arXiv research

Develops a personalized reinforcement learning algorithm for dyadic health interventions.

Efficient algorithm computes knot invariants quickly.

Method estimates treatment effects in dyadic data with unknown confounders.

Estimates piecewise polynomials and bounded variation functions using optimal decision trees.

The paper develops a cross-validation method for improving signal denoising techniques.

Improves algorithm selection for thousands of candidates using dyadic features.

Develop conformal prediction for dyadic regression under complex missingness.

Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAE's original formulation as normal distributions with a diagonal covariance matrix limits the flexibility to match the true posterior distribution. We propose a new transformation, …

New framework tackles fairness in link prediction beyond demographic parity.

Dyadic Data Prediction (DDP) is an important problem in many research areas. This paper develops a novel fully Bayesian nonparametric framework which integrates two popular and complementary approaches, discrete mixed membership modeling and continuous latent factor modeling into a unified Heterogeneous Matrix Factoriz…

Paper introduces a method for generating interlocutor-aware facial gestures in dyadic settings.

Paper recovers lattice signal partitions efficiently.

Study predicts internet-based treatment effects for GPPPD based on dyadic coping.

A novel hypergraph partitioning method using tensor eigenvalue decomposition captures super-dyadic interactions.

The paper examines logistic regression in sparse network settings, improving inference under varying degrees of dyadic dependence.

In this paper long-run risk sensitive optimisation problem is studied with dyadic impulse control applied to continuous-time Feller-Markov process. In contrast to the existing literature, focus is put on unbounded and non-uniformly ergodic case by adapting the weight norm approach. In particular, it is shown how to com…

WALNUTS improves sampling efficiency and robustness for multi-scale distributions.

We present an approach to deep estimation of discrete conditional probability distributions. Such models have several applications, including generative modeling of audio, image, and video data. Our approach combines two main techniques: dyadic partitioning and graph-based smoothing of the discrete space. By recursivel…

New method reduces linear regret in high-dimensional bandit problems.

New method distinguishes predictive distribution estimators in high-dimensional inputs.

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…

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 …

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…

We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country $i$ took action $a$ toward country $j$ at time $t$"---known as dyadic events---in order to form an…

New method for efficient ERG fitting on large graphs.

Global existence and decay for quasilinear wave equations on various spacetimes, including Kerr black holes.

Proves subelliptic estimates for geometric Kramers-Fokker-Planck operators on closed manifolds.

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 …

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 …

New method selects diffusion scales for graph wavelets.

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…

Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships …

Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to fi…

Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the lower and upper bound at least a target percentage of the time; and tight, having a small mean width of the bounds. Many prior techniques f…

New methods for ordinal classification of interval-valued data and functional data.

Develops active intervals for geodesics in Teichmüller space.

Paper tackles risk-sensitive impulse control for continuous-time processes.

Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is shown that symmetric intervals are the shortest. The length of the sho…

CONTINA provides adaptive confidence intervals for traffic demand prediction.

Proposes a method to create prediction intervals for neural networks using cross-validation.

The statistical properties of the return intervals $τ_q$ between successive 1-min volatilities of 30 liquid Chinese stocks exceeding a certain threshold $q$ are carefully studied. The Kolmogorov-Smirnov (KS) test shows that 12 stocks exhibit scaling behaviors in the distributions of $τ_q$ for different thresholds $q$. …

The paper presents a method for generating well-calibrated prediction intervals using quality-driven deep ensembles.

Constructs tail-specific prediction intervals for financial applications

Analyzes biased random walks and corrupted intervals in adversarial settings.

Paper introduces a new method for classifying interval-valued time series.

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…

Extends Fisher's Discriminant Analysis for interval-valued data.