Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Fanti, Giulia"'
In the international development community, the term "digital public goods" is used to describe open-source digital products (e.g., software, datasets) that aim to address the United Nations (UN) Sustainable Development Goals. DPGs are increasingly b
Externí odkaz:
http://arxiv.org/abs/2406.15842
Autor:
Hou, Charlie, Shrivastava, Akshat, Zhan, Hongyuan, Conway, Rylan, Le, Trang, Sagar, Adithya, Fanti, Giulia, Lazar, Daniel
On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several drawbacks: (1) most user devices are too small to train large models on
Externí odkaz:
http://arxiv.org/abs/2406.02958
Diffusion models have shown great promise for image and video generation, but sampling from state-of-the-art models requires expensive numerical integration of a generative ODE. One approach for tackling this problem is rectified flows, which iterati
Externí odkaz:
http://arxiv.org/abs/2405.20320
Differentially private mechanisms achieving worst-case optimal error bounds (e.g., the classical Laplace mechanism) are well-studied in the literature. However, when typical data are far from the worst case, \emph{instance-specific} error bounds -- w
Externí odkaz:
http://arxiv.org/abs/2403.10116
Differentially private (DP) machine learning pipelines typically involve a two-phase process: non-private pre-training on a public dataset, followed by fine-tuning on private data using DP optimization techniques. In the DP setting, it has been obser
Externí odkaz:
http://arxiv.org/abs/2402.18905
Differential privacy (DP) is increasingly used to protect the release of hierarchical, tabular population data, such as census data. A common approach for implementing DP in this setting is to release noisy responses to a predefined set of queries. F
Externí odkaz:
http://arxiv.org/abs/2401.18024
Publikováno v:
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4672-4680, 2024
Long-term time series forecasting (LTSF) aims to predict future values of a time series given the past values. The current state-of-the-art (SOTA) on this problem is attained in some cases by linear-centric models, which primarily feature a linear ma
Externí odkaz:
http://arxiv.org/abs/2312.06786
Autor:
Luhanga, Edith, Sowon, Karen, Cranor, Lorrie Faith, Fanti, Giulia, Tucker, Conrad, Gueye, Assane
Mobile money services in Sub-Saharan Africa (SSA) have increased access to financial services. To ensure proper identification of users, countries have put in place Know-Your-Customer (KYC) measures such as SIM registration using an official identifi
Externí odkaz:
http://arxiv.org/abs/2311.00830
Autor:
Sowon, Karen, Luhanga, Edith, Cranor, Lorrie Faith, Fanti, Giulia, Tucker, Conrad, Gueye, Assane
Digital financial services have catalyzed financial inclusion in Africa. Commonly implemented as a mobile wallet service referred to as mobile money (MoMo), the technology provides enormous benefits to its users, some of whom have long been unbanked.
Externí odkaz:
http://arxiv.org/abs/2309.00226
Autor:
Hou, Charlie, Thekumparampil, Kiran Koshy, Shavlovsky, Michael, Fanti, Giulia, Dattatreya, Yesh, Sanghavi, Sujay
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while significantly underperforming them on outlier data. However, these works of
Externí odkaz:
http://arxiv.org/abs/2308.00177