activity
20192021
most citedRisk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty

26 citations · 52 across the 7 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

econ.EM2020

What can be learned from satisfaction assessments?

Naftali Cohen, Simran Lamba, Prashant Reddy

Companies survey their customers to measure their satisfaction levels with the company and its services. The received responses are crucial as they allow companies to assess their…

cs.CL2020

DocuBot : Generating financial reports using natural language interactions

Vineeth Ravi, Selim Amrouni, Andrea Stefanucci +3

The financial services industry perpetually processes an overwhelming amount of complex data. Digital reports are often created based on tedious manual analysis as well as visualiz…

cs.MA2020★ 6 cited

Calibration of Shared Equilibria in General Sum Partially Observable Markov Games

Nelson Vadori, Sumitra Ganesh, Prashant Reddy +1

Training multi-agent systems (MAS) to achieve realistic equilibria gives us a useful tool to understand and model real-world systems. We consider a general sum partially observable…

cs.LG2020★ 26 cited

Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty

Nelson Vadori, Sumitra Ganesh, Prashant Reddy +1

We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems. While risk-sensitive formulations for Markov decision proce…

cs.LG2020★ 1 cited

Heuristics for Link Prediction in Multiplex Networks

Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen +2

Link prediction, or the inference of future or missing connections between entities, is a well-studied problem in network analysis. A multitude of heuristics exist for link predict…

q-fin.ST2020

Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise

Chirag Nagpal, Robert E. Tillman, Prashant Reddy +1

We consider the problem of aggregating predictions or measurements from a set of human forecasters, models, sensors or other instruments which may be subject to bias or miscalibrat…