minimax regret

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4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 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 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 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 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src

topic2 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

papersSEP 11 04:00 UTC

Paper Shows Minimax Regret Possible in Bilateral Trade with Heavy-Tailed Valuations

A new arXiv paper examines contextual bilateral trade where the posted price does not influence which valuations the seller observes. The authors prove that this action-independent feedback structure removes the polynomial adaptation penalty previously associated with heavy-tailed valuation distributions. The result yields minimax regret guarantees without requiring a variance bound.

research/arxiv/economics/theory