Develops a personalized reinforcement learning algorithm for dyadic health interventions.
problem Personalizing health interventions for dyadic relationships in mobile health.
method Dyadic Reinforcement Learning (dyadic RL), a Bayesian and hierarchical online algorithm.
result Established a regret bound and demonstrated empirical performance through simulations and real data.
Method estimates treatment effects in dyadic data with unknown confounders.
problem Estimating treatment effects in dyadic data with unobserved confounders.
method Neighborhood kernel smoothing method for graphon estimation.
result Derives rate of convergence for estimator and demonstrates test size control.
Paper develops a Bayesian HMF model for accurate dyadic data prediction.
problem Predicting unobserved dyadic data in various research areas.
method Integrates discrete and continuous modeling into a unified Heterogeneous Matrix Factorization model using Variational Bayesian method and online learning.
result The model outperforms state-of-the-art methods on large-scale collaborative filtering datasets.
Estimates piecewise polynomials and bounded variation functions using optimal decision trees.
problem Estimating piecewise smooth functions in general dimensions.
method Dyadic CART and Optimal Regression Tree (ORT) estimators for piecewise polynomials and bounded variation functions.
result Oracle inequalities and risk bounds for ORT estimators, demonstrating adaptivity and optimality.
Model infers latent group structures from dyadic event counts.
problem Inferring latent group structures from sparse dyadic event counts.
method Bayesian Poisson tensor factorization.
result Model outperforms non-negative tensor factorization methods.
Develop conformal prediction for dyadic regression under complex missingness.
problem Conformal prediction for dyadic regression under complex missingness mechanisms.
method Developing general technical tools and conformal prediction procedures for dyadic regression under complex missingness.
result Establishing asymptotic validity of weighted conformal prediction under a nonparametric graphon model for missingness mechanism.
Paper studies long-run risk optimization with dyadic impulses for unbounded processes.
problem Long-run risk optimization problem with unbounded and non-uniformly ergodic processes.
method Adapting weight norm approach, combining geometric drift and local minorization property.
result Existence of solution to Bellman equation for risk-averse parameters.
The paper develops a cross-validation method for improving signal denoising techniques.
problem Improving signal denoising methods for nonparametric regression.
method Develops a general cross-validation framework for signal denoising and applies it to Trend Filtering and Dyadic CART.
result Cross validated versions of Trend Filtering and Dyadic CART achieve nearly optimal convergence rates.
Improves algorithm selection for thousands of candidates using dyadic features.
problem Selecting the best algorithm from a large set of candidates for specific problems.
method Proposes extreme algorithm selection (XAS) with dyadic feature representation.
result Improves significantly over current state of the art in various metrics.
Paper introduces a method for generating interlocutor-aware facial gestures in dyadic settings.
problem Generating appropriate non-verbal behavior for conversational agents in dyadic settings.
method Probabilistic method using multi-modal cues from the interlocutor to synthesize facial gestures.
result The model successfully leverages multi-modal input from the interlocutor to generate more appropriate behavior.
Paper recovers lattice signal partitions efficiently.
problem Estimating lattice partition from noisy data.
method Uses dyadic CART for computationally-efficient partition recovery.
result Consistently estimates partition with optimal error rate.
Study predicts internet-based treatment effects for GPPPD based on dyadic coping.
problem Identifying which patients will benefit most from internet-based GPPPD treatment.
method Developed a multivariable decision tree model using recursive partitioning.
result Predicts large effects for high dyadic coping patients, small effects for low dyadic coping patients.
The paper examines logistic regression in sparse network settings, improving inference under varying degrees of dyadic dependence.
problem Improving inference in logistic regression with sparse network data.
method Sparse network asymptotics, martingale central limit theorem, variance decomposition.
result Sparse network asymptotics lead to better variance estimators for logistic regression.
New method distinguishes predictive distribution estimators in high-dimensional inputs.
problem Difficulty in evaluating predictive distributions for high-dimensional inputs.
method Introduces dyadic sampling to focus on predictive distributions associated with pairs of inputs.
result Demonstrates efficient distinction of predictive distribution estimators in high-dimensional examples.
New framework tackles fairness in link prediction beyond demographic parity.
problem Systemic biases in link prediction can exacerbate societal inequalities.
method Formalizes limitations of existing fairness evaluations and proposes a new framework.
result Proposes a lightweight post-processing method combined with decoupled link predictors.
