Proposes CHDP for modeling cooperative hierarchical structures with Dirichlet processes.
problem Lack of flexible topic modeling for cooperative hierarchical structures.
method Introduces Cooperative Hierarchical Dirichlet Processes (CHDP) with superposition and maximization measures.
result Demonstrates improved modeling of cooperative hierarchical structures with CHDP.
New DFP model improves tree clustering and density estimation.
problem Modeling tree structures in data.
method Developed a novel Dirichlet fragmentation process (DFP) and hierarchical mixture model.
result DFP mixture model outperforms existing methods in clustering and density estimation.
We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent…
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
problem High nodal variance in BHMC trees, leading to weak separation between nodes at higher levels.
method Employing Posterior Regularization to impose max-margin constraints on nodes at every level.
result Improves cluster separation in BHMC models, enhancing overall model performance.
Dynamic topic model detects abnormal behavior in video sequences.
problem Detecting abnormal behavior in sequential video data.
method Dynamic Hierarchical Dirichlet Process with online inference algorithms.
result The dynamic model improves abnormal behavior detection performance.
Driven by the multi-level structure of human intracranial electroencephalogram (iEEG) recordings of epileptic seizures, we introduce a new variant of a hierarchical Dirichlet Process---the multi-level clustering hierarchical Dirichlet Process (MLC-HDP)---that simultaneously clusters datasets on multiple levels. Our sei…
HIRM models noisy, sparse, heterogeneous relational data using hierarchical clustering and Dirichlet processes.
problem Modeling noisy, sparse, and heterogeneous relational data.
method Hierarchical Chinese restaurant process and Dirichlet process mixture for clustering and modeling relation values.
result HIRM generalizes standard models and discovers relational structure in real-world datasets.
Proposes an exact slice sampler for HDP and its mixture models.
problem Challenges in sampling from Hierarchical Dirichlet Process (HDP) models.
method Bayesian variable augmentation to address hierarchical nature of HDPs, resulting in a full factorization of the joint distribution suitable for slice sampling.
result Fast mixing and natural truncation of infinite measures without ad-hoc modifications.
Bayesian nonparametric method for hierarchical clustering.
problem Hierarchical non-overlapping clustering of a dataset.
method Combining nCRP and HDP for complex latent mixture features.
result Solid empirical results compared to existing algorithms.
Spectral methods improve efficiency in nonparametric model inference.
problem Efficient inference for nonparametric models like IBP and HDP.
method Spectral methods for Indian Buffet Process and Hierarchical Dirichlet Process.
result Spectral methods provide computationally and statistically efficient inference.
Bayesian methods model diverse groups with censored data.
problem Pooling and analyzing small heterogeneous groups of time-to-event data.
method Three Bayesian nonparametric methods: Dirichlet process, hierarchical Dirichlet process, and nested Dirichlet process.
result Model accuracy comparison on simulated and real-world datasets.
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
A new model separates persistence and transition priors in HDP-HMM.
problem Limitation of sticky HDP-HMM in expressing different persistence strengths.
method Developed a disentangled sticky HDP-HMM (DS-HDP-HMM) with novel Gibbs sampling algorithms.
result DS-HDP-HMM outperforms sticky HDP-HMM and HDP-HMM on synthetic and real data.
Develops a more flexible HDP-HMM for temporal data segmentation.
problem Limited expressiveness of sticky HDP-HMM due to stationary self-persistence probability.
method Introduces recurrent sticky HDP-HMM with a novel Gibbs sampling strategy.
result RS-HDP-HMM outperforms other models in segmentation tasks.
Unified framework for DRO and DTA using Bayesian nonparametrics.
problem Combining DRO and DTA under ambiguity.
method Unified framework using DP and HDPs, with outlier robustness.
result Favorable performance in prediction accuracy and stability.
The hierarchical Dirichlet process (HDP) has become an important Bayesian nonparametric model for grouped data, such as document collections. The HDP is used to construct a flexible mixed-membership model where the number of components is determined by the data. As for most Bayesian nonparametric models, exact posterio…
Efficiently trains HDP topic models on large datasets using a sparse data-parallel sampler.
problem Scaling non-parametric topic models to large datasets.
method Data-parallel training with a doubly sparse sampler for HDP topic models.
result Trains HDP topic models on a 8m document, 768m token PubMed corpus in under 4 days.
Proposes a nonparametric tensor factorization for sparse data.
problem Handling sparse tensor data with structural and interpretability benefits.
method Hierarchical Gamma processes and Poisson random measures for tensor-valued process, Dirichlet processes for sampling entry indices, Gaussian processes for values.
result Demonstrates superior performance on benchmark datasets.
A new model sHDP adds smoothness constraints to HDP for evolving mixture densities.
problem Evolution of mixture densities in time-varying scenarios.
method Smoothed Hierarchical Dirichlet Process (sHDP) with temporal constraints.
result Inference algorithm and experimental validation on NIPS keywords.
A new model for multi-level non-parametric admixture modeling.
problem Non-parametric topic modeling with shared distributions across levels.
method Nested Hierarchical Dirichlet Processes (nHDP) with a multi-level Chinese Restaurant Franchise (nCRF) representation.
result Significantly better generalization and detection of missing author entities.
Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.
problem Classifying compositional data, especially in spam detection and color space identification.
method Hierarchical mixture of discriminative Generalized Dirichlet classifiers, using variational approximation for parameter learning.
result First time a variational upper-bound for Generalized Dirichlet mixture is proposed in literature.
