#regret-bounds

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4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src

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papersTODAY 04:00 UTC

KL-Regularized Contextual Bandits Achieve Logarithmic Regret via Greedy Sampling

A new arXiv paper analyzes KL-regularized contextual bandits under both reward and preference feedback. The authors show that a greedy sampling approach attains logarithmic regret without an explicit dependence on the eluder dimension. The work covers regret guarantees for the reward-feedback setting and extends the analysis to preference-based feedback.

bandits/research/theory/regret-bounds

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