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J. Nair

30 papers hereh-index 12693 citations72 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author13
  • last author16

Across the 29 of 30 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.NI5
  • math.OC5
  • cs.DS3
  • cs.PF3
  • eess.SY3
same name
  • J. Nair — 8 papers, h 38
  • J. Nair — 1 paper, h 2
  • J. Nair — 1 paper, h 1
  • J. Nair — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedDistribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards

18 citations · 33 across the 21 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.LG2022★ 1 cited

Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget

Fathima Zarin Faizal, Jayakrishnan Nair

We consider a constrained, pure exploration, stochastic multi-armed bandit formulation under a fixed budget. Each arm is associated with an unknown, possibly multi-dimensional dist…

math.OC2022

Pricing, competition and market segmentation in ride hailing

Tushar Shankar Walunj, Shiksha Singhal, Veeraruna Kavitha +1

We analyse a non-cooperative strategic game among two ride-hailing platforms, each of which is modeled as a two-sided queueing system, where drivers (with a certain patience level)…

math.OC2022★ 1 cited

Non-asymptotic near optimal algorithms for two sided matchings

Rahul Vaze, Jayakrishnan Nair

A two-sided matching system is considered, where servers are assumed to arrive at a fixed rate, while the arrival rate of customers is modulated via a price-control mechanism. We a…

cs.LG2022

Unsupervised Crowdsourcing with Accuracy and Cost Guarantees

Yashvardhan Didwania, Jayakrishnan Nair, N. Hemachandra

We consider the problem of cost-optimal utilization of a crowdsourcing platform for binary, unsupervised classification of a collection of items, given a prescribed error threshold…

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