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

Updates will be posted on this page, as well as emailed to the EE student mail list.

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IT-Forum

IT-Forum welcomes David Woodruff

Topic: 
TBA
Abstract / Description: 

TBA


The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held every Friday at 1:15 pm during the academic year.

Until further notice, the IT Forum convenes exclusively via Zoom (on Fridays at 1:15pm PT) due to the ongoing pandemic. To avoid "Zoom-bombing", we ask attendees to input their email address here https://stanford.zoom.us/meeting/register/tJwkf-uvqjoqHNIWxY4HHon4K107QMo22PVR to receive the Zoom meeting details via email.

Date and Time: 
Friday, November 13, 2020 - 1:15pm
Venue: 
Zoom

IT-Forum presents "High-accuracy Optimality and Limitation of the Profile Maximum Likelihood"

Topic: 
High-accuracy Optimality and Limitation of the Profile Maximum Likelihood
Abstract / Description: 

Symmetric properties of distributions arise in multiple settings, where for each of them separate estimators have been developed. Recently, Orlitsky et al. showed that a single estimator, called the profile maximum likelihood (PML), achieves the optimal sample complexity universally for many properties and any accuracy parameter larger than $n^{-1/4}$, where $n$ is the sample size. They also raised the question whether this low-accuracy range is an artifact of the analysis or a fundamental limitation of the PML, which remained open after several subsequent work.

In this talk, we provide a complete answer to this question and characterize the tight performance of PML in the high-accuracy regime. On the positive side, we show that the PML remains sample-optimal for any accuracy parameter larger than $n^{-1/3}$ using a novel chaining property of the PML distributions. In particular, the PML distribution itself is an optimal estimator of the sorted hidden distribution. On the negative side, we show that the PML as well as any adaptive approach cannot be universally sample-optimal when the accuracy parameter is below $n^{-1/3}$, and characterize the exact penalty for adaptation via a matching adaptation lower bound.

Based on joint work with Kirankumar Shiragur. The full papers are available online at https://arxiv.org/abs/2004.03166 and https://arxiv.org/abs/2008.11964.


The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held every Friday at 1:15 pm during the academic year.

Until further notice, the IT Forum convenes exclusively via Zoom (on Fridays at 1:15pm PT) due to the ongoing pandemic. To avoid "Zoom-bombing", we ask attendees to input their email address here https://stanford.zoom.us/meeting/register/tJwkf-uvqjoqHNIWxY4HHon4K107QMo22PVR to receive the Zoom meeting details via email.

Date and Time: 
Friday, September 25, 2020 - 1:15pm
Venue: 
Zoom registration req'd

Learning to Bid in Repeated First-price Auctions

Topic: 
Learning to Bid in Repeated First-price Auctions
Abstract / Description: 

First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction where it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors? To answer this question, we study online learning in repeated first-price auctions, and consider various scenarios involving different assumptions on the characteristics of the other bidders' bids, of the bidder's private valuation, of the feedback structure of the auction, and of the reference policies with which our bidder competes. For all of them, we characterize the essentially optimal performance and identify computationally efficient algorithms achieving it. Experimentation on first-price auction datasets from Verizon Media demonstrates the promise of our schemes relative to existing bidding algorithms.

Based on joint work with Aaron Flores, Erik Ordentlich, Tsachy Weissman, and Zhengyuan Zhou. The full papers are available online at https://arxiv.org/abs/2003.09795 and https://arxiv.org/abs/2007.04568


 

The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held every Friday at 1:15 pm during the academic year.

Until further notice, the IT Forum convenes exclusively via Zoom (on Fridays at 1:15pm PT) due to the ongoing pandemic. To avoid "Zoom-bombing", we ask attendees to input their email address here https://stanford.zoom.us/meeting/register/tJwkf-uvqjoqHNIWxY4HHon4K107QMo22PVR to receive the Zoom meeting details via email.

Date and Time: 
Friday, September 18, 2020 - 1:15pm
Venue: 
Zoom

IT Forum presents "Fundamental barriers to estimation in high-dimensions"

Topic: 
Fundamental barriers to estimation in high-dimensions
Abstract / Description: 

Modern large-scale statistical models require to estimate thousands to millions of parameters. Understanding the tradeoff between statistical optimality and computational tractability in such models remains an outstanding challenge. Under a random design assumption, we establish lower bounds to statistical estimation with two popular classes of tractable estimators in several popular statistical models. First, in high-dimensional linear models we show that a large gap often exists between the optimal statistical error and that achieved by least squares with a convex penalty. Examples of such estimators include the Lasso, ridge regression, and MAP estimation with log-concave priors and Gaussian noise. Second, in generalized linear models and low-rank matrix estimation problems, we introduce the class of 'general first order methods,' examples of which include gradient descent, projected gradient descent, and their accelerated versions. We derive lower bounds for general first order methods which are tight up to asymptotically negligible terms. Our results demonstrate a gap to statistical optimality for general first order methods in both sparse phase retrieval and sparse PCA.

This is joint work with Andrea Montanari and Yuchen Wu.

 

Date and Time: 
Friday, April 3, 2020 - 1:15pm
Venue: 
Zoom: stanford.zoom.us/j/516499996

IT Forum presents "What Hockey Teams and Foraging Animals Can Teach Us About Feedback Communication"

Topic: 
What Hockey Teams and Foraging Animals Can Teach Us About Feedback Communication
Abstract / Description: 

Suppose we wish to communicate over a memoryless channel with known statistics. How can the use of a feedback link from the receiver to the transmitter help? We introduce a novel mechanism for using feedback, called timid/bold coding, and we show that for some channels timid/bold coding yields a strict asymptotic improvement over the best non- feedback schemes. We also show that for a broad class of channels, feedback is useful if and only if timid/bold coding is applicable. The talk contains a puzzle (recently featured on the FiveThirtyEight website), a life lesson, a (potential) practical application, and some stochastic calculus.

