Results for "false inference"
Probability of data given parameters.
Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.
Controlled experiment comparing variants by random assignment to estimate causal effects of changes.
Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.
A broader capability to infer internal system state from telemetry, crucial for AI services and agents.
How many requests or tokens can be processed per unit time; affects scalability and cost.
Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples.
System design where humans validate or guide model outputs, especially for high-stakes decisions.
Measures how one probability distribution diverges from another.
Stores past attention states to speed up autoregressive decoding.
Estimating parameters by maximizing likelihood of observed data.
Ensuring decisions can be explained and traced.
Recovering training data from gradients.
Inferring sensitive features of training data.
Probabilistic graphical model for structured prediction.
Probabilistic model for sequential data with latent states.
Graphical model expressing factorization of a probability distribution.
Sequential data indexed by time.
Predicting future values from past observations.
Identifying abrupt changes in data generation.
Directed acyclic graph encoding causal relationships.
Formal model linking causal mechanisms and variables.
Models effects of interventions (do(X=x)).
What would have happened under different conditions.
Expected causal effect of a treatment.
Increasing model capacity via compute.
Variable whose values depend on chance.
Describes likelihoods of random variable outcomes.
Updated belief after observing data.
Belief before observing data.