Jelani Nelson
where else to find them
no profile links on file
what they talk about
all 4 in the filters
where they keep going back
no series more than once — all 6 in the filters
on a stage with
- Badih Ghazionce
- Edith Cohenonce
- Pasin Manurangsionce
all 7 in the filters
who talks about the same things
- Pasin Manurangsi8 talks
- Badih Ghazi4 talks
- Edith Cohen6 talks
- Uri Stemmer3 talks
- Aaron Roth11 talks
- Oded Goldreich4 talks
| title | conference | date |
|---|---|---|
| New upper and lower bounds on approximate counting | Extroverted Sublinear Algorithms 2024 | 2024-06-17 |
Proves tight upper and lower bounds for Morris-style approximate counters, replacing the old log 1/δ failure-term by a log-log 1/δ dependence and showing this… | ||
| Sublinear Algorithms | 9th Annual Simons Institute Industry Day | 2023-11-02 |
Program runs May 20–August 9 2024 and lists 29 long-term participants with applications still open. Core themes are streaming and sketching algorithms… | ||
| Lightning Talks | Sketching and Algorithm Design 2023 | 2023-10-09 |
Seven three-minute updates cover concrete results: a streaming correlation-clustering multipass implementation and covering-in-streams algorithms with tight… | ||
| Generalized Private Selection and Testing with High Confidence | ITCS 2023 | 2023-01-10 |
A unified framework produces a private selection algorithm that attains 2 + o(1) pure DP selection from arbitrarily many epsilon‑DP candidates by randomized… | ||
| Private Counting of Distinct and k-Occurring Items in Time Windows | ITCS 2023 | 2023-01-10 |
Paper gives private algorithms for counting distinct and k-occurring items over time windows by reducing these queries to differentially private range queries… | ||
| Sketching High-Dimensional Data | Probability, Geometry, and Computation in High Dimensions Boot Camp (Simons Institute, Fall 2020) | 2020-08-19 |
Estimator uses the minimum hashed value of each distinct item (returning 1/X−1), then reduces variance by averaging R independent copies (R ≈ O(1/ε^2)) and… | ||
| Sketching High-Dimensional Data | Probability, Geometry, and Computation in High Dimensions Boot Camp (Simons Institute, Fall 2020) | 2020-08-19 |
Optimal space for one plus epsilon distinct counting is 1/ε^2 log(1/δ)+log n, and both lower and upper bounds are given; deterministic exact algorithms require… | ||
| Sketching High-Dimensional Data | Probability, Geometry, and Computation in High Dimensions Boot Camp (Simons Institute, Fall 2020) | 2020-08-19 |
Linear sketches estimate norms and heavy hitters with concrete bounds: AMS sketch uses m = Θ(ε^{-2}) rows and O(ε^{-2} log(1/δ)) words to get 1+ε L2 estimates… | ||
| Sketching High-Dimensional Data | Probability, Geometry, and Computation in High Dimensions Boot Camp (Simons Institute, Fall 2020) | 2020-08-19 |
Johnson–Lindenstrauss embedding requires m = O(ε^{-2} log n) and is information-theoretically tight; practical work replaces dense Gaussian maps with faster or… | ||
| Dimensionality Reduction Via Sparse Matrices | Workshop on Succinct Data Representations and Applications (Simons Institute Big Data 2013) | 2013-09-16 |
Shows sparse Johnson–Lindenstrauss matrices that embed N points to m = O(ε^{-2} log N) rows while reducing per-column nonzeros s by an Θ(ε) factor versus prior… | ||
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