Search Results for author:
Found 143 papers, 71 papers with code
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass.
Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
Generative models have the potential to accelerate key steps in the discovery of novel molecular therapeutics and materials.
EvIL: Evolution Strategies for Generalisable Imitation Learning
Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e. g. demonstrations collected in simulation but deployment in the real world).
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning.
Ranked #1 on
Few-Shot Image Classification
on Meta-Dataset
Online Adaptation of Language Models with a Memory of Amortized Contexts
We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank.
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation.
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale.
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
The prevailing theoretical understanding is that this can then be viewed as online reinforcement learning in an approximate dynamics model, and any remaining gap is therefore assumed to be due to the imperfect dynamics model.
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Recognizing that the primary object of interest in most settings is the distribution over functions induced by the posterior distribution over neural network parameters, we frame Bayesian inference in neural networks explicitly as inferring a posterior distribution over functions and propose a scalable function-space variational inference method that allows incorporating prior information and results in reliable predictive uncertainty estimates.
Continual Learning via Sequential Function-Space Variational Inference
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data.
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role.
Geometric Neural Diffusion Processes
In this work, we extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling.
Kalman Filter for Online Classification of Non-Stationary Data
Non-stationarity over the linear predictor weights is modelled using a parameter drift transition density, parametrized by a coefficient that quantifies forgetting.
Incorporating Unlabelled Data into Bayesian Neural Networks
Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions.
Synthetic Experience Replay
We believe that synthetic training data could open the door to realizing the full potential of deep learning for replay-based RL algorithms from limited data.
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
Skip connections and normalisation layers form two standard architectural components that are ubiquitous for the training of Deep Neural Networks (DNNs), but whose precise roles are poorly understood.
Modality-Agnostic Variational Compression of Implicit Neural Representations
We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR).
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks.
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
Riemannian Diffusion Schrödinger Bridge
Our proposed method generalizes Diffusion Schr\"odinger Bridge introduced in \cite{debortoli2021neurips} to the non-Euclidean setting and extends Riemannian score-based models beyond the first time reversal.
When Does Re-initialization Work?
However, when deployed alongside other carefully tuned regularization techniques, re-initialization methods offer little to no added benefit for generalization, although optimal generalization performance becomes less sensitive to the choice of learning rate and weight decay hyperparameters.
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain.
Conformal Off-Policy Prediction in Contextual Bandits
Most off-policy evaluation methods for contextual bandits have focused on the expected outcome of a policy, which is estimated via methods that at best provide only asymptotic guarantees.
Learning Instance-Specific Augmentations by Capturing Local Invariances
We introduce InstaAug, a method for automatically learning input-specific augmentations from data.
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks.
Meta-Learning Sparse Compression Networks
Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal.
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
To that end, we study a Bayesian reformulation of INRs, UncertaINR, in the context of computed tomography, and evaluate several Bayesian deep learning implementations in terms of accuracy and calibration.
Riemannian Score-Based Generative Modelling
Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance.
COIN++: Neural Compression Across Modalities
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities.
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.
Powerpropagation: A sparsity inducing weight reparameterisation
The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling the effective scaling up of models.
On Incorporating Inductive Biases into VAEs
InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es).
On Contrastive Representations of Stochastic Processes
Learning representations of stochastic processes is an emerging problem in machine learning with applications from meta-learning to physical object models to time series.
Group Equivariant Subsampling
We use these layers to construct group equivariant autoencoders (GAEs) that allow us to learn low-dimensional equivariant representations.
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging.
COIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image.
Generative Models as Distributions of Functions
By treating data points as functions, we can abstract away from the specific type of data we train on and construct models that are agnostic to discretization.
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing.
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Motivated by objects such as electric fields or fluid streams, we study the problem of learning stochastic fields, i. e. stochastic processes whose samples are fields like those occurring in physics and engineering.
Amortized Probabilistic Detection of Communities in Graphs
While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.
Behavior Priors for Efficient Reinforcement Learning
In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.
Importance Weighted Policy Learning and Adaptation
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones.
