Results for "game theory"

Agents copy others’ actions.

Number of steps considered in planning.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

AI limited to specific domains.

Mathematical framework for controlling dynamic systems.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Storing results to reduce compute.

Stability proven via monotonic decrease of Lyapunov function.

Control that remains stable under model uncertainty.

Optimizing continuous action sequences.

Quantifying financial risk.

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience rather than explicit rule-coding.

A branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data distributions.

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster structure.

Minimizing average loss on training data; can overfit when data is limited or biased.

When information from evaluation data improperly influences training, inflating reported performance.

Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.

Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Tracking where data came from and how it was transformed; key for debugging and compliance.

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

A broader capability to infer internal system state from telemetry, crucial for AI services and agents.

Time from request to response; critical for real-time inference and UX.

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

Converts logits to probabilities by exponentiation and normalization; common in classification and LMs.

A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samples.

A measure of randomness or uncertainty in a probability distribution.

Measures how much information an observable random variable carries about unknown parameters.