Corinna Cortes
Corinna Cortes is a VP in Google Research, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs), for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from University of Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993.
Corinna is also a competitive and a mother of two.
Authored Publications
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A general framework for online learning with partial information is one where feedback graphs specify which losses can be observed by the learner. We study a challenging scenario where feedback graphs vary with time stochastically and,more importantly, where graphs and losses are stochastically dependent. This scenario appears in several applications that we describe. We give a new algorithm for this scenario that exploits the stochastic properties of the graphs and that benefits from favorable regret guarantees in this challenging setting. We present a detailed theoretical analysis of this algorithm, and also report the results of a series of experiments on real-world datasets, which show that our algorithm outperforms standard baselines for online learning with feedback graphs.
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We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and nonadaptive region-based active learning baselines.
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Understanding the Effects of Batching in Online Active Learning
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (2020)
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Online active learning (AL) algorithms often assume immediate access to a label once a query has been made. However, due to practical constraints, the labels of these queried examples are generally only available in ``batches''. In this work, we present a novel analysis for a generic class of batch online AL algorithms and reveal that the effects of batching are in fact mild and only result in an additional term in the label complexity that is linear in the batch size. To our knowledge, this provides the first theoretical justification for such algorithms and we show how they can be applied to batch variants of three canonical online AL algorithms: IWAL, ORIWAL, and DHM. We also conduct an empirical study that corroborates the novel theoretical insights.
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Relative deviation learning bounds and generalization with unbounded loss functions
Spencer Greenberg
Annals of Mathematics and Artificial Intelligence (2019)
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We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications. We also give detailed proofs of two-sided generalization bounds that hold in the general case of unbounded loss functions, under the assumption that a moment of the loss is bounded. We then illustrate how to apply these results in a sample application: the analysis of importance weighting.
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We present two novel extensions of an on-line importance weighted active learning algorithm IWAL, using the properties of disagreement values among hypotheses. The first extension, DIWAL, prunes the hypothesis set with a more aggressive strategy based on concentration bounds with disagreement values. We show that DIWAL improves the generalization performance and the label complexity of the original IWAL, and further quantify the improvement in terms of the disagreement graph coefficient. The second extension, ZOOM, adaptively zooms into the function space near the best-in-class hypothesis, which effectively reduces the best-in-class error and thus simultaneously improves the generalization performance and the label complexity. We report experimental results on multiple datasets and demonstrate that the proposed algorithms achieve better test performances than IWAL given the same amount of labeling budget.
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Adaptation Based on Generalized Discrepancy
Journal of Machine Learning Research, 20 (2019), pp. 1-30
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We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm, (DM), previously shown to outperform a number of algorithms for this problem. Unlike many previously proposed solutions for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, the reweighting depends on the hypothesis sought. The algorithm is derived from a less conservative notion of discrepancy than the DM algorithm called generalized discrepancy. We present a detailed description of our algorithm and show that it can be formulated as a convex optimization problem. We also give a detailed theoretical analysis of its learning guarantees which helps us select its parameters. Finally, we report the results of experiments demonstrating that it improves upon discrepancy minimization.
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We study a scenario of active learning where the input space is partitioned into different regions and where a distinct hypothesis is learned for each region. We first introduce a new active learning algorithm (EIWAL), which is an enhanced version of the IWAL algorithm, based on a finer analysis that results in more favorable learning guarantees. Then, we present a new learning algorithm for region-based active learning, ORIWAL, in which either IWAL or EIWAL serve as a subroutine. ORIWAL optimally allocates points to the subroutine algorithm for each region. We give a detailed theoretical analysis of ORIWAL, including generalization error guarantees and bounds on the number of points labeled, in terms of both the hypothesis set used in each region and the probability mass of that region. We also report the results of several experiments for our algorithm which demonstrate substantial benefits over existing non-region-based active learning algorithms, such as IWAL, and over passive learning.
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We consider the scenario of online learning with the sleeping experts where not all experts are available at each round and analyze the general framework of learning with stochastic feedback graphs, where loss observations associated to each expert are characterized by a graph, thereby including both the bandit and full information settings as special cases. A critical assumption in this framework is that the loss observations and the set of sleeping experts at each round is independent. We first extend the classical algorithm of Kleinberg et al 2008 to use the loss information encoded by any sequence of feedback graphs and prove matching upper and lower bounds for the sleeping regret of this algorithm. Our main contribution is then to relax this independence assumption, present a finer notion of sleeping regret, and derive a general algorithm with strong theoretical guarantees. We instantiate our framework to the important scenario of online learning with abstention, where a learner can elect to abstain from making a prediction at the price of a certain cost. We empirically validate the improvement of our algorithm against multiple abstention algorithms on several real-world datasets
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Learning GANs and Ensembles Using Discrepancy
Ben Adlam
Ningshan Zhang
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019
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Generative adversarial networks (GANs) generate data based on minimizing a divergence between two distributions. The choice of that divergence is therefore critical. We argue that the divergence must take into account the hypothesis set and the loss function used in a subsequent learning task, where the data generated by a GAN serves for training. Taking that structural information into account is also important to derive generalization guarantees. Thus, we propose to use the discrepancy measure, which was originally introduced for the closely related problem of domain adaptation and which precisely takes into account the hypothesis set and the loss function. We show that discrepancy admits favorable properties for training GANs and prove explicit generalization guarantees. We present efficient algorithms using discrepancy for two tasks: training a GAN directly, namely DGAN, and mixing previously trained generative models, namely EDGAN. Our experiments on toy examples and several benchmark datasets show that DGAN is competitive with other GANs and that EDGAN outperforms existing GAN ensembles, such as AdaGAN.
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AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
Charles Weill
Vitaly Kuznetsov
Scott Yang
Scott Yak
Hanna Mazzawi
Eugen Hotaj
Ghassen Jerfel
Vladimir Macko
Ben Adlam
(2019)
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AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention. Our framework is inspired by the AdaNet algorithm (Cortes et al., 2017) which learns the structure of a neural network as an ensemble of subnetworks. We designed it to: (1) integrate with the existing TensorFlow ecosystem, (2) offer sensible default search spaces to perform well on novel datasets, (3) present a flexible API to utilize expert information when available, and (4) efficiently accelerate training with distributed CPU, GPU, and TPU hardware. The code is open-source and available at https://github.com/tensorflow/adanet.
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