Paper shows domain recursion is more powerful than previously thought, enabling faster inference.
problem Intractable probabilistic inference in relational models.
method Study of domain recursion rule and its impact on lifted inference.
result Domain recursion extends the range of models for which lifted inference is polynomial-time.
ERM uses energy-based selection to improve recursive reasoning.
problem Lack of principled inference mechanism in recursive models.
method Energy-guided Recursive Model (ERM) introduces Hopfield energies for trajectory selection.
result ERM achieves optimal solutions on various puzzles.
Novel method recursively partitions sample space for density estimation.
problem Estimating complex density functions efficiently and accurately.
method Recursive partitioning of the sample space, asymptotically exact.
result Asymptotically exact approximation of any density function.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
problem Inaccurate posterior approximation in traditional VAEs.
method Recursive mixture estimation algorithm using functional gradient approach for iterative component selection.
result Significantly higher test data likelihood compared to state-of-the-art methods on benchmark datasets.
This text investigates relations between two well-known family of algorithms, matrix factorisations and recursive linear filters, by describing a probabilistic model in which approximate inference corresponds to a matrix factorisation algorithm. Using the probabilistic model, we derive a matrix factorisation algorithm …
Adds recursion to deep learning frameworks for better handling of recursive data structures.
problem Lack of support for recursion in existing deep learning frameworks.
method Complements existing frameworks with recursive execution of dataflow graphs and APIs for recursive definitions.
result Recursive implementation reduces training and inference time by more effectively using resources.
Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.
problem Accurately learning system dynamics in complex, multi-channel systems with nonlinear and noisy data.
method Formulates system as Gaussian process state-space models (GPSSMs), introduces heterogeneous multi-output kernel, and develops recursive inference framework.
result Matches SOTA offline GPSSMs in accuracy with 1/100 runtime, and outperforms SOTA online GPSSMs by 70% in accuracy under noise with 1/20 runtime.
Cardinality potentials are a generally useful class of high order potential that affect probabilities based on how many of D binary variables are active. Maximum a posteriori (MAP) inference for cardinality potential models is well-understood, with efficient computations taking O(DlogD) time. Yet efficient marginalizat…
RGPs connect predictive coding to Bayesian inference, providing a neural substrate.
problem Scalable implementations of Bayesian inference respecting neurobiological constraints.
method Formal connection between predictive coding and Recursive Gaussian Processes (RGPs).
result RGPs intrinsically implement hierarchical Bayesian inference and uncertainty propagation.
New method speeds up Gaussian process inference for large datasets.
problem Numerical instability and inefficiency in approximate inference methods for non-Gaussian likelihoods.
method Conjugate-computation variational inference with Kalman recursions.
result Linear-time inference with fast and stable variational inference for state-space GP models.
Unified approach to path planning using probabilistic inference on factor graphs.
problem Path planning problems using probabilistic inference.
method Unified framework using probabilistic factor graphs and message composition rules.
result Unified approach includes various algorithms like Sum-product, Max-product, Dynamic programming, and mixed criteria.
RDIS solves nonconvex optimization problems faster than gradient descent.
problem Nonconvex optimization problems in AI.
method Recursive decomposition and combinatorial optimization.
result RDIS solves nonconvex optimization problems exponentially faster than gradient descent.
NTI models text structure without syntactic trees, improving NLP tasks.
problem Lack of robust syntactic parsing in recursive models.
method Bottom-up tree construction with attention mechanism.
result NTI outperforms state-of-the-art models on NLP tasks.
Bayesian method improves online NARMAX model identification.
problem Online identification of nonlinear systems with small sample sizes and low noise.
method Variational Bayesian inference using message passing algorithm for polynomial NARMAX models.
result Variational Bayesian estimator outperforms recursive and offline least-squares methods.
The paper challenges the use of decision trees for pointwise inference due to slow convergence rates.
problem The slow convergence rates of decision trees in uniform norm, especially with non-vanishing probability.
method Demonstrates the limitations of adaptive recursive partitioning and shows how random forests can improve performance.
result Decision trees can fail to achieve polynomial rates of convergence in uniform norm, even with pruning.
