43 citations · 154 across the 32 of their papers we have counts for
12 papers · 1 filter
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Traditional multi-armed bandit (MAB) formulations usually make certain assumptions about the underlying arms' distributions, such as bounds on the support or their tail behaviour.…
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha +1
We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical ro…
Constrained regret minimization for multi-criterion multi-armed bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
We consider a stochastic multi-armed bandit setting and study the problem of constrained regret minimization over a given time horizon. Each arm is associated with an unknown, poss…
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks
Neema Davis, Gaurav Raina, Krishna Jagannathan
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Lon…
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory
Neema Davis, Gaurav Raina, Krishna Jagannathan
In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation…
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Classical multi-armed bandit problems use the expected value of an arm as a metric to evaluate its goodness. However, the expected value is a risk-neutral metric. In many applicati…
Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts
Neema Davis, Gaurav Raina, Krishna Jagannathan
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate t…
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Prashanth L. A., Krishna Jagannathan, Ravi Kumar Kolla
Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases o…
Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks
Neema Davis, Gaurav Raina, Krishna Jagannathan
In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i)…
Concentration bounds for empirical conditional value-at-risk: The unbounded case
Ravi Kumar Kolla, Prashanth L. A., Sanjay P. Bhat +1
In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control loss…
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy
Neema Davis, Gaurav Raina, Krishna Jagannathan
A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a sig…
Collaborative Learning of Stochastic Bandits over a Social Network
Ravi Kumar Kolla, Krishna Jagannathan, Aditya Gopalan
We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each…