Results for "alternative outcomes"

Alternative formulation providing bounds.

What would have happened under different conditions.

An RNN variant using gates to mitigate vanishing gradients and capture longer context.

Matrix of second derivatives describing local curvature of loss.

Early architecture using learned gates for skip connections.

Generator produces limited variety of outputs.

A proper scoring rule measuring squared error of predicted probabilities for binary outcomes.

Using production outcomes to improve models.

Systematic differences in model outcomes across groups; arises from data, labels, and deployment context.

A measure of randomness or uncertainty in a probability distribution.

Expected causal effect of a treatment.

Agent reasoning about future outcomes.

Variable whose values depend on chance.

Describes likelihoods of random variable outcomes.

Grouping patients by predicted outcomes.

Predicting disease progression or survival.

Unequal performance across demographic groups.

Requirement to reveal AI usage in legal decisions.

Agents optimize collective outcomes.

No agent can improve without hurting another.

Designing systems where rational agents behave as desired.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.

The degree to which predicted probabilities match true frequencies (e.g., 0.8 means ~80% correct).

Penalizes confident wrong predictions heavily; standard for classification and language modeling.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and experiments.

Logging hyperparameters, code versions, data snapshots, and results to reproduce and compare experiments.

Stochastic generation strategies that trade determinism for diversity; key knobs include temperature and nucleus sampling.

Scales logits before sampling; higher increases randomness/diversity, lower increases determinism.