A new inference model improves training of deep VAEs.
problem Training deep VAEs with many layers is difficult.
method Ladder Variational Autoencoder, recursively correcting generative distribution.
result Improves predictive log-likelihood and lower bound.
New method for epidemic model inference using multinomial approximations.
problem Inference in stochastic epidemic models with partial observations.
method Recursive multinomial approximations to integrate over unobserved variables.
result Accuracy demonstrated through real and simulated data.
HINT improves invertible neural networks for better density estimation and Bayesian inference.
problem Sparse Jacobians limit expressiveness in invertible neural architectures.
method Recursive hierarchical coupling within subsets of variables leads to dense, triangular Jacobian.
result HINT allows efficient sampling from joint and posterior distributions using a single network.
CCI algorithm handles cycles, latent variables, and selection bias in causal discovery.
problem Cycles, latent variables, and selection bias in causal processes.
method CCI algorithm using a conditional independence oracle for cyclic, latent, and selection bias cases.
result CCI outperforms existing algorithms in cyclic cases and rivals them in acyclic cases.
AdaVol adapts QML for real-time GARCH volatility prediction.
problem Real-time estimation of GARCH volatility in streaming data.
method Adaptive recursive estimation routine with Variance Targeting Estimation.
result AdaVol provides a stable and adaptive method for real-life data.
The paper analyzes SGD with dropout regularization in linear models, proving asymptotic properties and providing inference tools.
problem Analyzing the behavior of SGD with dropout regularization in linear models.
method Establishing geometric-moment contraction (GMC) and proving quenched central limit theorems (CLT).
result The existence of a unique stationary distribution and asymptotic normality results for SGD with dropout.
We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It applies to Turing-complete probabilistic programming languages and supports accurate inference in models …
Proposes an active RBI framework using Rényi information measures for more informed decision-making.
problem Optimal latent variable estimates in real-time settings with streaming noisy observations.
method Unified inference and query selection steps through Rényi entropy and α-divergence; new objective called Momentum for exploration.
result Analytically demonstrates superior performance compared to conventional methods like mutual information.
Bayesian structure learning for high-dimensional data using recursive bootstrap.
problem Bayesian structure learning for domains with hundreds of variables.
method Non-parametric bootstrap, recursive structure learning, combining bootstrap with constraint-based learning.
result The proposed method learns better MAP models and more reliable causal relationships than other state-of-the-art methods.
Adaptive learning of SPDE solutions using score-based diffusion models.
problem Model errors and reduced accuracy in SPDE solutions due to incomplete physical knowledge and environmental variability.
method Score-based diffusion models with recursive Bayesian inference, incorporating simulation data and observational information.
result Accuracy and robustness of the proposed method demonstrated on benchmark SPDEs.
BBRT improves molecular properties through iterative translation.
problem Optimizing molecular structures for improved biochemical properties.
method Iterative translation of molecules using a black box approach.
result Improvement in molecular properties with each iteration of the translator.
Causal trees struggle with accuracy in estimating treatment effects.
problem Estimating heterogeneous causal treatment effects using recursive decision trees.
method Adaptive recursive partitioning with and without sample splitting.
result Causal tree estimators can have uniform-norm errors decreasing more slowly than any power of the sample size.
Belief Propagation outperforms other algorithms in reconstructing binary symmetric channel trees.
problem Reconstructing binary symmetric channel trees with bounded memory.
method Combining recursive reconstruction, information theory, and optimal transport.
result Any recursive algorithm with bounded memory for the reconstruction problem on binary symmetric channel trees has a phase transition strictly below the Belief Propagation threshold.
Score-based martingale posteriors improve uncertainty quantification in deep neural networks.
problem Uncertainty quantification in deep neural networks
method Score-based martingale posteriors
result SMPs provide a fast, deterministic way to simulate the limiting random variable.
Extends GP models for sequential data, scalable and robust.
problem Handling sequential input-output observations in multi-task settings.
method Variational inference with sparse approximations and recursive GP priors.
result Tractable continual learning with KL divergences and recursive reconstruction.
Paper introduces v-CMC linking causality and utility.
problem Linking causality and utility for value theory.
method Developed a new causal independence principle (v-CMC) and proved its equivalence.
result Equivalence of local, global, and decomposition versions of v-CMC.
