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researcher

Zheng Wen

DeepMind, Stanford University

56 papers hereh-index 304.1k citations82 works total

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

author position
  • first author5
  • middle author36
  • last author13

Across the 54 of 56 papers where every author was matched, so the position is known.

fields
  • cs.LG36
  • stat.ML8
  • cs.CL2
  • cs.GT2
  • cs.IR2
  • math.OC2
affiliations
  • DeepMind, Stanford University
Homepage
same name
  • Zheng Wen — 6 papers, h 3
  • Zheng Wen — 6 papers, h 4
  • Zheng Wen — 4 papers, h 2
  • Zheng Wen — 3 papers, h 15
  • Zheng Wen — 1 paper
  • Zheng Wen — 1 paper, h 2

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
20132026
most citedDeep Exploration via Randomized Value Functions

160 citations · 1.2k across the 55 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

cs.GT2015

Exponential Weight Functions for Quasi-Proportional Auctions

Eric Bax, James Li, Zheng Wen

In quasi-proportional auctions, the allocation is shared among bidders in proportion to their weighted bids. The auctioneer selects a bid weight function, and bidders know the weig…

cs.LG2015★ 57 cited

Combinatorial Cascading Bandits

Branislav Kveton, Zheng Wen, Azin Ashkan +1

We propose combinatorial cascading bandits, a class of partial monitoring problems where at each step a learning agent chooses a tuple of ground items subject to constraints and re…

cs.GT2015

Revenue-Maximizing Mechanism Design for Quasi-Proportional Auctions

Zheng Wen, Eric Bax, James Li

In quasi-proportional auctions, each bidder receives a fraction of the allocation equal to the weight of their bid divided by the sum of weights of all bids, where each bid's weigh…

cs.LG2015★ 108 cited

Cascading Bandits: Learning to Rank in the Cascade Model

Branislav Kveton, Csaba Szepesvari, Zheng Wen +1

A search engine usually outputs a list of K web pages. The user examines this list, from the first web page to the last, and chooses the first attractive page. This model of user…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.