LIVE PULSE
5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 Study traces LLM hallucinations to competing latent associations — 1 src1.3 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.3 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 Study traces LLM hallucinations to competing latent associations — 1 src1.3 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.3 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
HEATPULSEAI MAGAZINES
FLIP · FOLLOW · SAVE
Log inSign up

multi-armed bandits

topic4 events
papersTODAY 04:00 UTC

Paper Sets Minimax Regret Bounds for Bandits with Probing Feedback

A new arXiv paper studies a bandit setting where a learner may probe up to k of n arms per round and only observes the highest reward among those probed, rather than each individual reward. The authors derive two minimax laws characterizing when probing yields a statistical advantage over standard bandit learning, covering independent stochastic reward models. They also identify limits on what can be learned from this winner-only feedback.

research/bandits/regret-bounds/theory
papersTODAY 04:00 UTC

New Gap Entropy Method Nears Instance-Wise Optimal Best-Arm Identification

Researchers introduce a quantity called gap entropy for the best-arm identification problem with independent Gaussian arms, where the goal is to find the highest-mean arm using as few samples as possible at a given confidence level. They show that an algorithm based on this measure comes close to the optimal sample complexity for each individual problem instance. The work is a theoretical contribution posted to arXiv and has not yet been peer reviewed.

arxiv/bandits/sample-complexity/theory
papersTODAY 04:00 UTC

Optimal Switching Regret Bounds for Multi-Armed Bandits Against Oblivious Adversaries

This paper studies adversarial multi-armed bandit problems in which the benchmark arm sequence may change up to S times over the course of play, a setting known as switching regret. It reviews and develops regret guarantees of order the square root of (S+1)KT, which prior work showed is achievable when S is known in advance. The work aims to pin down the optimal achievable rate under an oblivious adversary.

bandits/theory/online-learning/arxiv
papersSEP 10 04:00 UTC

Researchers prove gap-entropy conjecture for fixed-confidence best-arm identification

A new arXiv paper in machine learning theory settles the gap-entropy conjecture, an open problem in best-arm identification for multi-armed bandits. The proof covers the fixed-confidence setting with independent unit-variance Gaussian arms, means bounded in [0,1], and a single optimal arm. The result confirms that the entropy of suboptimality gaps governs the sample complexity needed to identify the best arm.

bandits/theory/sample-complexity/best-arm-identification