SIGML resumes this semester with student presentations. There will be two
students presentations on last semester UGP work and course project. CSE
Y13 and MTech'15 students are encourage to attend the same. You are all
cordially invited for the same. Below are the details of the talks.

*TALK I :*

Title : Sparse Recovery And Optimization (UGP)
Speaker : Amartya And Bhuvesh
Timing : 4:00 PM - 4:30 PM , 9 January 16
Day : Saturday

Abstract* : *
Problem of parameter estimation with given set of constraints is central to
machine learning. In several learning applications, one is required to
perform estimation with far fewer data points than the number of parameters
to be estimated. Though consistent estimation is impossible in these
settings in general, structural constraints like sparsity or low rank make
it possible to perform consistent estimation. Such constraints naturally
arise in several situations where we desire "simple" solutions that can be
expressed as a combination of a few "atoms" or dictionary elements. Ex.
include gene expression analysis, collaborative filtering, and compressive
sensing. In these cases, the optimization problem reduces to an $l_0$
minimization problem, which is a non-convex optimization problem and
NP-Hard in general. There have been numerous methods proposed to "solve"
this problem and all these methods provide guarantees under certain
conditions. Such methods include convex relaxation to an $l_1$ minimization
problem, iterative hard thresholding, and greedy approaches.

In this project, we look at various sparse recovery algorithms :- GradeS,
OMP, OMPR and COSAMP. We look at the problem of how accurately these
algorithms can recover a high dimensional signal from a small set of
measurements and provide performance guarantees on them. These methods
reflect different classes of algorithms with varying requisite conditions,
cost, guarantees, and different operating principles in general. We study
the various mathematical properties associated with the problem and the
performance of these methods with respect to various parameters like error,
sparsity and number of dimensions.


Title : Diachronic Word Sense Change Identification (NLP course Project)
Speaker : Raghuveer
Timing : 4:30 PM - 5:00 PM , 9 January 16
Day : Saturday

* : *Language has continuously changed over time. New senses for some words
took birth while some old senses demised. For an example the word ‘gay’
which referred being full of joy about 2 centuries ago but is now used to
refer to a homosexual; ‘economy’ referred to management of resources in the
past and is now being referred to state of country in terms of productions.
In this work we find words which changed their senses over time by using
Word vector models trained from different time epoches and plotting their
inter cosine similarity to find the words of interest.