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Performance Characterization and Optimizations of Traditional ML Applications
Even in the era of Deep Learning based methods, traditional machine learning methods with large data sets continue to attract significant attention. However, we find an apparent lack of a detailed performance characterization ...
Recovery Algorithms for planted structures in Semi-random models
For many NP-hard problems, the analysis of best-known approximation algorithms yields “poor” worst-case guarantees. However, using various heuristics, the problems can be solved (to some extent) in real-life instances. ...
Algorithms for Challenges to Practical Reinforcement Learning
Reinforcement learning (RL) in real world applications faces major hurdles - the foremost being safety of the physical system controlled by the learning agent and the varying environment conditions in which the autonomous ...
Practically Efficient Secure Small Party Computation over the Internet
Secure Multi-party Computation (MPC) with small population has drawn focus specifically
due to customization in techniques and resulting efficiency that the constructions can offer.
Practically efficient constructions ...
Revisiting Statistical Techniques for Result Cardinality Estimation
The Relational Database Management Systems (RDBMS) constitute the backbone of today's information-rich society, providing a congenial environment for handling enterprise data during its entire life cycle of generation, ...
Recommendations in Complex Networks: Unifying Structure into Random Walk
Making recommendations or predicting links which are likely to exist in the future is one
of the central problems in network science and graph mining. In spite of modern state-of-
the-art approaches for link prediction, ...
Robust Algorithms for recovering planted structures in Semi-random instances
In this thesis, we study algorithms for three fundamental graph problems. These are NP-hard problems which have not been understood completely as there is a signifiicant gap between the algorithmic and the hardness fronts ...
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning
Stochastic approximation algorithms are sequential non-parametric methods for finding a zero
or minimum of a function in the situation where only the noisy observations of the function
values are available. Two time-scale ...
Learning to Adapt Policies for uSD card
Machine Learning(ML) for Systems is a new and promising research area where performance
of computer systems is optimized using machine learning methods. ML for Systems has outperformed
traditional heuristics methods in ...
Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
The classical Stochastic Multi-armed Bandit (MAB) problem provides an abstraction for many
real-world decision making problems such as sponsored-search auctions, crowd-sourcing, wireless
communication, etc. In this work, ...