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 EE Student Information, Spring & Summer Quarters 19-20: FAQs and Updated EE Course List.

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IT-Forum

IT-Forum: Social Learning in Decision-Making Groups

Topic: 
Social Learning in Decision-Making Groups
Abstract / Description: 

People have always been influenced by the opinions of their acquaintances. Increasingly, through recommendations and ratings provided on all sorts of goods and services, people are also influenced by the opinions of people that are not even acquaintances. This ubiquity of the sharing of opinions has intensified the interest is the concept of herding (or informational cascades) introduced in 1992. While agents in most previous works have only individualistic goals, this talk focuses on social influence among agents in two collaborative settings.

We consider agents that perform Bayesian binary hypothesis testing and, in addition to their private signals, observe the decisions of earlier-acting agents. In the first setting, each decision has its own corresponding Bayes risk. Each agent affects the minimum possible Bayes risk for subsequent agents, so an agent may have a mixed objective including her own Bayes risk and the Bayes risks of subsequent agents; we demonstrate her tension between being informative to other agents and being right in her own decisions, and we show that she is more informative to others when she is open minded. In the second setting, opinions are aggregated by voting, and all agents aim to minimize the Bayes risk of the team's decision. We show that social learning is futile when the agents observe conditionally independent and identically distributed private signals (but not merely conditionally independent private signals) or when the agents require unanimity to make a decision. Our experiments with human subjects suggest that when opinions of people with equal qualities of information are aggregated by voting, the ballots should be secret. They have also raised questions about rationality and trust.

Date and Time: 
Friday, April 24, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds

Topic: 
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds
Abstract / Description: 

We study the following generalized matrix rank estimation problem: given an n×n matrix and a constant c≥0, estimate the number of eigenvalues that are greater than c. In the distributed setting, the matrix of interest is the sum of m matrices held by separate machines. We show that any deterministic algorithm solving this problem must communicate Ω(n^2) bits, which is order-equivalent to transmitting the whole matrix. In contrast, we propose a randomized algorithm that communicates only O(n) bits. The upper bound is matched by an Ω(n) lower bound on the randomized communication complexity. We demonstrate the practical effectiveness of the proposed algorithm with some numerical experiments.

Date and Time: 
Friday, April 17, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: Control Information

Topic: 
Control Information
Abstract / Description: 

Control provides a conceptually interesting area for understanding the nature of information beyond traditional communication problems. After all, both control and communication are about the reduction of uncertainty ---- in communication it is about informing the beholder so that its idea of the world is closer to reality while in control, it is about changing the world so that it more closely conforms to the idea of a beholder. Building by analogy with communication's standard picture of a source, channel, and destination/sink, this talk will discuss the source-nature of control systems, the channel-nature of control systems, and also the sink-nature of control systems. This sink-nature in particular, is something that we don't normally think about in communication systems. For control, however, it is natural to consider it and I will talk about a concept that we have developed recently that we call "control capacity."

Joint work with Gireeja Ranade and Se Yong Park.

Date and Time: 
Friday, April 3, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: Searching with measurement dependent noise

Topic: 
Searching with measurement dependent noise
Abstract / Description: 

We consider a search problem in which a target is arbitrarily placed on the unit interval. To acquire the target, any region of the interval can be probed for its presence, but the associated measurement noise increases with the size of the probed region. We are interested in the expected search time required to find the target to within some given resolution and error probability. When the measurement noise is constant (independent of the probed region), this problem is known to be equivalent to standard channel coding with feedback. We characterize the optimal tradeoff between time and resolution (i.e., maximal rate), and show that in contrast to the case of constant measurement noise, measurement dependent noise incurs a multiplicative gap between adaptive search and non-adaptive search. Moreover, our adaptive scheme attains the optimal rate-reliability tradeoff. An extension of this problem into a multi-target setting is also considered. We highlight the equivalence of this extension to coding for a certain multiple access channel and the optimal rate, as a function of the number of targets, is characterized. Finally, we show that as the number of targets increases, the performance gap between adaptive- and non-adaptive search becomes negligible. This talk is based on joint work with Ofer Shayevitz and Tara Javidi.

Date and Time: 
Friday, March 6, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: New matrix decompositions for Gaussian communication networks

Topic: 
New matrix decompositions for Gaussian communication networks
Abstract / Description: 

A central concept in matrix analysis is the decomposition of a matrix into a product of orthogonal (or unitary) matrices and a diagonal/triangular one, e.g., unitary diagonalization of a symmetric matrix, and more generally the singular-value decomposition, and the QR decomposition. Such decompositions are of particular importance for multi-antenna point-to-point physical-layer communications, where the channel gains are represented by a (channel) matrix. Transforming the channel matrix into diagonal/triangular forms, in this case, allows to reduce the coding task to that of coding for scalar (single-antenna) channels. Thus, the modulation and coding tasks are effectively decoupled and the performance is dictated by the diagonal values. In this work we develop new joint matrix decompositions of several matrices using the same unitary matrix on one side (corresponding to a joint transmitter or receiver) to achieve desired properties for the resulting diagonals. An important special case is a transformation leading to equal diagonals for all matrices simultaneously. This, in turn, allows to construct practical schemes for various communications settings, as well as deriving new theoretic bounds for others.

