Search Results for author: Junier B. Oliva

Found 23 papers, 8 papers with code

Adversarial Scrubbing of Demographic Information for Text Classification

1 code implementation EMNLP 2021 Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier B. Oliva, Shashank Srivastava, Snigdha Chaturvedi

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.

Classification Text Classification

Towards Robust Active Feature Acquisition

no code implementations9 Jul 2021 Yang Li, Siyuan Shan, Qin Liu, Junier B. Oliva

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.

Partially Observed Exchangeable Modeling

no code implementations11 Feb 2021 Yang Li, Junier B. Oliva

Modeling dependencies among features is fundamental for many machine learning tasks.

Imputation

Arbitrary Conditional Distributions with Energy

1 code implementation NeurIPS 2021 Ryan R. Strauss, Junier B. Oliva

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.

Arbitrary Conditional Density Estimation Imputation

NRTSI: Non-Recurrent Time Series Imputation

1 code implementation5 Feb 2021 Siyuan Shan, Yang Li, Junier B. Oliva

Time series imputation is a fundamental task for understanding time series with missing data.

Imputation Time Series

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

no code implementations18 Jan 2021 David K. Lim, Naim U. Rashid, Junier B. Oliva, Joseph G. Ibrahim

However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs).

Active Feature Acquisition with Generative Surrogate Models

no code implementations6 Oct 2020 Yang Li, Junier B. Oliva

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data.

Exchangeable Neural ODE for Set Modeling

no code implementations NeurIPS 2020 Yang Li, Haidong Yi, Christopher M. Bender, Siyuan Shan, Junier B. Oliva

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.

Dynamic Feature Acquisition with Arbitrary Conditional Flows

no code implementations13 Jun 2020 Yang Li, Junier B. Oliva

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.

Deep Goal-Oriented Clustering

no code implementations7 Jun 2020 Yifeng Shi, Christopher M. Bender, Junier B. Oliva, Marc Niethammer

Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively.

Defense Through Diverse Directions

no code implementations ICML 2020 Christopher M. Bender, Yang Li, Yifeng Shi, Michael K. Reiter, Junier B. Oliva

In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training.

Adversarial Robustness

Flow Models for Arbitrary Conditional Likelihoods

1 code implementation13 Sep 2019 Yang Li, Shoaib Akbar, Junier B. Oliva

Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks.

Image Inpainting Imputation

Meta-Curvature

1 code implementation NeurIPS 2019 Eunbyung Park, Junier B. Oliva

We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation.

Few-Shot Image Classification General Classification

A Forest from the Trees: Generation through Neighborhoods

no code implementations4 Feb 2019 Yang Li, Tianxiang Gao, Junier B. Oliva

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors).

Transformation Autoregressive Networks

no code implementations ICML 2018 Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider

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.

Density Estimation Outlier Detection

The Statistical Recurrent Unit

2 code implementations ICML 2017 Junier B. Oliva, Barnabas Poczos, Jeff Schneider

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications.

Deep Mean Maps

no code implementations13 Nov 2015 Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff Schneider

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

no code implementations24 Sep 2015 Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider

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

no code implementations10 Nov 2013 Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

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

no code implementations10 Nov 2013 Junier B. Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.