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## Past Events

September 2018

September 21, 2018 @ 11:00 am - 12:00 pm

Weizmann Institute

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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## Topics in Information and Inference Seminar

September 20, 2018 @ 4:00 pm - 5:00 pm

Yury Polyanskiy (MIT )

32-D677

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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## LIDS Seminar Series – Regret of Queueing Bandits

September 17, 2018 @ 4:00 pm - 5:00 pm

Sanjay Shakkotai (University of Texas, Austin)

32-155

LIDS Seminar Series Speaker: Sanjay Shakkotai Affiliation: University of Texas, Austin Abstract: We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems, including supply and demand in online platforms (e.g., Uber, Lyft, Airbnb, Upwork, etc.), order flow in financial markets (e.g., limit order books), communication systems, and supply chains. We study…

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## An Information-Geometric View of Learning in High Dimensions

September 14, 2018 @ 11:00 am - 12:00 pm

Gregory Wornell (32-155)

32-155

Abstract: We consider the problem of data feature selection prior to inference task specification, which is central to high-dimensional learning. Introducing natural notions of universality for such problems, we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and practically useful learning methodology. The development reveals the key roles of the singular value decomposition, Hirschfeld-Gebelein-Renyi maximal correlation, canonical correlation and principle component analyses, Tishby's information bottleneck, Wyner's common information, Ky Fan…

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## Science for Policy 2.0

September 11, 2018 @ 4:00 pm - 5:00 pm

32-141

We live in an increasingly polarized present, looking to a complex and uncertain future while basing our legislative decisions on systems of the past. We need the processes and structures that underpin our political decision-making to be aligned with the complexities of the 21st century. Such changes cannot be undertaken by a technocratic elite, potentially disenfranchising citizens further from their governing institutions. Rather, political institutions must seek to improve transparency, openness, and accountability. The great divide between science and policy…

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## LIDS Seminar Series – Streaming Analytics for the Smart Grid

September 10, 2018 @ 4:00 pm - 5:00 pm

Le Xie (Texas A&M University)

32-155

LIDS Seminar Series Speaker: Le Xie Affiliation: Texas A&M University   Abstract: How to conduct real-time analytics of streaming measurement data in the power grid? This talk offers a dynamic systems approach to utilizing data of different time scale for improved monitoring of the grid cyber and physical security. The first example of the talk presents how to leverage synchrophasor data dimensionality reduction and Robust Principal Component Analysis for early anomaly detection, visualization, and localization. The second example presents an…

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## Data Science and Big Data Analytics: Making Data-Driven Decisions

September 10, 2018

Developed by 10 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins Sept 10, 2018.

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## Dejan Slepcev

September 7, 2018 @ 11:00 am - 12:00 pm

Carnegie Mellon University

MIT Statistics and Data Science Center host guest lecturers from around the world in this weekly seminar.

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August 2018

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

August 21, 2018 @ 2:00 pm - 3:00 pm

Prateek Jain (Microsoft Research)

E18-304

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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May 2018

## Fitting a putative manifold to noisy data

May 25, 2018 @ 11:00 am - 12:00 pm

Hariharan Narayanan (Tata Institute of Fundamental Research, Mumbai)

E18-304

Abstract: We give a solution to the following question from manifold learning. Suppose data belonging to a high dimensional Euclidean space is drawn independently, identically distributed from a measure supported on a low dimensional twice differentiable embedded compact manifold M, and is corrupted by a small amount of i.i.d gaussian noise. How can we produce a manifold $M_o$ whose Hausdorff distance to M is small and whose reach (normal injectivity radius) is not much smaller than the reach of M?…

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