Contextual Bandits: A Literature Review

Here in this post, I will provide excerpts from some of the most influential works...

Adversarial Bandits: Hedge and Exp3

In the case of stochastic bandits, our estimators such as the sample mean or the...

Stochastic Bandits: Optimism In The Face of Uncertainty

The previous two algorithms had regret bounds in the order of $\mathcal{O}(n^{\frac{2}{3}} (k\log{n})^{\frac{1}{3}})$. But with...

ML Theory: PAC Learnability

The Multi-Class Classification Problem: Let’s say, we have a domain $\mathcal{X}$ where each element $x...

Stochastic Bandits: Successive Elimination

Stochastic Bandits: The MOSS Algorithm

Stochastic Bandits: The Epsilon Greedy Algorithm

Previously, we saw that in the ETC algorithm, we first explore all of the arms...

Stochastic Bandits: Explore Then Commit

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Multi-Armed Bandits: Exploration vs Exploitation

Research Area and Interests

Broadly, I am interested in almost everything that ends with “learning”, for example, Machine Learning,...