Kareem Amin
Authored Publications
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Consider a setting where we wish to automate an expensive task with a machine
learning algorithm using a limited labeling resource. In such settings, examples
routed for labeling are often out of scope for the machine learning algorithm. For
example, in a spam detection setting, human reviewers not only provide labeled
data but are such high-quality detectors of spam that examples routed to them no
longer require machine evaluation. A consequence is that distribution of examples
routed to the machine is intimately tied to the process generating labels. We
introduce a formalization of this setting, and give an algorithm that simultaneously
learns a model and decides when to request a label by leveraging ideas from both
the abstention and active learning literatures. We prove an upper bound on the
algorithm’s label complexity and a matching lower bound for any algorithm in this
setting. We conduct a thorough set of experiments including an ablation study to
test different components of our algorithm. We demonstrate the effectiveness of an
efficient version of our algorithm over margin sampling on a variety of datasets.
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A differentially private algorithm guarantees privacy against an adversary that sees the output of the algorithm. We study pan-privacy, which guarantees privacy against an adversary that sees both the output and any single internal state of the algorithm during its computation. First, we motivate the single-intrusion assumption by showing that pan-privacy against multiple intrusions is equivalent to sequentially interactive local privacy. Next, we contextualize pan-privacy by analyzing the sample complexity of uniformity testing. We show that this sample complexity sits strictly between that of the central and (sequentially interactive) local models.
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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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Differentially private learning algorithms protect individual participants in the training dataset by guaranteeing that their presence does not significantly change the resulting model. In order to make this promise, such algorithms need to know the maximum contribution that can be made by a single user: the more data an individual can contribute, the more noise will need to be added to protect them. While most existing analyses assume that the maximum contribution is known and fixed in advance—indeed, it is often assumed that each user contributes only a single example— we argue that in practice there is a meaningful choice to be made. On the one hand, if we allow users to contribute large amounts of data, we may end up adding excessive noise to protect a few outliers, even when the majority contribute only modestly. On the other hand, limiting users to small contributions keeps noise levels low at the cost
of potentially discarding significant amounts of excess data, thus introducing bias. Here, we characterize this trade-off for an empirical risk minimization setting, showing that in general there is a “sweet spot” that depends on measurable properties of the dataset, but that there is also a concrete cost to privacy that cannot be avoided simply by collecting more data.
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We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.
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Motivated by real-time advertising exchanges, we analyze the problem of pricing inventory in a repeated posted-price auction. We consider both the cases of a truthful and surplus-maximizing buyer, where the former makes decisions myopically on every round, and the latter may strategically react to our algorithm, forgoing short-term surplus in order to trick the algorithm into setting better prices in the future. We further assume a buyer’s valuation of a good is a function of a context vector that describes the good being sold. We give the first algorithm attaining sublinear (O(T^{
2/3})) regret in the contextual setting against a surplus-maximizing
buyer. We also extend this result to repeated second-price auctions with multiple buyers.
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Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer’s value for a good when the buyer is repeatedly interacting with the seller through a posted-price mechanism. We model the buyer as a strategic agent, interested in maximizing her long-term surplus, and are interested in optimizing seller revenue. We show conditions under which the seller cannot hope to gain an advantage by learning the buyer’s value – i.e. the buyer can always manipulate the exchange to hide her value. This result is accompanied by a seller algorithm that is able to achieve no-regret when the buyer is unable to incur the short-term costs of such manipulation.
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