This is joint work with Nirmal Shende and Yucel Altug.

Date and Time: 
Friday, March 6, 2020 - 1:15pm
Venue: 
Packard 202

RL forum presents "Temporal Abstraction in Reinforcement Learning with the Successor Representation"

Topic: 
Temporal Abstraction in Reinforcement Learning with the Successor Representation
Abstract / Description: 

Reasoning at multiple levels of temporal abstraction is one of the key abilities for artificial intelligence. In the reinforcement learning problem, this is often instantiated with the options framework. Options allow agents to make predictions and to operate at different levels of abstraction within an environment. Nevertheless, when a reasonable set of options is not known beforehand, there are no definitive answers for characterizing which options one should consider. Recently, a new paradigm for option discovery has been introduced. This paradigm is based on the successor representation (SR), which defines state generalization in terms of how similar successor states are. In this talk I'll discuss the existing methods from this paradigm, providing a big picture look at how the SR can be used in the options framework. I'll present methods for discovering "bottleneck" options, as well as options that improve an agent's exploration capabilities. I'll also discuss the option keyboard, which uses the SR to extend a finite set of options to a combinatorially large counterpart without additional learning.

Date and Time: 
Tuesday, February 25, 2020 - 2:00pm
Venue: 
Packard 202

IT Forum presents "Why should information theorists and probabilists care about blockchains?"

Topic: 
Why should information theorists and probabilists care about blockchains?
Abstract / Description: 

The invention of blockchains in 2008 opened up for the first time the possibility of large scale decentralized trust systems. In the past few years, the design and analysis of blockchains have received significant attention from cryptography, security and distributed systems communities. In this talk, I argue that there are many interesting problems for information theorists and probabilists as well. I will discuss two particular classes of problems: 1) security analysis of blockchains as convergence analysis of random tree processes; 2) the use of coding to efficiently scale blockchains. I will also briefly discuss a course I plan to teach in the Spring on the topic.

Part of this talk is on joint work with Amir Dembo and Ofer Zeitouni.

 

Date and Time: 
Friday, January 31, 2020 - 1:00pm
Venue: 
Packard 202

Q-Farm Quantum Seminar Series presents "Surprises from Time Crystals"

Topic: 
Surprises from Time Crystals
Abstract / Description: 

Time crystals are new states of matter that only exist in an out-of-equilibrium setting. I will review the state of this rapidly evolving field, focusing in particular on some of the remarkable properties of this phase, and the surprises coming out of its study. I will provide a detailed overview of existing experiments, with a view towards identifying the ingredients needed for an unambiguous observation of this phase in the future.

Date and Time: 
Wednesday, January 29, 2020 - 12:00pm
Venue: 
Hansen Physics & Astrophysics Building, 102/103

ISL & IT-Forum present "Approaching Capacity at Short Blocklengths"

Topic: 
Approaching Capacity at Short Blockengths
Abstract / Description: 

This talk explores a family of recent results directed to approaching capacity at short blocklengths on the order of 50-500 channel transmissions. Convolutional codes out-perform polar codes and LDPC codes to approach the random coding union bound with low complexity when used with an optimized CRCs and list decoding. This perspective rehabilitates "catastrophic" convolutional codes, which are more properly understood for finite blocklengths as clever expurgation rather than any sort of catastrophe. This approach also provides a low-complexity approach for maximum-likelihood decoding of high-rate BCH codes. The use of variable length coding, i.e. incremental redundancy controlled with simple ACK/NACK feedback, allows capacity to be closely approached by practical codes with fewer than 500 channel uses. This talk reviews the information-theoretic results of Polyanskiy with respect to ACK/NACK feedback, presents new results extending the classic approach of Horstein for full feedback, and shows how to optimize the number and length of incremental redundancy transmissions (and feedback transmissions) for a variable-length code with feedback (i.e. a type-II hybrid ARQ). The talk also shows how to avoid entirely the overhead of a CRC in a hybrid ARQ setting by directly computing the reliability of convolutional codeword decisions. Finally, attendees will learn about a novel communications architecture that allows the use of incremental redundancy even without feedback.

 

Date and Time: 
Friday, January 24, 2020 - 1:15pm
Venue: 
Packard 202

ISL Colloquium presents "Self-Programming Networks: Applications to Financial Trading Systems"

Topic: 
Self-Programming Networks: Applications to Financial Trading Systems
Abstract / Description: 

We describe Self-Programming Networks (SPNs), an ongoing research effort at Stanford for making cloud computing networks autonomous; that is, to enable the networks to sense and monitor themselves, and program and control themselves. We describe the goals and the architecture of SPNs and present two key outcomes: (i) Huygens, for scalable and accurate clock synchronization, and (ii) Simon, for fine-grained network telemetry using observations from the network’s edge. We also describe the relevance of this work to existing financial trading systems and demonstrate how, in future, it enables financial trading systems in the Cloud.


The Information Systems Laboratory Colloquium (ISLC) is typically held in Packard 101 every Thursday at 4:30 pm during the academic year. Coffee and refreshments are served at 4pm in the second floor kitchen of Packard Bldg.

The Colloquium is organized by graduate students Joachim Neu, Tavor Baharav and Kabir Chandrasekher. To suggest speakers, please contact any of the students.

Date and Time: 
Thursday, January 23, 2020 - 4:30pm
Venue: 
Packard 101

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