Past Special Lecture

23 August 2017
14:00
Dave Benson (Aberdeen)
Abstract

I shall describe recent work with Srikanth Iyengar, Henning 
Krause and Julia Pevtsova on the representation theory and cohomology
of finite group schemes and finite supergroup schemes. Particular emphasis 
will be placed on the role of generic points, detection of projectivity
for modules, and detection modulo nilpotents for cohomology.

 

21 January 2017
13:00
to
18:00
Graduate Students CANCELLED
Abstract

In Your Third Year & want to find out about opportunities for

summer placements and future graduate study?

Why not visit Oxford and hear from graduate students about their research

TALKS ON

Dynamics of jumping elastic toys

Vertex models in developmental biology

Modelling of glass sheets

Glimpse into the mathematics of information

Network analysis of consumer data

Complex singularities in jet and splash flows

Complementary Lunch & Drinks Reception - TRAVEL BURSARIES AVAILABLE (up to £50)

 

Please RSVP to InFoMM@maths.ox.ac.uk

21 January 2017
13:00
to
18:00
Abstract

In Your Third Year & want to find out about opportunities for summer placements and future graduate study?

Why not visit Oxford and hear from graduate students about their research

Saturday 21 January 2017: 1-6pm

Mathematical Institute, University of Oxford

TALKS ON

  • Dynamics of jumping elastic toys
  • Vertex models in developmental biology
  • Modelling of glass sheets
  • Glimpse into the mathematics of information
  • Network analysis of consumer data
  • Complex singularities in jet and splash flows

Complementary Lunch & Drinks Reception - TRAVEL BURSARIES AVAILABLE (up to £50)

Please RSVP to InFoMM@maths.ox.ac.uk

6 September 2016
11:30
Volkan Cevher
Abstract

Bayesian optimization (BO) is a powerful tool for sequentially optimizing black-box functions that are expensive to evaluate, and has extensive applications including automatic hyperparameter tuning, environmental monitoring, and robotics. The problem of level-set estimation (LSE) with Gaussian processes is closely related; instead of performing optimization, one seeks to classify the whole domain according to whether the function lies above or below a given threshold, which is also of direct interest in applications.

In this talk, we present a new algorithm, truncated variance reduction (TruVaR) that addresses Bayesian optimization and level-set estimation in a unified fashion. The algorithm greedily shrinks a sum of truncated variances within a set of potential maximizers (BO) or unclassified points (LSE), which is updated based on confidence bounds. TruVaR is effective in several important settings that are typically non-trivial to incorporate into myopic algorithms, including pointwise costs, non-uniform noise, and multi-task settings. We provide a general theoretical guarantee for TruVaR covering these phenomena, and use it to obtain regret bounds for several specific settings. We demonstrate the effectiveness of the algorithm on both synthetic and real-world data sets.

4 March 2016
15:30
Professor Kerry Emanuel
Abstract

In his talk, Kerry will explore the pressing practical problem of how hurricane activity will respond to global warming, and how hurricanes could in turn be influencing the atmosphere and ocean

1 December 2015
15:00
Professor Philippe Toint
Abstract
Weather prediction and, more generally, data assimilation in earth sciences, set a significant computing challenge 
because the size of the problem involved is very large.  The talk discusses algorithmic aspects related to the numerical 
solution of such problems and, in particular, focusses on how the lower dimensionality of the (dual) observation space 
may be used to advantage for computing a primal solution.  This is achieved both by adapting the preconditioned 
conjugate gradient and trust-region algorithms to dual space and by reducing the dimensionality of the latter as much 
as possible using observation hierarchies.
 
 

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