Organiser: Tom Gur
Colloquia take place fortnightly on Thursdays 14:00-15:00, Lecture Room CS1.01. They are directed towards a general computer science audience. After the talk, we convene in the staff common room for coffee/tea and informal discussions with the speaker.
Additional colloquia, titles and abstracts will be added as details become available.
Current term
First term, 2019-2020:
10 October 2019
14:00 - 15:00
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Sunil Prabhakar (Purdue University)
Title: Database Fidelity Without Trust
Abstract:
Ensuring the trustworthiness of data retrieved from a database is of utmost importance to users. Databases are typically subjected to updates from multiple clients, often at very high rates. The correctness of data stored in a database is defined by the faithful execution of only valid (authorized) transactions. In this talk we address the question of whether it is necessary to trust a database server in order to trust the data retrieved from it? The lack of trust arises naturally if the database server is owned by a third party, as in the case of cloud computing, or if it is likely that the server may have been compromised, or there is a malicious insider.
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24 October 2019
14:00 - 15:00
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Title: THEMIS -- Fairness in Federated Stream Processing under Overload
Abstract:
Federated stream processing systems, which utilise nodes from multiple
independent domains, can be found increasingly in multi-provider cloud
deployments, internet-of-things systems, collaborative sensing
applications and large-scale grid systems. To pool resources from several
sites and take advantage of local processing, submitted queries are split
into query fragments, which are executed collaboratively by different
sites. When supporting many concurrent users, however, queries may exhaust
available processing resources, thus requiring constant load shedding.
Given that individual sites have autonomy over how they allocate query
fragments on their nodes, it is an open challenge how to ensure global
fairness on processing quality experienced by queries in a federated
scenario.
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7 November 2019
14:00 - 15:00 |
Title: Uncovering structure with randomness for learning in high dimensions
Abstract:
Random projection (RP) is a simple, computationally efficient linear
dimensionality reduction technique with interesting theoretical
properties for learning from data. For instance, it can regularise
ill-conditioned covariance matrices, and it preserves dot products
between vectors and their signs to an extent that depends on their
cosine similarity. It can speed up computations when dealing with high
dimensional data sets, while retaining control of generalisation
performance. Furthermore, in this talk we discuss how RP can also be
used as an analytic tool to better understand why learning from high
dimensional data is not always as difficult as it looks. We present
generalisation bounds where the use of RP highlights how learning can
take advantage of the presence of certain benign geometric structures
in the problem, and yields conditions that reduce
dimensional-dependence of error-guarantees in settings where such
dependence is known to be essential in general.
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21 November 2019
14:00 - 15:00 |
Title: Non-classicality, quantum resources and quantum advantage (slides)
Abstract:
A key objective in the field of quantum information and computation is to understand the advantage which can be gained in information processing tasks by the use of quantum resources. While a range of examples has been studied, to date a systematic understanding of quantum advantage is lacking.
Our focus here is on quantum resources which take the form of non-local, or more generally \emph{contextual}, correlations. Contextuality is one of the key signatures of non-classicality in quantum mechanics and has been shown to be a necessary ingredient for quantum advantage in a range of information processing tasks. We will describe a notion of simulation between quantum resources, and more generally between resources described in terms of contextual correlations, in the ``sheaf-theoretic'' framework for contextuality (Abramsky-Brandenburger).
The notion of simulation is expressed as a morphism of empirical models, in a form which allows the behaviour of one set of correlations to be simulated in terms of another using classical processing and shared randomization.
The talk will be essentially self-contained, introducing the various notions we will discuss.
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3 December 2019
15:00 - 16:00 |
Title: Deep Learning for Precision Oncology
Abstract:
Precision oncology requires molecular and genetic testing of tumour tissue. For many tests, widespread implementation into clinical practise is limited because these biomarkers are costly, require significant expertise and are limited by tissue availability. However, virtually every cancer patient gets a biopsy as part of the diagnostic workup and this tissue is routinely stained with hematoxylin and eosin (HE). We have developed a deep learning-based technology to predict molecular features, prognosis and markers for treatment response directly from routine histology. This talk will summarize the state of the art in deep learning histopathology, demonstrate emerging use cases and discuss the clinical implications of deep learning-based molecular testing of solid tumours.
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Past terms
Third term, 2018-2019:
Thu 2 May, '19
14:00 - 15:00
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Title: Anonymity in Cryptocurrencies
(This talk will take place in CS1.04)
Abstract: A long line of recent research has demonstrated that existing cryptocurrencies often do not achieve the level of anonymity that users might expect they do, while at the same time another line of research has worked to increase the level of anonymity by adding new features to existing cryptocurrencies or creating entirely new cryptocurrencies. This talk will explore both of these lines of research, demonstrating both de-anonymization attacks and techniques for anonymity that achieve provably secure guarantees.
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Thu 9 May, '19
14:00 - 15:00 |
Title: Fair Division of a Graph
Abstract: We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envy-freeness and maximin share guarantee.In particular, we prove that for acyclic graphs a maximin share allocation always exists and can be computed efficiently. We also discuss a continuous variant of this problem, where agents need to divide a collection of interconnected "cakes" and each agent's piece has to be connected, or, more generally, have a small number of connected components.
Based on joint work with Sylvain Bouveret, Katarina Cehlarova, Ayumi Igarashi, Dominik Peters, Xiaohui Bei and Warut Suksompong
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Thu 6 Jun, '19
14:00 - 15:00
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(This talk will take place in CS1.04)
Title: Probability and uncertainty in Deep Learning
Abstract:
In this talk I motivate deep learning from the perspective of probability and uncertainty. The resulting models can maintain and, crucially, communicate their degree of belief regarding their states (e.g. how certain they are in their predictions) using Bayesian inference. I will also highlight the deep Gaussian process family of approaches, which can be seen as a means of performing probabilistic, Bayesian deep learning directly in the space of functions, rather than in the space of finite parameters. Prior knowledge of deep learning is not essential to follow this talk, in fact I will offer a quick overview in the beginning.
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Thu 20 Jun, '19
14:00 - 15:00 |
Title: Quantum Technologies for Cryptography
Abstract:
The rise of quantum technologies opens exciting possibilities for performing cryptographic tasks, but it also poses new threats. On the one hand, quantum mechanical devices are the basis of a variety of novel cryptographic protocols like key distribution or secure identification, which offer a significant advantage over standard routines. On the other hand, quantum computers will render some existing cryptographic schemes insecure. The interplay between these two aspects is best understood by analysing the power of adversaries which have access to a quantum computer - quantum adversaries. In my talk, I will discuss methods to analyse the power of quantum adversaries which shine light on both aspects.
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Thu 11 Jul, '19
11:00 - 12:00
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Title: Learning, synthesis and efficient analysis of probabilistic models
Abstract:
Probabilistic modelling is a powerful tool for establishing performance, dependability and other quantitative properties of systems and processes during design, verification and at runtime. However, the usefulness of this tool depends on the accuracy of the models being analysed, on the efficiency of the analysis, and on the ability to find models corresponding to effective system and process architectures and configurations. This talk will describe how recent approaches to probabilistic model learning, analysis and synthesis address major challenges posed by these prerequisites, significantly advancing the applicability of probabilistic modelling.
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