Results for "game theory"

Estimating parameters by maximizing likelihood of observed data.

Updating beliefs about parameters using observed evidence and prior distributions.

Optimization problems where any local minimum is global.

Optimization with multiple local minima/saddle points; typical in neural networks.

Neural networks can approximate any continuous function under certain conditions.

Fundamental recursive relationship defining optimal value functions.

Coordination arising without explicit programming.

Required human review for high-risk decisions.

Recovering training data from gradients.

Embedding signals to prove model ownership.

Neural networks that operate on graph-structured data by propagating information along edges.

Predicting future values from past observations.

Models time evolution via hidden states.

Agents communicate via shared state.

Distributed agents producing emergent intelligence.

Increasing performance via more data.

Variable whose values depend on chance.

Describes likelihoods of random variable outcomes.

Average value under a distribution.

Measure of spread around the mean.

Sample mean converges to expected value.

Sum of independent variables converges to normal distribution.

Probability of data given parameters.

Eliminating variables by integrating over them.

Lowest possible loss.

Flat high-dimensional regions slowing training.

Restricting updates to safe regions.

Converts constrained problem to unconstrained form.

Alternative formulation providing bounds.

Optimization under uncertainty.