Date and Time: 
Friday, February 6, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: A Tale of Two Measures

Topic: 
A Tale of Two Measures
Abstract / Description: 

Information theory has been traditionally studied in the context of communication theory and statistical physics. However, it has also had important applications in other fields such as computer science, economics, mathematics, and statistics. This talk is very much in the spirit of discovering applications of information theory in other fields. We will discuss three such recent applications:

Statistics: The Hirschfeld-Gebelein-Rényi maximal correlation is an important tool in statistics that has found numerous applications from correspondence analysis, to detection of non-linear patterns in data. We will describe a simple information-theoretic proof of a fundamental result on maximal correlation due to Dembo, Kagan, and Shepp (2001).

Computer Science: Boolean functions are one of the most basic objects of study in theoretical computer science. We show how information-theoretic tools can aid Fourier analytic tools in this quest. Specifically, we will consider the problem of correlation between Boolean functions on a noisy hypercube graph.

Mathematics: Hypercontractivity and Reverse Hypercontractivity are very useful tools for studying concentration of measure, and extremal questions in the geometry of high-dimensional spaces, both discrete and continuous. In this talk, we will describe a recent result by Chandra Nair characterizing hypercontractivity using information measures. We will extend this result to reverse hypercontractivity, and we will discuss implications of these results.

The title of this presentation is derived from two measures of correlation - the maximal correlation and the so-called strong data processing constant - that will be key concepts used throughout. This talk is based on joint work with Venkat Anantharam, Amin Gohari, and Chandra Nair.

Date and Time: 
Friday, March 13, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: Precinct or Prejudice? Understanding Racial Disparities in New York City's Stop-and-Frisk Policy

Topic: 
Precinct or Prejudice? Understanding Racial Disparities in New York City's Stop-and-Frisk Policy
Abstract / Description: 

Recent studies have examined racial disparities in stop-and-frisk, a widely employed but controversial policing tactic. The statistical evidence, though, has been limited and contradictory. We investigate by analyzing three million stops in New York City over five years, focusing on cases where officers suspected the stopped individual of criminal possession of a weapon (CPW). For each CPW stop, we estimate the ex-ante probability that the detained suspect would have a weapon. We find that in 44% of cases, the likelihood of finding a weapon was less than 1%, raising concerns that the legal requirement of "reasonable suspicion" was often not met. We further find that blacks and Hispanics were disproportionately stopped in these low hit rate contexts, a phenomenon largely attributable to lower thresholds for stopping individuals in high-crime, predominately minority areas, particularly public housing. Even after adjusting for location effects, however, we find that stopped blacks and Hispanics were still less likely than similarly situated whites to possess weapons, indicative of racial bias in stop decisions. We demonstrate that by conducting only the 6% ex-ante highest hit rate stops, one can both recover the majority of weapons and mitigate racial disparities. Finally, we develop stop heuristics that can be implemented as a simple scoring rule, and have comparable accuracy to our full statistical models.

This work is joint with Justin Rao (Microsoft) and Ravi Shroff (NYU).A draft of the paper can be downloaded here: https://5harad.com/papers/frisky.pdf

Date and Time: 
Friday, February 20, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum

Topic: 
A puzzle: How to communicate via a binary erasure channel with feedback without repeated ones?
Abstract / Description: 

In this talk we will present a simple and fundamental problem: communicating via a memoryless binary erasure channel with feedback without consecutive 1's.

First, we will present the problem as a puzzle and provide a simple solution. We will prove its optimality using only counting, logics and basic probability arguments. Then we will show how we obtained the solution using information theory tools (such as the Directed information) and optimization tools (such as Dynamic Programing).

The talk will be given mostly on a whiteboard.

Based on Joint work with Oron Sabag from Ben-Gurion University and Navin Kashyap from Indian Institute of Science.

Date and Time: 
Friday, February 13, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum: On Optimal Solutions in Decentralized Control (Team) Problems

Topic: 
On Optimal Solutions in Decentralized Control (Team) Problems
Abstract / Description: 

Recently, there has been a lot of work on decentralized control (team) problems -- problems in which multiple agents having different information act, perhaps in a dynamic environment, to minimize a common objective function. Such scenarios naturally occur, for example, in large-scale control systems, communication systems, organizations, and networks. However, very few team problems were known to admit optimal solutions.

In this talk, we discuss some recent results on this topic and show that a class of dynamic LQG teams with no observation sharing information structures admit team-optimal solutions. This result provides the first unified proof of existence of optimal solutions in several different classes of stochastic teams, including the celebrated Witsenhausen's counterexample, the Gaussian test channel, the Gaussian relay channel and their non-scalar extensions.

Date and Time: 
Friday, January 30, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

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