A new method for online VI in SSMs using asymptotic contrast.
problem Lack of functionality for streaming data in standard VI methods for SSMs.
method Propose maximising an IWAE-type variational lower bound on the asymptotic contrast function using stochastic approximation.
result OSIWAE allows for online learning of model parameters and latent states.
New framework improves robust inference in HMMs under model misspecification.
problem Inference in general state-space HMMs under likelihood misspecification.
method Generalized Bayesian Inference (GBI) and Sequential Monte Carlo (SMC) methods.
result Improved performance in object tracking and Gaussian process regression.
Warped DLMs improve forecasting for count time series.
problem Limited options for modeling count time series data.
method Introduces a semiparametric methodology using warping of Gaussian DLMs.
result Demonstrates improved forecasting capabilities for count time series.
Paper defines Farey Recursive Functions and explores their properties.
problem Understanding recursive functions on rationals.
method Defined and studied Farey Recursive Functions using Farey graph.
result Farey Recursive Functions naturally connect to 2-bridge knots and links.
The paper explores generalizations of Mirzakhani's recursion and computes volumes for physical gravity models.
problem Computing volumes for physical gravity models.
method Topological recursion and physical two-dimensional gravity models.
result Derivation of Virasoro constraints and cut-and-join equations for generalized Mirzakhani's recursions.
Proposes a new Gaussian factor for probabilistic inference with degenerate settings.
problem Handling linear dependencies among random variables in Gaussian networks.
method Introduces a parametrised factor that relaxes the positive-definite constraint of the covariance matrix.
result Accurately accommodates degeneracies in probabilistic inference without significant computational overhead.
Paper proves 1-point recursions for various enumerative problems.
problem Enumerating gluings of polygons to orientable surfaces.
method Proves existence of 1-point recursions for specific classes of problems.
result Recover Harer-Zagier recursion and prove existence for new problems.
The paper develops bootstrap methods for ACD models with random durations.
problem Bootstrap inference for autoregressive duration models with random durations.
method Recursive schemes for fixed calendar span or realized event count.
result The bootstrap method reproduces the conditional Gaussian component for ACD models with 0<κ<1. Tab-TRM uses recursive model for insurance pricing on tabular data.
problem Insurance pricing on tabular data.
method Adapts recursive latent reasoning to insurance modeling using a compact, parameter-efficient network.
result Improves insurance pricing accuracy using iterative refinement of latent tokens.
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 recursion formula for non-orientable surfaces resolves divergences.
problem Computing volumes of moduli spaces for non-orientable surfaces.
method Generalization of Mirzakhani's recursion to non-orientable surfaces, handling divergences with integral kernels.
result Regularized volumes can be computed with a cutoff on crosscap size.
This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.
problem Improving learning dynamics in recursive ensemble learning.
method Develops second-order recursive architectures with Fibonacci-type update flows.
result Establishes global convergence conditions and generalization bounds for recursive ensembles.
Derives a recursion formula for irregular spectral curves.
problem Calculating the mean of irregular spectral curves.
method Variant of replica method by Brezín and Hikami, generalized to generalized Laguerre polynomial case.
result Derives a recursion formula for special times where terms are polynomials.
The article improves prediction by aggregating Kalman recursions online.
problem Improving expert aggregation in prediction models.
method Using exponential weights and state-space models to aggregate Kalman recursions.
result New algorithms outperform existing methods in Kalman recursion expert aggregation.
Geometric recursion constructs measurable functions on moduli spaces.
problem Constructing measurable functions on moduli spaces of bordered Riemann surfaces.
method Inductive construction via excisions of pairs of pants, with convergence conditions.
result Geometric recursion produces functions that can be integrated with respect to the Weil-Petersson measure.
Solves a recursion for Gromov-Witten invariants of the unknot.
problem Determining Gromov-Witten invariants for a specific Lagrangian brane.
method Uses a skein-theoretic recursion and geometric solutions.
result Solves the recursion to find the expected hook-content formula.
Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.
problem Efficient inference for large-scale time series data.
method Combining inducing variables with Kalman filter-like recursions for linear scaling.
result General site-based approach for approximating non-Gaussian likelihoods.