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …
Efficient algorithm computes knot invariants quickly.
problem Computing finite type invariants efficiently for knots.
method Create look-up tables for subdiagrams indexed by dyadic intervals, then compute invariants in ildeO(n⌈2kceil) time. result Finite type invariants can be computed on an n-crossing knot in ildeO(n⌈2kceil) time, significantly faster than previous methods. A novel hypergraph partitioning method using tensor eigenvalue decomposition captures super-dyadic interactions.
problem Capturing super-dyadic interactions in k-uniform hypergraphs.
method Tensor-based representation and tensor eigenvalue decomposition for capturing interactions.
result Improved min-cut solution on 2-uniform hypergraphs (graphs) compared to standard spectral partitioning.
Enhanced VAE with DT improves flexibility in latent variable modeling.
problem Limitations of VAE's diagonal covariance matrix in matching true posterior distribution.
method Proposes dyadic transformation (DT) to model multivariate normal distributions.
result DT enhances posterior flexibility and achieves competitive results.
A deep learning method for estimating discrete conditional distributions efficiently.
problem Estimating discrete conditional probability distributions efficiently.
method Smoothed dyadic partitioning and graph-based smoothing.
result Significantly reduces error in conditional distribution estimation.
New method reduces linear regret in high-dimensional bandit problems.
problem Heavy spectral tails in streaming matrices lead to linear regret in sketch-based linear bandits.
method Dyadic Block Sketching, a multi-scale matrix sketching approach.
result Achieves sublinear regret bounds without prior knowledge of streaming matrix properties.
The Weyl transform is introduced as a rich framework for data representation. Transform coefficients are connected to the Walsh-Hadamard transform of multiscale autocorrelations, and different forms of dyadic periodicity in a signal are shown to appear as different features in its Weyl coefficients. The Weyl transform …
We build polyhedral complexes in Rn that coincide with dyadic grids with different orientations, while keeping uniform lower bounds (depending only on n) on the flatness of the added polyhedrons including their subfaces in all dimensions. After the definitions and first properties of compact Euclidean polyhedrons and c…
Global existence and decay for quasilinear wave equations on various spacetimes, including Kerr black holes.
problem Global existence and decay for quasilinear wave equations on asymptotically flat spacetimes.
method Dyadically localised nature and direct use of a blackbox linear inhomogeneous energy estimate on exactly stationary metrics.
result Global existence and decay for small-data solutions to quasilinear wave equations on a wide variety of spacetime backgrounds, including Kerr black holes.
Self-attentive network improves emotion recognition in conversations.
problem Emotion recognition in dyadic conversations using deep learning.
method Introduces a novel self-attention mechanism for capturing temporal dynamics without a decoder.
result Outperforms state-of-the-art alternatives on the IEMOCAP benchmark.
In self-organizing networks, topology and dynamics coevolve in a continuous feedback, without exogenous driving. The World Trade Network (WTN) is one of the few empirically well documented examples of self-organizing networks: its topology strongly depends on the GDP of world countries, which in turn depends on the str…
Unified view on multi-target prediction combining various machine learning subfields.
problem Simultaneous prediction of multiple target variables of diverse type.
method Formal discussion and general framework of MTP problems, identification of key properties for MTP methods.
result Unified understanding of multi-target prediction across different machine learning subfields.
New method for efficient ERG fitting on large graphs.
problem Fitting non-trivial ERGs on large graphs.
method Fast matrix block-approximation techniques for dyadic independence.
result Models can generate networks with similar properties to observed networks.
Proves subelliptic estimates for geometric Kramers-Fokker-Planck operators on closed manifolds.
problem Proving subelliptic estimates for a specific class of operators on closed manifolds.
method Significantly different method from previous works, using dyadic partition and local analysis in position variable.
result Maximal subelliptic estimates with control of constants in high and low friction regimes.
