## Organisers:

## Time:

## Venue:

**Abstract:** Mathematical proofs when written in conventional ways often contain imprecise definitions, unstated background assumptions, and inferential gaps in reasoning.

Speaker:

Abhishek Singh, TIFR

Wednesday, 15 January 2020, 11:30 to 13:00

**Abstract:** Mathematical proofs when written in conventional ways often contain imprecise definitions, unstated background assumptions, and inferential gaps in reasoning.

Parikshit Gopalan

Monday, 13 January 2020, 14:30 to 15:30

**Abstract: **Anomaly detection is a ubiquitous problem in machine learning. Here one is given a large population of points, we may not have much knowledge about their structure a priori.

Speaker:

Vipin S, TIFR

Tuesday, 28 January 2020, 14:00 to 15:30

The Directed Subgraph Homeomorphism Problem", Fortune, Hopcroft and Wyllie, Theoretical Computer Science, 1980.

Karthikeyan Shanmugam

Friday, 10 January 2020, 14:30 to 15:30

Abstract: Directed Causal Graphs (DAGs) capture causal relationships amongst a set of variables and they specify how interventional distributions relate to observational ones.

R.K. Shyamasundar

Tuesday, 14 January 2020, 14:30 to 15:30

"2nd UNESCO World Logic Day Celebration Lecture"

Speaker:

Vidya Sagar Sharma, TIFR

Friday, 27 December 2019, 17:15 to 18:15

**Abstract: **We study the complexity of simulating a neural network. We simplified the model of a neural network as a directed graph $G(V,E)$, where the nodes represent the neurons.

Anindya De

Monday, 6 January 2020, 10:30 to 11:30

**Abstract:** Consider the following basic problem in sparse linear regression -- an algorithm gets labeled samples of the form (x, <w.x> + \eps) where w is an unknown n-imensional vector, x is drawn from a background distributi

Speaker:

Anirban Bhattacharjee, TIFR

Thursday, 26 December 2019, 15:00 to 16:00

**Abstract:** In the stochastic K-armed bandit problem, an agent or learner is given a set of K probability distributions (informally, `arms').

Poonam Kesarwani

Thursday, 26 December 2019, 11:30 to 12:30

Abstract: Multiobjective Optimization Problems (MOPs) characterize an essential class of optimization problems that involve optimizing more than one objective function simultaneously.

Rohit Parikh

Wednesday, 1 January 2020, 14:30 to 15:30

Reasoning about knowledge is an old area but much of the recent technical work goes back to David Lewis and Jaakko Hintikka. Impetus was given in Computer Science