no code implementations • 21 Jun 2024 • Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott

While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem.

no code implementations • 1 May 2024 • Matt Raymond, Angela Violi, Clayton Scott

Finally, we demonstrate the performance of JOPLEn on 153 regression and classification datasets and with a variety of penalties.

no code implementations • 21 Mar 2024 • Matt Raymond, Jacob Charles Saldinger, Paolo Elvati, Clayton Scott, Angela Violi

Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging.

no code implementations • 29 Nov 2023 • Yutong Wang, Clayton Scott

The notion of margin loss has been central to the development and analysis of algorithms for binary classification.

no code implementations • 29 Jul 2023 • YIlun Zhu, Clayton Scott, Darren Holland, George Landon, Aaron Fjeldsted, Azaree Lintereur

Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources.

1 code implementation • 2 Jun 2023 • YIlun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott

The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture.

no code implementations • 31 May 2023 • Jianxin Zhang, Clayton Scott

Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label.

1 code implementation • 4 Mar 2022 • Jianxin Zhang, Yutong Wang, Clayton Scott

Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels.

no code implementations • 10 Nov 2020 • Alexander Ritchie, Laura Balzano, Daniel Kessler, Chandra S. Sripada, Clayton Scott

Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more useful for a prediction task of interest.

1 code implementation • NeurIPS 2020 • Alexander Ritchie, Robert A. Vandermeulen, Clayton Scott

Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations.

1 code implementation • NeurIPS 2020 • Clayton Scott, Jianxin Zhang

Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with the proportion of each class occurring in that bag.

no code implementations • 28 May 2020 • Han Bao, Clayton Scott, Masashi Sugiyama

Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns.

no code implementations • 10 Oct 2019 • Clayton Scott, Jianxin Zhang

We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruption, and introduce an approach based on minimizing a weighted combination of corruption-corrected empirical risks.

no code implementations • 23 Sep 2019 • Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott

This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement.

no code implementations • 24 May 2019 • Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W. Cutler, Clayton Scott

Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided.

no code implementations • 17 Oct 2018 • Aniket Anand Deshmukh, Srinagesh Sharma, James W. Cutler, Mark Moldwin, Clayton Scott

Contextual bandits are a sub-class of MABs where, at every time step, the learner has access to side information that is predictive of the best arm.

no code implementations • 3 Oct 2018 • Clayton Scott

In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source.

2 code implementations • 21 Nov 2017 • Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott

In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner.

no code implementations • 6 Oct 2017 • Gopal Nataraj, Jon-Fredrik Nielsen, Clayton Scott, Jeffrey A. Fessler

This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK).

no code implementations • 30 Sep 2017 • Julian Katz-Samuels, Gilles Blanchard, Clayton Scott

Many machine learning problems can be characterized by mutual contamination models.

no code implementations • 24 May 2017 • Julian Katz-Samuels, Clayton Scott

We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user rating matrix, the goal is to recover the \emph{personalized ranking} of each user over all of the items.

no code implementations • 24 May 2017 • Efrén Cruz Cortés, Clayton Scott

Consistency of the kernel density estimator requires that the kernel bandwidth tends to zero as the sample size grows.

no code implementations • NeurIPS 2017 • Aniket Anand Deshmukh, Urun Dogan, Clayton Scott

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications.

no code implementations • 5 Jan 2017 • Max Yi Ren, Clayton Scott

In this work, we (1) demonstrate that accurate preference estimation is neither necessary nor sufficient for identifying the optimal design, (2) introduce a novel adaptive questionnaire that leverages knowledge about engineering feasibility and manufacturing costs to directly determine the optimal design, and (3) interpret product design in terms of a nonlinear segmentation of part-worth space, and use this interpretation to illuminate the intrinsic difficulty of optimal design in the presence of noisy questionnaire responses.

no code implementations • 8 Mar 2016 • Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari

Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component.

no code implementations • 19 Feb 2016 • Julian Katz-Samuels, Clayton Scott

We examine the decontamination problem in two mutual contamination models that describe popular machine learning tasks: recovering the base distributions up to a permutation in a mixed membership model, and recovering the base distributions exactly in a partial label model for classification.

no code implementations • 15 Jan 2016 • Hossein Keshavarz, Clayton Scott, XuanLong Nguyen

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

no code implementations • 3 Jun 2015 • Hossein Keshavarz, Clayton Scott, XuanLong Nguyen

By contrast, the standard CUSUM method, which does not account for the covariance structure, is shown to be asymptotically optimal only in the increasing domain.

no code implementations • 21 Oct 2013 • Takanori Watanabe, Daniel Kessler, Clayton Scott, Michael Angstadt, Chandra Sripada

Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection.

no code implementations • 21 Jun 2013 • Tyler Sanderson, Clayton Scott

The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class.

no code implementations • 5 Mar 2013 • Gilles Blanchard, Marek Flaska, Gregory Handy, Sara Pozzi, Clayton Scott

For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions.

no code implementations • NeurIPS 2011 • Gilles Blanchard, Gyemin Lee, Clayton Scott

We develop a distribution-free, kernel-based approach to the problem.

no code implementations • NeurIPS 2010 • Gowtham Bellala, Suresh Bhavnani, Clayton Scott

Generalized Binary Search (GBS) is a well known greedy algorithm for identifying an unknown object while minimizing the number of yes" or "no" questions posed about that object, and arises in problems such as active learning and active diagnosis.

no code implementations • NeurIPS 2008 • Jooseuk Kim, Clayton Scott

We provide statistical performance guarantees for a recently introduced kernel classifier that optimizes the $L_2$ or integrated squared error (ISE) of a difference of densities.

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