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researcher

Raaz Dwivedi

Harvard University, MIT

36 papers hereh-index 161.1k citations50 works total

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

author position
  • first author10
  • middle author17
  • last author7

Across the 34 of 36 papers where every author was matched, so the position is known.

fields
  • stat.ML15
  • cs.LG10
  • math.ST3
  • stat.ME3
  • cs.CL2
  • econ.EM1
affiliations
  • Harvard University, MIT
Homepage
same name
  • Raaz Dwivedi — 1 paper

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
20162026
most citedLog-concave sampling: Metropolis-Hastings algorithms are fast

94 citations · 229 across the 34 of their papers we have counts for

collaborators
Showing 2024 · stat.MLShow all

4 papers · 2 filters

stat.ML2024

Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion

Tathagata Sadhukhan, Manit Paul, Raaz Dwivedi

Nearest neighbor (NN) algorithms have been extensively used for missing data problems in recommender systems and sequential decision-making systems. Prior theoretical analysis has…

stat.ML2024

Learning Counterfactual Distributions via Kernel Nearest Neighbors

Kyuseong Choi, Jacob Feitelberg, Caleb Chin +2

Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate d…

stat.ML2024

Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space

Jacob Feitelberg, Kyuseong Choi, Anish Agarwal +1

We study the problem of distributional matrix completion: Given a sparsely observed matrix of empirical distributions, we seek to impute the true distributions associated with both…

stat.ML2024

Debiased Distribution Compression

Lingxiao Li, Raaz Dwivedi, Lester Mackey

Modern compression methods can summarize a target distribution P more succinctly than i.i.d. sampling but require access to a low-bias input sequence like a Markov chain…

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