Results for "sensitivity to data"
Reinforcement learning from human feedback: uses preference data to train a reward model and optimize the policy.
Systematic differences in model outcomes across groups; arises from data, labels, and deployment context.
A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.
Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.
Systematic review of model/data processes to ensure performance, fairness, security, and policy compliance.
Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.
Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.
Ability to replicate results given same code/data; harder in distributed training and nondeterministic ops.
Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate data.
Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.
Methods to protect model/data during inference (e.g., trusted execution environments) from operators/attackers.
Estimating parameters by maximizing likelihood of observed data.
Empirical laws linking model size, data, compute to performance.
Learning from data generated by a different policy.
Learning only from current policy’s data.
Recovering training data from gradients.
Inferring sensitive features of training data.
Detecting unauthorized model outputs or data leaks.
Neural networks that operate on graph-structured data by propagating information along edges.
Probabilistic model for sequential data with latent states.
Models that learn to generate samples resembling training data.
Sequential data indexed by time.
Identifying abrupt changes in data generation.
Trend reversal when data is aggregated improperly.
Scaling law optimizing compute vs data.
Competitive advantage from proprietary models/data.
Probability of data given parameters.
Updated belief after observing data.
Belief before observing data.
Software pipeline converting raw sensor data into structured representations.