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Claire Monteleoni

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11 papers

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ICML Conference 2024 Conference Paper

Position: Application-Driven Innovation in Machine Learning

David Rolnick
Alán Aspuru-Guzik
Sara Beery
Bistra Dilkina
Priya L. Donti
Marzyeh Ghassemi
Hannah Kerner
Claire Monteleoni

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offers the potential for significant impact not merely in domains of application but also in machine learning itself. In this paper, we describe the paradigm of application-driven research in machine learning, contrasting it with the more standard paradigm of methods-driven research. We illustrate the benefits of application-driven machine learning and how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation. We outline how these processes may be improved.

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IJCAI Conference 2017 Conference Paper

Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means

Mahesh Mohan
Claire Monteleoni

In this paper we present a framework for spectral clustering based on the following simple scheme: sample a subset of the input points, compute the clusters for the sampled subset using weighted kernel k-means (Dhillon et al. 2004) and use the resulting centers to compute a clustering for the remaining data points. For the case where the points are sampled uniformly at random without replacement, we show that the number of samples required depends mainly on the number of clusters and the diameter of the set of points in the kernel space. Experiments show that the proposed framework outperforms the approaches based on the Nystrom approximation both in terms of accuracy and computation time.

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TCS Journal 2016 Journal Article

Differentially-private learning of low dimensional manifolds

Anna Choromanska
Krzysztof Choromanski
Geetha Jagannathan
Claire Monteleoni

In this paper, we study the problem of differentially-private learning of low dimensional manifolds embedded in high dimensional spaces. The problems one faces in learning in high dimensional spaces are compounded in a differentially-private learning. We achieve the dual goals of learning the manifold while maintaining the privacy of the dataset by constructing a differentially-private data structure that adapts to the doubling dimension of the dataset. Our differentially-private manifold learning algorithm extends random projection trees of Dasgupta and Freund. A naive construction of differentially-private random projection trees could involve queries with high global sensitivity that would affect the usefulness of the trees. Instead, we present an alternate way of constructing differentially-private random projection trees that uses low sensitivity queries that are precise enough for learning the low dimensional manifolds. We prove that the size of the tree depends only on the doubling dimension of the dataset and not its extrinsic dimension.

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ICRA Conference 2015 Conference Paper

Environment selection and hierarchical place recognition

Mahesh Mohan
Dorian Gálvez-López
Claire Monteleoni
Gabe Sibley

As robots continue to create long-term maps, the amount of information that they need to handle increases over time. In terms of place recognition, this implies that the number of images being considered may increase until exceeding the computational resources of the robot. In this paper we consider a scenario where, given multiple independent large maps, possibly from different cities or locations, a robot must effectively and in real time decide whether it can localize itself in one of those known maps. Since the number of images to be handled by such a system is likely to be extremely large, we find that it is beneficial to decompose the set of images into independent groups or environments. This raises a new question: Given a query image, how do we select the best environment? This paper proposes a similarity criterion that can be used to solve this problem. It is based on the observation that, if each environment is described in terms of its co-occurrent features, similarity between environments can be established by comparing their co-occurrence matrices. We show that this leads to a novel place recognition algorithm that divides the collection of images into environments and arranges them in a hierarchy of inverted indices. By selecting first the relevant environment for the operating robot, we can reduce the number of images to perform the actual loop detection, reducing the execution time while preserving the accuracy. The practicality of this approach is shown through experimental results on several large datasets covering a combined distance of more than 750Km.

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AAAI Conference 2012 Conference Paper

Global Climate Model Tracking Using Geospatial Neighborhoods

Scott McQuade
Claire Monteleoni

A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models. Recent work in machine learning (Monteleoni et al. 2011) showed the promise of an algorithm for online learning with experts for this task. We extend the Tracking Climate Models (TCM) approach to (1) take into account climate model predictions at higher spatial resolutions and (2) to model geospatial neighborhood influence between regions. Our algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model used by TCM, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best expert (climate model) at a given location. In experiments on historical data at a variety of spatial resolutions, our algorithm demonstrates improvements over TCM, when tracking global temperature anomalies.

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JMLR Journal 2011 Journal Article

Differentially Private Empirical Risk Minimization

Kamalika Chaudhuri
Claire Monteleoni
Anand D. Sarwate

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε -differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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JMLR Journal 2009 Journal Article

Analysis of Perceptron-Based Active Learning

Sanjoy Dasgupta
Adam Tauman Kalai
Claire Monteleoni

We start by showing that in an active learning setting, the Perceptron algorithm needs Ω(1/ε 2 ) labels to learn linear separators within generalization error ε. We then present a simple active learning algorithm for this problem, which combines a modification of the Perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization error ε after asking for just Õ ( d log 1/ε) labels. This exponential improvement over the usual sample complexity of supervised learning had previously been demonstrated only for the computationally more complex query-by-committee algorithm. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

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NeurIPS Conference 2009 Conference Paper

Streaming k-means approximation

Nir Ailon
Ragesh Jaiswal
Claire Monteleoni

We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting. We make no assumptions about the data, and our algorithm is very light-weight in terms of memory, and computation. This setting is applicable to unsupervised learning on massive data sets, or resource-constrained devices. The two main ingredients of our theoretical work are: a derivation of an extremely simple pseudo-approximation batch algorithm for k-means, in which the algorithm is allowed to output more than k centers (based on the recent k-means++"), and a streaming clustering algorithm in which batch clustering algorithms are performed on small inputs (fitting in memory) and combined in a hierarchical manner. Empirical evaluations on real and simulated data reveal the practical utility of our method. "

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NeurIPS Conference 2008 Conference Paper

Privacy-preserving logistic regression

Kamalika Chaudhuri
Claire Monteleoni

This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. First we apply an idea of Dwork et al. to design a specific privacy-preserving machine learning algorithm, logistic regression. This involves bounding the sensitivity of logistic regression, and perturbing the learned classifier with noise proportional to the sensitivity. Noting that the approach of Dwork et al. has limitations when applied to other machine learning algorithms, we then present another privacy-preserving logistic regression algorithm. The algorithm is based on solving a perturbed objective, and does not depend on the sensitivity. We prove that our algorithm preserves privacy in the model due to Dwork et al. , and we provide a learning performance guarantee. Our work also reveals an interesting connection between regularization and privacy.

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NeurIPS Conference 2007 Conference Paper

A general agnostic active learning algorithm

Sanjoy Dasgupta
Daniel Hsu
Claire Monteleoni

We present an agnostic active learning algorithm for any hypothesis class of bounded VC dimension under arbitrary data distributions. Most previ- ous work on active learning either makes strong distributional assumptions, or else is computationally prohibitive. Our algorithm extends the simple scheme of Cohn, Atlas, and Ladner [1] to the agnostic setting, using re- ductions to supervised learning that harness generalization bounds in a simple but subtle manner. We provide a fall-back guarantee that bounds the algorithm’s label complexity by the agnostic PAC sample complexity. Our analysis yields asymptotic label complexity improvements for certain hypothesis classes and distributions. We also demonstrate improvements experimentally.

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NeurIPS Conference 2003 Conference Paper

Online Learning of Non-stationary Sequences

Claire Monteleoni
Tommi Jaakkola

We consider an online learning scenario in which the learner can make predictions on the basis of a ﬁxed set of experts. We derive upper and lower relative loss bounds for a class of universal learning algorithms in- volving a switching dynamics over the choice of the experts. On the basis of the performance bounds we provide the optimal a priori discretiza- tion for learning the parameter that governs the switching dynamics. We demonstrate the new algorithm in the context of wireless networks.

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