Monthly talks by academic and industry leaders from around the world.

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Towards a Theory of Fairness in Machine Learning

Jamie Morgenstern (Penn)
32-G449 (Kiva)

Abstract:  Algorithm design has moved from being a tool used exclusively for designing systems to one used to present people with personalized content, advertisements, and other economic opportunities. Massive amounts of information is recorded about people's online behavior including the websites they visit, the advertisements they click on, their search history, and their IP address. Algorithms then use this information for many purposes: to choose which prices to quote individuals for airline tickets, which advertisements to show them, and even which news stories to promote.…

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Gene Regulation in Space and Time, or From Ellipsoid Packing to Causal Inference – DaVinci Lecture (presented by Tau Beta Pi)

Caroline Uhler (MIT)

Abstract: Although the genetic information in each cell within an organism is identical, gene expression varies widely between different cell types. The quest to understand this phenomenon has led to many interesting mathematics problems. Experimental evidence suggests that the differential gene expression is related to the spatial organization of chromosomes in the cell nucleus. I will present a new model, based on ellipsoid packing and causal inference, that can link the 3d organization of chromosomes with gene regulation. Such models…

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Learning from People

Nihar Shah (University of California, Berkeley)
34-401 (Grier Room)

Abstract: Learning from people represents a new and expanding frontier for data science. Two critical challenges in this domain are of developing algorithms for robust learning and designing incentive mechanisms for eliciting high-quality data. In this talk, I describe progress on these challenges in the context of two canonical settings, namely those of ranking and classification. In addressing the first challenge, I introduce a class of “permutation-based” models that are considerably richer than classical models, and present algorithms for estimation…

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Provably Secure Machine Learning

Jacob Steinhardt (Stanford)
32-G449 (Kiva/Patel)

Abstract:  The widespread use of machine learning systems creates a new class of computer security vulnerabilities where, rather than attacking the integrity of the software itself, malicious actors exploit the statistical nature of the learning algorithms. For instance, attackers can add fake data (e.g. by creating fake user accounts), or strategically manipulate inputs to the system once it is deployed. So far, attempts to defend against these attacks have focused on empirical performance against known sets of attacks. I will argue that…

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Resource-efficient ML in 2 KB RAM for the Internet of Things

Prateek Jain (Microsoft Research)

Abstract: We propose an alternative paradigm for the Internet of Things (IoT) where machine learning algorithms run locally on severely resource-constrained edge and endpoint devices without necessarily needing cloud connectivity. This enables many scenarios beyond the pale of the traditional paradigm including low-latency brain implants, precision agriculture on disconnected farms, privacy-preserving smart spectacles, etc. Towards this end, we develop novel tree and kNN based algorithm, called Bonsai and ProtoNN, for efficient prediction on IoT devices — such as those based…

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Topics in Information and Inference Seminar

Yury Polyanskiy (MIT )

Title: Strong data processing inequalities and information percolation Abstract: The data-processing inequality, that is, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. A channel-dependent improvement is called the strong data-processing inequality (or SDPI). In this talk we will: a) review SDPIs; b) show how point-to-point SDPIs can be combined into an SDPI for a network; c) show recent…

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