Recent theory work has found that a special type of spatial partition tree - called a random projection tree - is adaptive to the intrinsic dimension of the data from which it is built. Here we examine this same question, with a combination of theory and experiments, for a broader class of trees that includes k-d trees…
We consider the problem of group testing with sum observations and noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets. We study a probabilistic setting with entropy loss, in which we assume a joint Bayesian prior density on the locations …
An elliptic theory is constructed for operators acting in subspaces defined via odd pseudodifferential projections. Subspaces of this type arise as Calderon subspaces for first order elliptic differential operators on manifolds with boundary, or as spectral subspaces for self-adjoint elliptic differential operators of …
New method selects diffusion scales for graph wavelets.
problem Choosing optimal diffusion scales for graph wavelets.
method Proposes an unsupervised method using information theory.
result Method selects diffusion scales for graph wavelets.
Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y given another random vector X as input. We refer to the problem of estimating the graph G(x) of Y conditioned on X=x as ``graph-valued regression.'' In this pap…
The paper extends Gaussian processes to model complex interactions in cellular complexes.
problem Capturing topological inductive biases in machine learning models.
method Proposes Gaussian processes on cellular complexes, introducing novel kernels.
result Derives two novel kernels for modeling interactions between cells.
Novel network models capture social interaction features like reciprocity and community.
problem Modeling temporal social interaction data with reciprocity and community structure.
method Self-exciting Hawkes point processes with conditional intensity function.
result Proposed model outperforms competing approaches for link prediction.
Bayesian models for networks are often misspecified, leading to overconfident inference.
problem Real-world networks violate assumptions of geometry and link function in latent space models.
method Proposes a generalized posterior framework for random geometric graphs, using Link-Sequential R-SafeBayes to adaptively tune posterior regularization.
result Improved calibration and better link prediction performance demonstrated on synthetic and real-world networks.
BEGIN network models binary data without parametric assumptions.
problem Conditional independence in non-parametric families of binary data.
method BEGIN network models binary data using sparse linear representations and block factorizations.
result BEGIN network captures conditional independence for arbitrary binary and multinomial variables.
A framework infers hyperedges and overlapping communities in hypergraphs.
problem Characterizing the structural organization of hypergraphs with higher-order interactions.
method Statistical inference to infer missing hyperedges and detect overlapping communities.
result Efficient numerical implementation and strong performance on real-world systems.
TTERGM models improve social network predictions by incorporating triadic relationships.
problem Lack of models capturing triadic relationships and social learning theories in temporal network data.
method Introduced TTERGM, a generative model that includes triadic relationships and social learning theory as additional probability distributions. Parameters are estimated via Monte Carlo maximum likelihood.
result TTERGM achieves improved accuracy and fidelity compared to existing models on social network data.
Paper tackles risk-sensitive impulse control for continuous-time processes.
problem Risk-sensitive impulse control for continuous-time Feller-Markov processes.
method Probabilistic approach to solve Bellman equation and construct optimal strategy.
result Optimal strategy approximated by dyadic impulse strategies.
New spectral tests assess network model fits efficiently.
problem Determining if network models fit data well and extrapolate.
method Random matrix theory-derived goodness-of-fit tests.
result General approach simplifies parameter selection in network models.
New algorithm predicts piecewise regular functions online.
problem Online prediction of piecewise regular functions.
method Modified sleeping experts aggregation algorithm.
result Oracle risk bounds for all local regions.
Study reveals linguistic signs of betrayal in online strategy games.
problem Predicting the dissolution of friendships in online games.
method Characterized dyadic interactions, analyzed temporal patterns, and examined conversational attributes.
result Subtle linguistic cues indicate impending betrayal in online strategy games.
Study finds neural dialog models struggle with conversational tasks.
problem Insufficient understanding of dialog by neural models.
method Analysis of internal representations and evaluation of model performance.
result Neural dialog models lack key conversational skills like answering questions and inferring contradiction.
Proposes a probabilistic framework for stationary topological signals on simplicial complexes.
problem Complex data structures require new models and tools.
method Generalizes stationarity to topological signals on simplicial complexes.
result Defines topological power spectral density (PSD) for stationary signals.
New method separates market motion from stock correlations.
problem Understanding the dynamics of stock correlations relative to market motion.
method Cluster reduced-rank correlation matrices by subtracting the largest eigenvalue.
result Extracted market states are quasi-stationary over long periods.
New findings show Shannon information measures fail to accurately assess multivariate dependencies.
problem Accurately measuring information flow in complex systems.
method Demonstrated that Shannon information measures fail to distinguish between dyadic and polyadic relationships.
result Shannon information measures are inadequate for discovering meaningful dependency structures in joint probability distributions.