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Found 19 papers, 5 papers with code
Denoising neural networks for magnetic resonance spectroscopy
Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals that are often obscured by strong interference that can be difficult to separate from the signal using traditional methods.
Understanding collections of related datasets using dependent MMD coresets
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets.
Balance is key: Private median splits yield high-utility random trees
Privatizing these queries typically comes at a high utility cost, in large part because we are privatizing queries on small subsets of the data, which are easily corrupted by added noise.
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow.
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions.
Avoiding Resentment Via Monotonic Fairness
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i. e. individuals with similar qualifications will receive different outcomes.
Sequential Edge Clustering in Temporal Multigraphs
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner.
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive.
A New Class of Time Dependent Latent Factor Models with Applications
These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc.
Stochastic Blockmodels with Edge Information
For example, in an email network, the volume of communication between two users and the content of that communication can give us information about both the strength and the nature of their relationship.
Large-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task.
Random clique covers for graphs with local density and global sparsity
Large real-world graphs tend to be sparse, but they often contain densely connected subgraphs and exhibit high clustering coefficients.
Methodology
Importance Weighted Generative Networks
Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution.
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models
First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference.
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
In this paper we present a novel algorithm to perform asymptotically exact parallel Markov chain Monte Carlo inference for Indian Buffet Process models.
Embarrassingly Parallel Inference for Gaussian Processes
Training Gaussian process-based models typically involves an $ O(N^3)$ computational bottleneck due to inverting the covariance matrix.
Variance Reduction in Stochastic Gradient Langevin Dynamics
In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient.
Dependent nonparametric trees for dynamic hierarchical clustering
Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets.
Restricting exchangeable nonparametric distributions
Distributions over exchangeable matrices with infinitely many columns are useful in constructing nonparametric latent variable models.
