulas, epsilon-regularity, weak solutions, and blowup techniques.
result Unique evolution through neck singularities, nonuniqueness through conical singularities.
Paper proposes CLAIR for efficient LLM fine-tuning across clients.
problem Fine-tuning large language models (LLMs) efficiently and collaboratively.
method Federated LoRA fine-tuning with Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR).
result CLAIR achieves better performance and contamination detection compared to local fine-tuning.
New EPS insurance offers partial protection against superannuation losses.
problem Lack of efficient investment insurance for superannuation holders.
method Developed a new financial derivative, equity protection swap (EPS), and derived a fair pricing formula.
result EPS can be an efficient investment insurance tool for superannuation accounts.
In conventional domain adaptation, a critical assumption is that there exists a fully labeled domain (source) that contains the same label space as another unlabeled or scarcely labeled domain (target). However, in the real world, there often exist application scenarios in which both domains are partially labeled and n…
One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on their high-level information (deep features). However, since the domain shift between sour…
RL agents fail to generalize to unseen environments, even when dynamics are similar.
problem RL agents fail to generalize to unseen environments despite similar dynamics.
method Analyzed policy learning in POMDPs, formalized training dynamics as instances, and introduced a shared belief representation over an ensemble of specialized policies.
result Maximizing rewards induces instance-specific policies that are suboptimal on the training set.
Study on estimating sparse transition matrix of partially-observed VAR with noisy and sparse data.
problem Estimating sparse transition matrix of partially-observed VAR with noisy and sparse data.
method Yule-Walker equation, Dantzig selector, minimax lower bound.
result Near-optimality of the proposed estimator with convergence rate analysis.
Paper proposes an algorithm for lifelong learning with shared structure.
problem Lifelong learning with shared structure in an online setting.
method Proposes a simple algorithm using multi-task empirical risk minimization.
result Establishes a sample complexity bound based on task-eluder dimension.
Kernel method embeds noisy datasets, capturing shared structures.
problem Limited power in capturing nonlinear structures, noisiness, high-dimensionality, and interpretability issues.
method Kernel spectral joint embeddings using duo-landmark integral operators.
result Consistent recovery of low-dimensional noiseless signals and convergence to eigenfunctions of integral operators.
New method for MTL with varying sparsity patterns across tasks.
problem Jointly training multiple linear models with differing sparsity patterns.
method Mixed-integer programming formulation and scalable algorithms.
result Our methods leverage shared support information to improve variable selection.
Unified multi-view learning framework using OPLS with regularization and deep extensions.
problem Improving multi-view learning for classification and feature extraction.
method Orthonormalized Partial Least Squares (OPLS) with regularization and deep extensions.
result Unified multi-view learning framework with improved performance.
We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…
A projective geometry is an equivalence class of torsion free connections sharing the same unparametrised geodesics; this is a basic structure for understanding physical systems. Metric projective geometry is concerned with the interaction of projective and pseudo-Riemannian geometry. We show that the BGG machinery of …