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Ion Stoica

68 papers hereh-index 4128.1k citations94 works total

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

author position
  • first author1
  • middle author33
  • last author32

Across the 66 of 68 papers where every author was matched, so the position is known.

fields
  • cs.LG25
  • cs.AI10
  • cs.DC10
  • cs.CL8
  • cs.CV3
  • cs.DB2
same name
  • Ion Stoica — 33 papers, h 17
  • Ion Stoica — 26 papers, h 10
  • Ion Stoica — 18 papers, h 18
  • Ion Stoica — 12 papers, h 5
  • Ion Stoica — 11 papers, h 6
  • Ion Stoica — 10 papers, h 8

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
20142026
most citedJudging LLM-as-a-Judge with MT-Bench and Chatbot Arena

492 citations · 1.5k across the 65 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019★ 37 cited

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

Paras Jain, Ajay Jain, Aniruddha Nrusimha +5

We formalize the problem of trading-off DNN training time and memory requirements as the tensor rematerialization optimization problem, a generalization of prior checkpointing stra…

cs.LG2019★ 20 cited

MLSys: The New Frontier of Machine Learning Systems

Alexander Ratner, Dan Alistarh, Gustavo Alonso +66

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…

cs.LG2019★ 4 cited

Constrained Thompson Sampling for Wireless Link Optimization

Vidit Saxena, Joseph E. Gonzalez, Ion Stoica +2

Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. Fo…

cs.DC2019★ 4 cited

The OoO VLIW JIT Compiler for GPU Inference

Paras Jain, Xiangxi Mo, Ajay Jain +3

Current trends in Machine Learning~(ML) inference on hardware accelerated devices (e.g., GPUs, TPUs) point to alarmingly low utilization. As ML inference is increasingly time-bound…

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