# Past Events

# Ranking Problems in Machine Learning: Theory and Applications

## Speaker:

## Time:

## Venue:

In the last few decades, there has been considerable progress in the understanding of binary classification (learning of binary-valued functions) and regression (learning of real-valued functions), both classical problems in machine learning.

# Adaptive Sampling for k-means Clustering

## Speaker:

## Time:

## Venue:

k-means clustering is a theoretically hard problem but in practice it is often solved efficiently using a simple heuristic due to Lloyd.

# How many times must a deck of cards be shuffled until it is close to random?

## Speaker:

## Time:

## Venue:

We will try to answer the above question by analyzing the stopping times (which is the time after which the deck of cards is completely random) of the card shuffling process.

# Evasiveness and the Music of Primes

## Speaker:

## Time:

## Venue:

A boolean function f on N variables is called evasive if its decision tree complexity is N, i.e., one must query *all* the variables (in worst case) in order to decide if f(X) = 1.

# QIP = PSPACE

## Speaker:

## Time:

## Venue:

I propose to give a series of lectures explaining the recent paper by Rahul Jain, Zhengfeng Ji, Sarvagya Upadhyay and John Watrous showing that the class of problems having quantum interactive proofs is the same as the class of problems having cla

# Winding Line on a Torus

## Speaker:

## Time:

## Venue:

The torus is one of the most important geometrical objects in mathematics. As a topological space it is just a product of two circles. The is a natural continuous mapping from the real plane to the torus which is called the exponential map.

# Workshop on Stochastic Methods: Analysis and Algorithms

## Speaker:

## Time:

## Venue:

## Webpage:

# Automata-theoretic Modeling of Streaming Applications

## Speaker:

## Time:

## Venue:

Lately, there has been a considerable amount of interest in design methodologies for embedded systems that are specifically targeted towards stream processing, e.g., audio/video applications and control applications processing sensor data.

# Graph Drawing with no k Pairwise Crossing Edges

## Speaker:

## Time:

## Venue:

We will try to find an upper bound on the number of edges in a graph with no k pairwise crossing edges.