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Found 26 papers, 11 papers with code
Multi-fidelity Gaussian Process Bandit Optimisation
However, in many cases, cheap approximations to $f$ may be obtainable.
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
However, in many cases, cheap approximations to $\func$ may be obtainable.
Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion
Objective and subjective evaluations show that \textit{Phoneme Hallucinator} outperforms existing VC methods for both intelligibility and speaker similarity.
NRTSI: Non-Recurrent Time Series Imputation
Time series imputation is a fundamental task for understanding time series with missing data.
Flow Models for Arbitrary Conditional Likelihoods
Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks.
Meta-Curvature
We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation.
Arbitrary Conditional Distributions with Energy
A more general and useful problem is arbitrary conditional density estimation, which aims to model any possible conditional distribution over a set of covariates, reflecting the more realistic setting of inference based on prior knowledge.
Distribution-based Sketching of Single-Cell Samples
Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort.
The Statistical Recurrent Unit
Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications.
Adversarial Scrubbing of Demographic Information for Text Classification
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.
Posterior Matching for Arbitrary Conditioning
Arbitrary conditioning is an important problem in unsupervised learning, where we seek to model the conditional densities $p(\mathbf{x}_u \mid \mathbf{x}_o)$ that underly some data, for all possible non-intersecting subsets $o, u \subset \{1, \dots , d\}$.
Transformation Autoregressive Networks
Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance.
Ranked #1 on
Density Estimation
on BSDS300
Recurrent Estimation of Distributions
After, an RNN is used to compute the conditional distributions of the latent covariates.
Deep Mean Maps
The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision.
Linear-time Learning on Distributions with Approximate Kernel Embeddings
This work develops the first random features for pdfs whose dot product approximates kernels using these non-Euclidean metrics, allowing estimators using such kernels to scale to large datasets by working in a primal space, without computing large Gram matrices.
Fast Distribution To Real Regression
We study the problem of distribution to real-value regression, where one aims to regress a mapping $f$ that takes in a distribution input covariate $P\in \mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and outputs a real-valued response $Y=f(P) + \epsilon$.
FuSSO: Functional Shrinkage and Selection Operator
We present the FuSSO, a functional analogue to the LASSO, that efficiently finds a sparse set of functional input covariates to regress a real-valued response against.
A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors).
Defense Through Diverse Directions
In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training.
Deep Goal-Oriented Clustering
Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively.
Dynamic Feature Acquisition with Arbitrary Conditional Flows
To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire.
Exchangeable Neural ODE for Set Modeling
Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements.
Active Feature Acquisition with Generative Surrogate Models
Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data.
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders
Deep Learning (DL) methods have dramatically increased in popularity in recent years.
Partially Observed Exchangeable Modeling
Modeling dependencies among features is fundamental for many machine learning tasks.
Towards Robust Active Feature Acquisition
Our framework can easily handle a large number of features using a hierarchical acquisition policy and is more robust to OOD inputs with the help of an OOD detector for partially observed data.
