arXiv research

Differentiable relaxation for inferring partial orders from noisy linear data.

The paper tackles partial inference in structured prediction using a convex optimization approach.

Inference for normal and Monte Carlo distributions using minimum relative entropy.

New method for inferring network topology from partial data.

New BED method handles online inference for partially observed dynamical systems.

New algorithm infers trajectories from partial observations using optimal transport.

Study uses deep neural networks for inference in partially linear models with dependent data.

A new approach uses partial likelihood to improve tree-based density estimation and inference.

ParKCa combines multiple causal inference methods to infer new causes from known and unknown factors.

PPI uses predictions and weighting to infer from partially labeled data.

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation …

Efficiently learns from partial labels using variational inference.

Transformer learns to infer partial MDPs for efficient in-context adaptation and exploration.

Develops a method to infer partial rankings from sparse comparisons.

Paper proposes efficient weight updates for edge nodes with minimal communication.

Improved Bayesian neural network inference by selectively removing redundant modes.

Unified method for inference on partially identified causal effects using covariates.

Temporal Point Processes (TPP) with partial likelihoods involving a latent structure often entail an intractable marginalization, thus making inference hard. We propose a novel approach to Maximum Likelihood Estimation (MLE) involving approximate inference over the latent variables by minimizing a tight upper bound on …

Deep active inference learns policies from sensory inputs.

This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample s…

Graph neural networks help infer causal effects from partially observable data.

The paper tackles skeptical binary inferences in multi-label problems with sets of probabilities.

Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.

Proposes a method to create predictive sets from partially labeled data.

Algorithm improves reinforcement learning in MDPs with partial order policies.

We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the co…

Functional PLS improves prediction and inference for scalar responses from functional predictors.

Study efficient inference for network quantile causal effects with partial interference.

Paper tackles causal inference with partially labeled data, introducing robust methods.

Bayesian neural networks can be partially stochastic without losing predictive power.

Paper proposes learning system dynamics from irregularly-sampled partial observations.

Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. Tha…

The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.

Hierarchical Partial-Order Models for Ranking

We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variation…

Deep learning scheme identifies and reconstructs chaotic and stochastic systems from noisy data.

WNVI solves inverse problems without forward models using neural networks.

Algorithm optimizes measurement sequence to minimize data acquisition.

Delta-AI speeds up inference in sparse PGMs by local credit assignment.

This paper studies when particle filtering is efficient for planning in partially observed systems.

Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…

New method improves generative model performance by fully conditioning variational posteriors.

The paper presents a framework for estimating treatment effects using partial network data.

Flexible DNN for survival data, avoiding proportional hazards assumption.

We consider the problem of providing nonparametric confidence guarantees for undirected graphs under weak assumptions. In particular, we do not assume sparsity, incoherence or Normality. We allow the dimension $D$ to increase with the sample size $n$. First, we prove lower bounds that show that if we want accurate infe…

This work frames active inference through control as inference, offering robust control algorithms.

PRL improves off-policy evaluation in partially observed MDPs.

New method for epidemic model inference using multinomial approximations.