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07/06/2023
When Does Confidence-Based Cascade Deferral Suffice?
Cascades are a classical strategy to enable inference cost to vary adapt...
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01/29/2023
Learning to reject meets OOD detection: Are all abstentions created equal?
Learning to reject (L2R) and out-of-distribution (OOD) detection are two...
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01/27/2023
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art perf...
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08/05/2022
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch
We address the problem of retrieving images with both a sketch and a tex...
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04/27/2022
ELM: Embedding and Logit Margins for Long-Tail Learning
Long-tail learning is the problem of learning under skewed label distrib...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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02/10/2021
An Optimal Witness Function for Two-Sample Testing
We propose data-dependent test statistics based on a one-dimensional wit...
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06/12/2020
Kernel Distributionally Robust Optimization
This paper is an in-depth investigation of using kernel methods to immun...
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06/03/2020
Learning Kernel Tests Without Data Splitting
Modern large-scale kernel-based tests such as maximum mean discrepancy (...
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03/31/2020
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
In order to anticipate rare and impactful events, we propose to quantify...
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02/24/2020
Testing Goodness of Fit of Conditional Density Models with Kernels
We propose two nonparametric statistical tests of goodness of fit for co...
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02/21/2020
Kernel Conditional Moment Test via Maximum Moment Restriction
We propose a new family of specification tests called kernel conditional...
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10/29/2019
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
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10/27/2019
Kernel Stein Tests for Multiple Model Comparison
We address the problem of non-parametric multiple model comparison: give...
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10/14/2019
More Powerful Selective Kernel Tests for Feature Selection
Refining one's hypotheses in the light of data is a commonplace scientif...
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10/11/2019
ABCDP: Approximate Bayesian Computation Meets Differential Privacy
We develop a novel approximate Bayesian computation (ABC) framework, ABC...
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07/01/2019
A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernel-based test to assess the relative goo...
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05/14/2019
Kernel Mean Matching for Content Addressability of GANs
We propose a novel procedure which adds "content-addressability" to any ...
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01/26/2019
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
Modern implicit generative models such as generative adversarial network...
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10/27/2018
Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address...
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05/18/2018
Model Inference with Stein Density Ratio Estimation
The Kullback-Leilber divergence from model to data is a classic goodness...
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05/22/2017
A Linear-Time Kernel Goodness-of-Fit Test
We propose a novel adaptive test of goodness-of-fit, with computational ...
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10/15/2016
An Adaptive Test of Independence with Analytic Kernel Embeddings
A new computationally efficient dependence measure, and an adaptive stat...
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05/22/2016
Interpretable Distribution Features with Maximum Testing Power
Two semimetrics on probability distributions are proposed, given as the ...
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03/09/2015
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message op...
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02/09/2015
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Complicated generative models often result in a situation where computin...
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01/02/2015
Passing Expectation Propagation Messages with Kernel Methods
We propose to learn a kernel-based message operator which takes as input...
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10/24/2014
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probab...
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10/06/2012
Feature Selection via L1-Penalized Squared-Loss Mutual Information
Feature selection is a technique to screen out less important features. ...
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02/02/2012
