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Ofer Dekel
Theory Day Session 2Ofer Dekel - Bandit Convex Optimization: SqrtT Regret in One DimensionBandit convex optimization is a sequential decision making game with partial information. On each round, an adversary chooses a bounded convex loss function and the decision maker chooses a point in the function's domain. The loss incurred by the decision maker on that round is the value of the loss function at his chosen point. The decision maker observes his loss value (a single number) and uses this information to make better...
Video details
Date: 26 March 2015
Duration: 00:45:48
Collection: Theory Day
Alexander (Sasho) Nikolov
Randomized Rounding for the Largest j-Simplex ProblemThe maximum volume j-simplex problem asks to compute the j-dimensional simplex of maximum volume inside the convex hull of a given set of n points in d-dimensional space. We give a deterministic approximation algorithm for this problem which achieves an approximation ratio of ej/2 + o(j) and runs in time polynomial in d and n. The problem is known to be NP-hard to approximate within a factor of cj for some constant c > 1. Our algorithm also approximates the problem of finding the largest determinant...
Video details
Date: 20 March 2015
Duration: 01:01:26
Collection: MSR Talk Series
Ben Parr
Captivology: The Science of Capturing PeopleWhy are we captivated by sites like Facebook and Instagram, but couldn't care less about MySpace? Why do some musicians grow as popular as Beyonce, while others never make the charts? Why do some nonprofits, such as Charity: Water, succeed in getting our donations, while other charities are ineffective?Whether you're an artist or a salesperson, a teacher or an engineer, a marketer or a parent - putting the spotlight on your ideas, insights, projects and products requires a deep understanding of the science...
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Date: 18 March 2015
Duration: 00:52:02
Collection: Visiting Speaker Series
Le Song
Scalable Kernel Methods via Doubly Stochastic GradientsThe general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called 'doubly stochastic functional gradients'. Our approach relies on the fact that many kernel methods can be expressed as convex optimization problems, and we solve the problems by making two unbiased stochastic approximations...
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Date: 17 March 2015
Duration: 01:10:33
Collection: MSR Talk Series
Jon Kleinberg
Long-Range Planning with Time-Inconsistency: A Class of Computational Problems in Behavioral EconomicsThere are many settings where people set long-range goals and make plans to achieve them. Such long-range planning is becoming an integral of the experience in many on-line contexts, where for example people work toward reputational milestones in question-answering sites, build up to administrative roles in open-source authoring domains, and reach educational goals in on-line learning communities.In order to understand these kinds of processes, we need to enrich our models with the types of human...
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Date: 17 March 2015
Duration: 00:58:37
Svitlana Volkova
Social Media Predictive Analytics: Methods and ApplicationsLarge-scale real-time social media analytics provides a novel set of conditions for the construction of predictive models. With individual users as training and test instances, their associated content (“lexical features”) and context ('network features') are made available incrementally over time, as they converse over discussion forums. We propose various approaches to handling this dynamic data for predicting latent user properties, from traditional batch training and testing, to incremental...
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Date: 17 March 2015
Duration: 00:56:12
Collection: UK Cambridge - Lecture/Seminar
Anvesh Komuravelli
Can you convince me why your software works?The rise of practical solvers for satisfiability checking (both propositional and first-order logics) has revolutionized the field of automatic program verification. However, present day algorithms make an inefficient use of these solvers by creating monolithic SAT instances for the entire program that can grow exponentially in size. I will first describe an efficient 'compositional' SAT-based approach for automatic verification that exploits the modularity of the program under consideration. This...
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Date: 17 March 2015
Duration: 00:56:26
Collection: UK Cambridge - Lecture/Seminar
Mohammad Reza Khani
Revenue Efficient Mechanisms for Online Advertisingnline advertising is an essential part of Internet and the main source of revenue for lots of web-centric companies such as search engines, news websites, Internet social networks, and other types of publishers. Online advertising happens in different settings and includes many challenges and constraints. A key component in each setting is the mechanism which selects and prices the set of winning ads. In this talk we talk about problems of current mechanisms being used in online advertising, candidate new...
