arXiv research

Paper tackles high-order inference in structured prediction tasks.

Differentiable relaxation for inferring partial orders from noisy linear data.

SOLBP extends efficient inference to uncertain Bayesian networks.

Paper proposes a second-order method for faster SVI convergence.

Combines neural networks and probabilistic graphical models for efficient higher-order inference.

We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, TrustVI proposes and assesses a step based on minibatches of draws from the variational distribution. The algorithm provably converges to a stat…

Bayesian causal inference method improves accuracy over traditional approaches.

A framework infers hyperedges and overlapping communities in hypergraphs.

New method improves inference for discrete diffusion models, achieving better quality and efficiency.

This method infers models from data with physical insights, minimizing model order.

The Neural Autoregressive Distribution Estimator (NADE) and its real-valued version RNADE are competitive density models of multidimensional data across a variety of domains. These models use a fixed, arbitrary ordering of the data dimensions. One can easily condition on variables at the beginning of the ordering, and …

New method finds significant high-order interactions efficiently.

Proposes exact inference for continuous-time Gaussian process dynamics.

We describe and extract time-ordered multibody interactions from complex systems.

Geometric framework analyzes bias in variational inference for posterior functionals.

CoRMF uses RNNs to solve Ising models efficiently by ordering critical edges.

We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two…

Paper tackles uncertainties in reduced-order modeling of complex systems.

A new probabilistic BTD method for tensor data.

For joint inference over multiple variables, a variety of structured prediction techniques have been developed to model correlations among variables and thereby improve predictions. However, many classical approaches suffer from one of two primary drawbacks: they either lack the ability to model high-order correlations…

A method for inferring motility models and heterogeneity from particle trajectories.

Deep learning enhances active inference for dynamic state spaces.

New method calibrates predictions in chaotic systems using variational inference.

Bayesian method learns causal orderings from heterogeneous data.

We consider the problem of approximate Bayesian inference in log-supermodular models. These models encompass regular pairwise MRFs with binary variables, but allow to capture high-order interactions, which are intractable for existing approximate inference techniques such as belief propagation, mean field, and variants…

Finding statistically significant high-order interaction features in predictive modeling is important but challenging task. The difficulty lies in the fact that, for a recent applications with high-dimensional covariates, the number of possible high-order interaction features would be extremely large. Identifying stati…

We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language desce…

Study uses Wasserstein distance to identify causal orders and unmix sources.

The paper extends hypothesis testing to non-diagonalizable matrices, improving network statistics inference.

Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models. The goal of lifted inference is to carry out probabilistic inference without needing to reason about each individual separately, by instead treating exchangeabl…

Autoregressive flow models can perform causal discovery and inference tasks.

Optimizes random forest inference by defining step order to maximize accuracy.

Non-autoregressive method speeds up protein folding prediction 23 times.

A new GP model uses spherical harmonics for faster inference.

We extend the framework of variational autoencoders to represent transformations explicitly in the latent space. In the family of hierarchical graphical models that emerges, the latent space is populated by higher order objects that are inferred jointly with the latent representations they act on. To explicitly demonst…

The paper identifies causal effects in latent variable models using higher-order cumulants.

Paper defends diffusion models from membership inference attacks using Langevin dynamics.

Enhances optimization and sampling methods using ensemble-based gradient inference.

Algorithm improves reinforcement learning in MDPs with partial order policies.

Paper develops methods to optimize policies directly from human feedback without reward inference.

A new method infers causal structures and generates data without DAGs.

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density functio…

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consump…

Develops a flexible deep autoencoding topic model with scalable hybrid Bayesian inference.

iLOCO measures feature interactions without assumptions, providing statistical inference.

A power-law fit to the empirical inference-compute frontier in LOB prediction suggests a scaling-law-style frontier.

We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and output-dependent factors. SPNs are a recently introduced class of deep models for which exact and efficient inference can be performed. By com…

Topo-MLP learns network representations without message passing.