EE Student Information

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EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

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IT-Forum

IT-Forum

Topic: 
A Topic Modeling Approach to Learning Preference-Behavior from Pairwise Comparisons
Abstract / Description: 

The recent explosion of web analytics tools has enabled us to collect an immense amount of partial preferences for large sets of items such as products from Amazon, movies from Netflix, or restaurants from Yelp, from a large and diverse population of users through transactions, clicks, etc. Modeling, learning, and ultimately predicting the preference behavior of users from pairwise comparisons has been extensively studied since the 1927 work of Thurstone. Yet, almost all models to date have been founded on a clustering-perspective in which users are grouped by their preference behavior.

We take a fundamentally different decomposition-perspective and propose a new class of generative models for pairwise comparisons in which user preference behavior can be decomposed into contributions from multiple shared latent "causes" (partial orders) that are prevalent in the population. We show how the estimation of shared latent partial orders in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem in which causes correspond to topics and item-pairs to words. We show that an inevitable consequence of having a relatively small number of shared latent causes in a world of large number of item-pairs is the presence of "novel" item-pairs for each latent cause. We then leverage recent advances in the topic modeling literature and develop an algorithm based on extreme-point identification of convex polytopes to learn the shared latent partial orders. Our algorithm is provably consistent and comes with polynomial sample and computational complexity guarantees. We demonstrate that our new model is empirically competitive with the current state-of-the-art approaches in predicting preferences on semi-synthetic and real world datasets.

This talk is based on joint work with Weicong Ding and Venkatesh Saligrama at Boston University.

Date and Time: 
Friday, May 22, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum

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, May 1, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

IT-Forum

Topic: 
Sub-optimality of the Han-Kobayashi region for the interference channel: Remarks on computing regions expressed by auxiliaries
Abstract / Description: 

A computable characterization of the capacity region for the two receiver interference channel, along with that of the two receiver broadcast channel, constitute two of the most fundamental open questions in network information theory. A computable achievable region proposed by Han and Kobayashi (1981) was the best-known achievable region and its optimality or sub-optimality had not been established prior to this work. In this work, we show that coding across time-slots can improve on Han-Kobayashi region (also known as multi-letter inner bounds).

The talk will be mainly about the various ideas and developments that eventually led to this result. It touches upon various computational aspects of computable regions or in other words, the study of extremal auxiliaries.

A similar result about Marton's achievable region for broadcast channels has not been found yet; and the difficulty lies precisely in the narrowing of extremal auxliaries, although significant progress has been made in this direction in the past few years.

This will be a whiteboard talk and though there is a rough outline, the talk can be steered by the audience.

Date and Time: 
Friday, May 15, 2015 - 1:00pm to 2:00pm
Venue: 
Packard 202

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

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