Many real-world planning tasks involve both harder “quantitative” constraints (e.g., budgets or scheduling requirements) and softer “qualitative” objectives (e.g., user preferences expressed in natural language). Consider someone planning a week-long vacation. Typically, this planning would be subject to various clearly quantifiable constraints, such as budget, travel logistics, and visiting attractions only when they are open, in addition to a number of constraints based on personal ...| research.google
The digital age offers ever growing access to vast amounts of knowledge, yet much remains locked behind complex language and specialist jargon. While complexity is often necessary in expert discourse, it can become a barrier when users need to understand information critical to their lives, such as navigating health information, understanding legal language, or grasping financial details. Tools that let users produce a simplified version of complex text that they encounter online can empower ...| research.google
The machine learning community has consistently found that while modern machine learning (ML) models are powerful, they often need to be fine-tuned on domain-specific data to maximize performance. This can be problematic or even impossible, as informative data is often privacy-sensitive. Differential privacy (DP) allows us to train ML models while rigorously guaranteeing that the learned model respects the privacy of its training data, by injecting noise into the training process.| research.google
