| Year | Rank | Type | Title / Venue / Authors |
|---|---|---|---|
| 2025 | J | jnl |
CoRR
|
| 2025 | A* | conf |
ICML
|
| 2024 | A | conf |
AISTATS
|
| 2024 | J | jnl |
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data.
Found. Comput. Math.
|
| 2024 | J | jnl |
CoRR
|
| 2024 | J | jnl |
CoRR
|
| 2023 | J | jnl |
CoRR
|
| 2023 | J | jnl |
IEEE Trans. Inf. Theory
|
| 2023 | J | jnl |
CoRR
|
| 2022 | J | jnl |
SIAM J. Math. Data Sci.
|
| 2022 | J | jnl |
CoRR
|
| 2021 | J | jnl |
SIAM J. Math. Data Sci.
|
| 2021 | J | jnl |
Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data.
CoRR
|
| 2021 | J | jnl |
CoRR
|
| 2021 | J | jnl |
CoRR
|
| 2020 | J | jnl |
CoRR
|
| 2020 | — | conf |
MSML
|
| 2019 | J | jnl |
CoRR
|