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to in…
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, …
Enhances topic models to better handle polysemous words.
problem Lack of polysemy handling in Gaussian latent Dirichlet allocation.
method Introduces a hierarchical structure to capture polysemy in Gaussian latent Dirichlet allocation.
result Significantly improves polysemy detection and provides more parsimonious topic representations.
A new method uses online mixtures of tasks to improve meta-learning efficiency.
problem Difficulty in meta-learning when tasks are dissimilar or change over time.
method Proposes a Dirichlet process mixture of hierarchical Bayesian models for task-dependent hyperparameter selection.
result Better handles latent distribution shift on evolving few-shot learning benchmarks.
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
problem Clustering passenger trips with hierarchical and multi-dimensional data, especially in large-scale transportation systems.
method Tensor Dirichlet Process Multinomial Mixture (Tensor-DPMM) model, incorporating Dirichlet Process for automatic cluster number determination and tensor representation for multi-mode data.
result Automatic determination of the number of clusters and improved clustering quality.
Generative model improves semantic parsing across domains.
problem Robust semantic parsing across multiple domains.
method Probabilistic generative grammar with hierarchical Dirichlet processes.
result Efficient algorithms for training, parsing, and sentence generation.
In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a.k.a. the infinite HMM). We include derivations for the probabilities of certain CRP and CRF partitions, which are of more general…
Improved Bayesian network classifiers using HDPs for better parameter estimation.
problem Inaccurate parameter estimation in Bayesian network classifiers limits their performance.
method Hierarchical Dirichlet Processes (HDPs) for accurate parameter estimation.
result HDPs improve BNCs' performance, matching or outperforming Random Forest on categorical datasets.
A new method for efficient BNC parameter estimation outperforms HDP smoothing.
problem Efficiently estimating parameters for Bayesian network classifiers to match or exceed random forest performance.
method Uses log-linear regression to approximate hierarchical Dirichlet process (HDP) smoothing, making the approach simpler and faster.
result Our method outperforms HDP smoothing while being orders of magnitude faster and competitive with random forests.
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power …
Bayesian nonparametric model learns new categories without predefined limits.
problem Learning new categories unseen in labeled training data.
method Hierarchical Dirichlet process and latent Dirichlet allocation for automatic category inference.
result Nonparametric approach yields comparable performance to parametric methods with pre-specified new categories.
New model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
New method simplifies Bayesian inference for multi-Dirichlet priors.
problem Inference for models with hierarchical Multi-Dirichlet priors is tricky.
method Auxiliary variable scheme simplifies joint distribution of model parameters.
result Efficient inference schemes derived using the auxiliary variable scheme.
Enhances HDP-HMM for state transitions between similar states.
problem Improving state transition probabilities between related states.
method Defines a similarity function and scales transition probabilities by it, using a Markov Jump Process with conditional conjugacy.
result Achieves favorable comparisons to existing models on various tasks.
Introduces RW-HDP for community detection using random walks and nonparametric Bayesian methods.
problem Community detection in social networks.
method RW-HDP model combining random walks and Hierarchical Dirichlet Process.
result Automatic determination of community number and efficient inference.
Develops scalable model for learning velocity fields in complex traffic scenarios.
problem Learning heterogeneous and dynamic velocity fields in complex traffic scenarios.
method Nonparametric Bayesian modeling with hierarchical Dirichlet process and infinite hidden Markov model, Gaussian process prior, and scalable approximate inference.
result Demonstrates effective scalability and applicability to real-world traffic data.
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations.…
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particul…
Proposes a new model for clustering passenger trajectories with graphs.
problem Hierarchical trip structure, inaccurate clustering number, and lack of spatial semantic graphs.
method Tensor Dirichlet Process Multinomial Mixture model with graphs and a tensor version of Collapsed Gibbs Sampling.
result Automatic determination of the number of clusters and better cluster quality.
Efficient nonparametric Bayesian topic model for social media text.
problem Text analytics for social media data.
method Hierarchical Pitman-Yor processes for topic modeling.
result Nonparametric model outperforms existing parametric models.
Paper develops a method to learn language and acoustic models from unsegmented speech.
problem Learning language and acoustic models from continuous speech signals.
method Nonparametric Bayesian double articulation analyzer (NPB-DAA) using a hierarchical Dirichlet process hidden language model (HDP-HLM).
result NPB-DAA outperforms conventional methods in word acquisition and phoneme categorization tasks.
New algorithm for efficient topic modeling in sequential data.
problem Efficient topic modeling in sequential data.
method Stochastic collapsed variational inference algorithm for hidden Markov models and hierarchical Dirichlet process hidden Markov models.
result Our inference is more efficient and accurate than uncollapsed version.
Using nonparametric methods has been increasingly explored in Bayesian hierarchical modeling as a way to increase model flexibility. Although the field shows a lot of promise, inference in many models, including Hierachical Dirichlet Processes (HDP), remain prohibitively slow. One promising path forward is to exploit t…
We propose a restricted collapsed draw (RCD) sampler, a general Markov chain Monte Carlo sampler of simultaneous draws from a hierarchical Chinese restaurant process (HCRP) with restriction. Models that require simultaneous draws from a hierarchical Dirichlet process with restriction, such as infinite Hidden markov mod…
New model clusters online learning activity patterns.
problem Understanding and clustering online learning activity patterns.
method Hierarchical Dirichlet Hawkes process (HDHP) for continuous-time grouped streaming data.
result HDHP can recover meaningful learning patterns and track user interests over time.
End-to-end deep model for coherent probabilistic forecasts in hierarchical time series.
problem Hierarchical probabilistic forecasting for coherent predictions.
method Dirichlet proportions model for learning root and child distributions.
result Significant improvements over state-of-the-art baselines (up to 26%).
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…