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Date: 13 March 2015
Duration: 00:59:17
Collection: UK Cambridge - Lecture/Seminar
Grigory Yaroslavtsev
Near Optimal LP Rounding for Correlation Clustering on Complete GraphsIntroduced about 10 years ago by Bansal, Blum and Chawla, correlation clustering has become one of the standard techniques in machine learning and data mining. This due to several advantages of correlation clustering as compared to other standard clustering methods (e.g. k-means): - Correlation clustering only requires qualitative information about similarities between objects. This makes it applicable in scenarios such as crowdsourced duplicate finding when information about similarities between objects...
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Date: 12 March 2015
Duration: 00:57:13
Collection: MSR Talk Series
Jessica Silbey
The Eureka Myth: Creators, Innovators and Everyday Intellectual PropertyAre innovation and creativity helped or hindered by our intellectual property laws? In the two hundred plus years since the Constitution enshrined protections for those who create and innovate, we're still debating the merits of IP laws and whether or not they actually work as intended. Artists, scientists, businesses, and the lawyers who serve them, as well as the Americans who benefit from their creations all still wonder: what facilitates innovation and creativity in our digital age? And what role, if...
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Date: 11 March 2015
Duration: 01:06:13
Collection: Candidate Talk
Ryan Curtin
Fast Dual-Tree k-means with Bounded Single-Iteration Runtimek-means is a widely used clustering algorithm, but for k clusters and a dataset size of N, each iteration of Lloyd's algorithm costs O(kN) time. Although there are a handful of techniques to accelerate single Lloyd iterations, none of these techniques are tailored to the case of large k, which is increasingly common as dataset sizes grow (one example: vector quantization), and none of these techniques have a worst-case runtime bound of less than O(kN) per iteration. I present a dual-tree algorithm with a...
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Date: 11 March 2015
Duration: 00:58:56
Collection: Candidate Talk
Li Deng
Recent Advances in Deep Learning at Microsoft: A Selected OverviewSince 2009, Microsoft has engaged with academic pioneers of deep learning and has created industry-scale successes in speech recognition as well as in speech translation, object recognition, automatic image captioning, natural language, multimodal processing, semantic modeling, web search, contextual entity search, ad selection, and big data analytics. Much of these successes are attributed to the availability of big datasets for training deep models, the powerful general-purpose GPU computing, and the...
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Date: 10 March 2015
Duration: 01:03:32
Collection: MSR Talk Series
Abhigyan Sharma
The evolution of computing infrastructure points to a future with massively geo-distributed infrastructures deployed at hundreds or even thousands of locations. In parallel, the evolution of users’ traffic demands is making two challenges increasingly prominent on the Internet. First, a rapid growth in traffic, especially video traffic, is increasing the cost for Internet service providers (ISPs) who carry this traffic on their network backbone. Second, the Internet’s poor infrastructure support for...
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Date: 9 March 2015
Duration: 00:50:46
Collection: UK Cambridge - Lecture/Seminar
Gunes Baydin
Automatic differentiation and machine learningDerivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD) is a technique for calculating derivatives efficiently and accurately, established in fields such as computational fluid dynamics, nuclear engineering, and atmospheric sciences. Despite its advantages and use in other fields, machine learning practitioners have been little influenced by AD and make scant use of available tools. We survey the intersection of AD and machine learning,...
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Date: 6 March 2015
Duration: 00:56:40
Collection: UK Cambridge - Lecture/Seminar
Tobias Große-Puppendahl
Supporting Proxemic Interactions with Multi-Scale Electric Field SensingElectric field sensing is the basis for many unobtrusive user interfaces that measure the proximity to objects and humans. Depending on the sensing approach, captured information may comprise the dimensions of orientation, distance, motion, identity, and location. This information is vital to realize proxemic interaction systems that adapt to the spatial relationships between the environment, humans, and devices.In this talk, electric field sensing approaches will be discussed to capture the different...
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Date: 6 March 2015
Duration: 01:00:29
Collection: UK Cambridge - Lecture/Seminar
Yakir Vizel
Fast Interpolating Bounded Model CheckingBounded Model Checking (BMC) is well known for its simplicity and ability to find counterexamples. It is based on the idea of symbolically representing counterexamples in a transition system and then using a SAT-solver to check for their existence or their absence. State-of-the-art BMC algorithms combine a direct translation to SAT with circuit-aware simplifications and work incrementally, sharing information between different bounds. While BMC is incomplete (it can only show existence of counterexamples),...
