b'Clayton Scott'

research

∙ 06/02/2023

Mixture Proportion Estimation Beyond Irreducibility

The task of mixture proportion estimation (MPE) is to estimate the weigh...

0 YIlun Zhu, et al. ∙

research

∙ 05/31/2023

Label Embedding by Johnson-Lindenstrauss Matrices

We present a simple and scalable framework for extreme multiclass classi...

0 Jianxin Zhang, et al. ∙

research

∙ 03/04/2022

Learning from Label Proportions by Learning with Label Noise

Learning from label proportions (LLP) is a weakly supervised classificat...

0 Jianxin Zhang, et al. ∙

research

∙ 11/10/2020

Supervised PCA: A Multiobjective Approach

Methods for supervised principal component analysis (SPCA) aim to incorp...

0 Alexander Ritchie, et al. ∙

research

∙ 06/12/2020

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations

Recent research has established sufficient conditions for finite mixture...

0 Alexander Ritchie, et al. ∙

research

∙ 06/12/2020

Learning from Label Proportions: A Mutual Contamination Framework

Learning from label proportions (LLP) is a weakly supervised setting for...

0 Clayton Scott, et al. ∙

research

∙ 05/28/2020

Calibrated Surrogate Losses for Adversarially Robust Classification

Adversarially robust classification seeks a classifier that is insensiti...

0 Han Bao, et al. ∙

research

∙ 10/10/2019

Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions

We study binary classification in the setting where the learner is prese...

0 Clayton Scott, et al. ∙

research

∙ 09/23/2019

PAC Reinforcement Learning without Real-World Feedback

This work studies reinforcement learning in the Sim-to-Real setting, in ...

0 Yuren Zhong, et al. ∙

research

∙ 05/24/2019

A Generalization Error Bound for Multi-class Domain Generalization

Domain generalization is the problem of assigning labels to an unlabeled...

0 Aniket Anand Deshmukh, et al. ∙

research

∙ 11/30/2018

Local inversion-free estimation of spatial Gaussian processes

Maximizing the likelihood has been widely used for estimating the unknow...

0 Hossein Keshavarz, et al. ∙

research

∙ 10/17/2018

Simple Regret Minimization for Contextual Bandits

There are two variants of the classical multi-armed bandit (MAB) problem...

0 Aniket Anand Deshmukh, et al. ∙

research

∙ 10/03/2018

A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation

In the problem domain adaptation for binary classification, the learner ...

0 Clayton Scott, et al. ∙

research

∙ 11/21/2017

Domain Generalization by Marginal Transfer Learning

Domain generalization is the problem of assigning class labels to an unl...

0 Gilles Blanchard, et al. ∙

research

∙ 10/06/2017

Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels

This paper introduces a fast, general method for dictionary-free paramet...

0 Gopal Nataraj, et al. ∙

research

∙ 09/30/2017

Decontamination of Mutual Contamination Models

Many machine learning problems can be characterized by mutual contaminat...

0 Julian Katz-Samuels, et al. ∙

research

∙ 05/24/2017

Consistent Kernel Density Estimation with Non-Vanishing Bandwidth

Consistency of the kernel density estimator requires that the kernel ban...

0 Efrén Cruz Cortés, et al. ∙

research

∙ 05/24/2017

Nonparametric Preference Completion

We consider the task of collaborative preference completion: given a poo...

0 Julian Katz-Samuels, et al. ∙

research

∙ 05/24/2017

Multi-Task Learning for Contextual Bandits

Contextual bandits are a form of multi-armed bandit in which the agent h...

0 Aniket Anand Deshmukh, et al. ∙

research

∙ 01/05/2017

Adaptive Questionnaires for Direct Identification of Optimal Product Design

We consider the problem of identifying the most profitable product desig...

0 Max Yi Ren, et al. ∙

research

∙ 03/08/2016

Mixture Proportion Estimation via Kernel Embedding of Distributions

Mixture proportion estimation (MPE) is the problem of estimating the wei...

0 Harish G. Ramaswamy, et al. ∙

research

∙ 02/19/2016

A Mutual Contamination Analysis of Mixed Membership and Partial Label Models

Many machine learning problems can be characterized by mutual contaminat...

0 Julian Katz-Samuels, et al. ∙

research

∙ 01/15/2016

On the consistency of inversion-free parameter estimation for Gaussian random fields

Gaussian random fields are a powerful tool for modeling environmental pr...

0 Hossein Keshavarz, et al. ∙

research

∙ 06/03/2015

Optimal change point detection in Gaussian processes

We study the problem of detecting a change in the mean of one-dimensiona...

0 Hossein Keshavarz, et al. ∙

research

∙ 06/21/2013

Class Proportion Estimation with Application to Multiclass Anomaly Rejection

This work addresses two classification problems that fall under the head...

0 Tyler Sanderson, et al. ∙

research

∙ 03/05/2013

Classification with Asymmetric Label Noise: Consistency and Maximal Denoising

In many real-world classification problems, the labels of training examp...

0 Gilles Blanchard, et al. ∙

research

∙ 02/14/2012

Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks

The problem of active diagnosis arises in several applications such as d...

0 Gowtham Bellala, et al. ∙

research

∙ 09/14/2010

Calibrated Surrogate Losses for Classification with Label-Dependent Costs

We present surrogate regret bounds for arbitrary surrogate losses in the...

0 Clayton Scott, et al. ∙

research

∙ 11/24/2009

Group-based Query Learning for rapid diagnosis in time-critical situations

In query learning, the goal is to identify an unknown object while minim...

0 Gowtham Bellala, et al. ∙

Clayton Scott

Featured Co-authors