Bootstrapping Neural Processes
While this "data-driven" way of learning stochastic processes has proven to handle various types of data, NPs still rely on an assumption that uncertainty in stochastic processes is modeled by a single latent variable, which potentially limits the flexibility.
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make?
Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning
We analyse the pruning procedure behind the lottery ticket hypothesis arXiv:1803. 03635v5, iterative magnitude pruning (IMP), when applied to linear models trained by gradient flow.
Bayesian Deep Ensembles via the Neural Tangent Kernel
We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs).
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
On a variety of classification tasks and modern architecture search spaces, we show that the resulting ensembles outperform deep ensembles not only in terms of accuracy but also uncertainty calibration and robustness to dataset shift.
Multiplicative Interactions and Where to Find Them
We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects.
Uncertainty Estimation Using a Single Deep Deterministic Neural Network
We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass.
Robust Pruning at Initialization
Overparameterized Neural Networks (NN) display state-of-the-art performance.
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Stochastic gradient descent with momentum (SGDm) is one of the most popular optimization algorithms in deep learning.
MetaFun: Meta-Learning with Iterative Functional Updates
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one.
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED).
Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially.
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes.
Stacked Capsule Autoencoders
In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.
Ranked #3 on
Unsupervised MNIST
on MNIST
Random Tessellation Forests
The Ostomachion process and the self-consistent binary space partitioning-tree process were recently introduced as generalizations of the Mondrian process for space partitioning with non-axis aligned cuts in the two dimensional plane.
Task Agnostic Continual Learning via Meta Learning
One particular formalism that studies learning under non-stationary distribution is provided by continual learning, where the non-stationarity is imposed by a sequence of distinct tasks.
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods.
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
Current meta-learning approaches focus on learning functional representations of relationships between variables, i. e. on estimating conditional expectations in regression.
Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.
Meta reinforcement learning as task inference
This includes proposals to learn the learning algorithm itself, an idea also known as meta learning.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Information asymmetry in KL-regularized RL
In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.
Infinitely Deep Infinite-Width Networks
Infinite-width neural networks have been extensively used to study the theoretical properties underlying the extraordinary empirical success of standard, finite-width neural networks.
Revisiting Reweighted Wake-Sleep
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn.
Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent.
Ranked #21 on
Image Classification
on MNIST
Meta-Learning surrogate models for sequential decision making
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important.
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources.
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i. e. a normalizing flow).
Functional Regularisation for Continual Learning with Gaussian Processes
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network.
Probabilistic symmetries and invariant neural networks
Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group.
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
We therefore endow VAEs with a Poincar\'e ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space.
Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior.
Stochastic Expectation Maximization with Variance Reduction
However, sEM has a slower asymptotic convergence rate than batch EM, and requires a decreasing sequence of step sizes, which is difficult to tune.
Neural probabilistic motor primitives for humanoid control
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids.
A Statistical Approach to Assessing Neural Network Robustness
Furthermore, it provides an ability to scale to larger networks than formal verification approaches.
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation.
Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data.
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
Hamiltonian Descent Methods
Yet, crucially the kinetic gradient map can be designed to incorporate information about the convex conjugate in a fashion that allows for linear convergence on convex functions that may be non-smooth or non-strongly convex.
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
Empirical evidence suggests that heavy-tailed degree distributions occurring in many real networks are well-approximated by power laws with exponents $\eta$ that may take values either less than and greater than two.
Conditional Neural Processes
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Neural Processes
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.
Mix & Match - Agent Curricula for Reinforcement Learning
We introduce Mix and match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents.
Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods.
Controllable Semantic Image Inpainting
We develop a method for user-controllable semantic image inpainting: Given an arbitrary set of observed pixels, the unobserved pixels can be imputed in a user-controllable range of possibilities, each of which is semantically coherent and locally consistent with the observed pixels.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects.
Mix&Match - Agent Curricula for Reinforcement Learning
(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables.
Progress & Compress: A scalable framework for continual learning
This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.
Causal Inference via Kernel Deviance Measures
Discovering the causal structure among a set of variables is a fundamental problem in many areas of science.
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
We propose a novel class of network models for temporal dyadic interaction data.