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Date: 5 March 2015
Duration: 01:00:23
Collection: MSR Talk Series
Nina Narodytska
SAT-based Techniques for Optimization and Synthesis.The SAT technology has been tremendously successful at solving a wide range of real-world problems, including hardware and software verification, bioinformatics, planning and scheduling. In this talk, I will present two practically important generalizations of the Boolean satisfiability (SAT) problem. First, I will talk about the maximum satisfiability problem (MaxSAT), which requires finding a solution of an over-constrained SAT instance that satisfies as many constraints as possible. I will describe...
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Date: 5 March 2015
Duration: 00:58:19
Stephanie Dick
After Math: Following Mathematics into the DigitalThe advent of modern digital computing in the mid-twentieth century precipitated many transformations in the practices of mathematical knowledge production. However, early computing practitioners throughout the United States subscribed to complicated and conflicting visions of just how much the computer could contribute to mathematics - each suggesting a different division of mathematical labor between humans and computers and a hierarchization of the tasks involved. Some imagined computers as mere...
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Date: 4 March 2015
Duration: 01:02:48
Drew Purves
Human-centric machine learning.How can we enable a human being to carry out machine learning that is of value to themselves and to others? Not, I would argue, by reducing that human to an operative that simply selects some data and fires off a giant automated algorithm! Rather, we need to enable that human to interact creatively with every step of the machine learning process, and with other humans, in order to extract whatever it is that they want and need: understanding, new protocols for gathering data, predictive models, whatever....
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Date: 3 March 2015
Duration: 00:22:23
Collection: UK Cambridge - Lecture/Seminar
Diarmuid Ó Séaghdha
Tracking State in Spoken Dialogue.Applications with voice interfaces often work in a 'one-shot' manner: the user makes a request, the system carries out the request (if it has been understood) and then the dialogue state is reset. Spoken interactions between humans, on the other hand, can involve a incremental process of establishing and negotiating the participants' goals. In this talk I will discuss statistical models that attempt to reflect the complex nature of human dialogue. We will discuss how recurrent neural networks can detect...
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Date: 3 March 2015
Duration: 00:19:26
Collection: UK Cambridge - Lecture/Seminar
Richard Samworth
Statistical and computational trade-offs in estimation of sparse principal components.In recent years, Sparse Principal Component Analysis has emerged as an extremely popular dimension reduction technique for high-dimensional data. The theoretical challenge, in the simplest case, is to estimate the leading eigenvector of a population covariance matrix under the assumption that this eigenvector is sparse. An impressive range of estimators have been proposed; some of these are fast to compute, while others are known to achieve the minimax optimal rate over certain Gaussian or subgaussian...
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Date: 3 March 2015
Duration: 00:20:50
Collection: UK Cambridge - Lecture/Seminar
Rich Turner
Tree Structured Gaussian Process Approximations.Gaussian process regression can be accelerated by constructing a small pseudo-dataset to summarize the observed data. This idea sits at the heart of many approximation schemes, but such an approach requires the number of pseudo-datapoints to be scaled with the range of the input space if the accuracy of the approximation is to be maintained. This presents problems in time-series settings or in spatial datasets where large numbers of pseudo-datapoints are required since computation typically scales...
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Date: 3 March 2015
Duration: 00:26:04
Collection: UK Cambridge - Lecture/Seminar
Rowan McAllister
Time series inference with nonlinear dynamics and filtering for control.Many tasks in finance, science and engineering require the ability to control a dynamic system to maximise some objective. Designing controllers based on a physical understanding of systems is often time consuming and inaccurate. One is generally forced to make an increasing amount of simplifying assumptions the more complex the system is. For instance, friction, `stiction' and flex are difficult to model and, thus, frequently ignored. An alternative is automatic learning of control. By training a...
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Date: 3 March 2015
Duration: 00:20:11
Collection: UK Cambridge - Lecture/Seminar
Yutian Chen
Variational Gaussian Process State-Space Models.State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting....
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Date: 3 March 2015
Duration: 00:21:25
Collection: UK Cambridge - Lecture/Seminar
Milan Vojnovic
A Spectral Algorithm for Ranking using Seriation.We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total...
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Date: 3 March 2015
Duration: 00:19:29
Collection: UK Cambridge - Lecture/Seminar
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