Does selection bias actually hurt an always-on assistant that learns only from windows where it spoke?

What it found

A stylised follow-up to ambient-ips: on the same logged history, a model fitted only on windows where the assistant spoke is compared with an inverse-propensity-weighted fit and with a full-information fit that sees every window’s label. The full-information fit reaches the oracle (12.72 against 12.71 utility per hour); the biased fit loses 1.3 to 2.1 per hour with an informed prior and fails with an uninformed one, so the bias is large, contrary to a guess in ambient-ips. Weighting recovers part of the loss only at ε = 0.02 with an informed prior. The comparison also mixes bias with data volume (no label-matched control). Stylised, simulated only.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Ambient AI assistant

• Is the gap in an assistant that learns only from windows where it spoke caused by selection bias or by too few labels?Is the self-selected-feedback gap bias or label volume: mostly bias (a random sample of equal size nearly reaches the oracle); clipped weights help only with an informed prior (stylised)
• Does a doubly robust estimator fix learning from an assistant’s own self-selected feedback?Does a doubly robust estimator beat IPS on an assistant's self-selected feedback: no, a stylised negative result; its pseudo-labels carry 1/ε residuals and are worse than the biased fit except with an informed prior.
• Does capping the inverse-propensity correction rescue doubly robust learning from an assistant’s own feedback?Does capping the inverse-propensity correction rescue doubly robust learning from an assistant's own feedback: with an informed prior yes (the collapse at ε = 0.002 goes from 0.26 to about 12 utility/h), with an uninformed prior no; stylised, simulated only.
• Does cross-fitting the outcome model help capped doubly robust learning from an assistant’s own feedback?Does fitting the doubly robust outcome model out-of-sample (2-fold cross-fitting) help the capped correction: no, a negative result; differences are within seed noise or worse, and the unclipped collapse at ε = 0.002 persists (1.53 vs 0.26 utility/h); stylised, simulated only.
• Can the doubly robust cap be chosen by validation on an assistant’s own logs?Can the capped doubly robust cap be chosen by 4-fold IPS validation on the log: with an informed prior it matches the best fixed cap within seed noise, with an uninformed prior it does not rescue learning; stylised, simulated only.
• Does a doubly robust validation score choose the doubly robust cap better than an IPS score?A doubly robust validation score does not pick the DR cap better than IPS with the same 4 folds (equal with an informed prior, mixed with the uninformed one); 8 random folds with the DR score is best in all three uninformed cells but still far below the label-matched control (stylised).

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.