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researcher

Handong Zhao

27 papers hereh-index 283.6k citations87 works total

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

author position
  • middle author24
  • last author3

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.CL7
  • cs.LG6
  • cs.IR3
  • cs.AI1
  • cs.HC1
same name
  • Handong Zhao — 5 papers, h 3
  • Handong Zhao — 5 papers, h 5
  • Handong Zhao — 3 papers, h 4
  • Handong Zhao — 3 papers, h 2
  • Handong Zhao — 2 papers, h 3
  • Handong Zhao — 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
20192024
most citedCAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

89 citations · 279 across the 27 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023★ 4 cited

InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding

Junda Wu, Tong Yu, Rui Wang +6

Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…

cs.CL2023

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning

Kaige Xie, Tong Yu, Haoliang Wang +6

In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To effici…

cs.LG2023

Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model

Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim +4

We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer…

cs.LG2023★ 13 cited

Better Generative Replay for Continual Federated Learning

Daiqing Qi, Handong Zhao, Sheng Li

Federated learning is a technique that enables a centralized server to learn from distributed clients via communications without accessing the client local data. However, existing…

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