An Analysis of Categorical Distributional Reinforcement Learning
Distributional approaches to value-based reinforcement learning model the entire distribution of returns, rather than just their expected values, and have recently been shown to yield state-of-the-art empirical performance.
Distributional Reinforcement Learning
reinforcement-learning
+1
Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator.
Faithful Inversion of Generative Models for Effective Amortized Inference
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Non-exchangeable random partition models for microclustering
Along with this result, we provide the asymptotic behaviour of the number of clusters of a given size, and show that the model can exhibit a power-law behavior, controlled by another parameter.
Distral: Robust Multitask Reinforcement Learning
Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes
Automating statistical modelling is a challenging problem in artificial intelligence.
Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results.
Particle Value Functions
The policy gradients of the expected return objective can react slowly to rare rewards.
Poisson Random Fields for Dynamic Feature Models
We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features.
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election.
Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior
In this paper, we prove almost surely consistency of a Survival Analysis model, which puts a Gaussian process, mapped to the unit interval, as a prior on the so-called hazard function.
Statistics Theory Statistics Theory
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution.
A nonparametric HMM for genetic imputation and coalescent inference
We develop a new nonparametric model of genetic sequence data, based on the hierarchical Dirichlet process, which supports these self transitions and nonhomogeneity.
Gaussian Processes for Survival Analysis
We introduce a semi-parametric Bayesian model for survival analysis.
Poisson intensity estimation with reproducing kernels
However, we prove that the representer theorem does hold in an appropriately transformed RKHS, guaranteeing that the optimization of the penalized likelihood can be cast as a tractable finite-dimensional problem.
Relativistic Monte Carlo
Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
A characterization of product-form exchangeable feature probability functions
We characterize the class of exchangeable feature allocations assigning probability $V_{n, k}\prod_{l=1}^{k}W_{m_{l}}U_{n-m_{l}}$ to a feature allocation of $n$ individuals, displaying $k$ features with counts $(m_{1},\ldots, m_{k})$ for these features.
Bayesian Nonparametrics for Sparse Dynamic Networks
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
The Mondrian Kernel
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel.
Collaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression.
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics.
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data.
The Mondrian Process for Machine Learning
We outline a slight adaptation of this algorithm to regression, as the remainder of the report uses regression as a case study of how Mondrian processes can be utilized in machine learning.
Expectation Particle Belief Propagation
The computational complexity of our algorithm at each iteration is quadratic in the number of particles.
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Many real-world regression problems demand a measure of the uncertainty associated with each prediction.
Particle Gibbs for Bayesian Additive Regression Trees
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression.
Distributed Bayesian Posterior Sampling via Moment Sharing
We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation.
Consistency and fluctuations for stochastic gradient Langevin dynamics
Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally expensive.
Bayesian Nonparametric Crowdsourcing
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets.
A marginal sampler for $σ$-Stable Poisson-Kingman mixture models
We investigate the class of $\sigma$-stable Poisson-Kingman random probability measures (RPMs) in the context of Bayesian nonparametric mixture modeling.
Asynchronous Anytime Sequential Monte Carlo
We introduce a new sequential Monte Carlo algorithm we call the particle cascade.
Mondrian Forests: Efficient Online Random Forests
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics.
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis.
Bayesian Hierarchical Community Discovery
We propose an efficient Bayesian nonparametric model for discovering hierarchical community structure in social networks.
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
For the representer theorem to hold, the linear functionals are required to be bounded in the RKHS, and we show that this is true for a variety of commonly used RKHS and invariances.
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
In this paper we investigate the use of Langevin Monte Carlo methods on the probability simplex and propose a new method, Stochastic gradient Riemannian Langevin dynamics, which is simple to implement and can be applied online.
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators.
Top-down particle filtering for Bayesian decision trees
Unlike classic decision tree learning algorithms like ID3, C4. 5 and CART, which work in a top-down manner, existing Bayesian algorithms produce an approximation to the posterior distribution by evolving a complete tree (or collection thereof) iteratively via local Monte Carlo modifications to the structure of the tree, e. g., using Markov chain Monte Carlo (MCMC).
Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes
In this paper we propose a Bayesian nonparametric model for clustering partial ranking data.
On Smoothing and Inference for Topic Models
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches.
