Can the doubly robust cap be chosen by validation on an assistant’s own logs?
research/ambient-capcv
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.
What it found
A stylised follow-up to ambient-crossfit. The cap on the inverse-propensity correction is chosen from {2, 5, 10, 20, 50, 100, ∞} by 4-fold validation, scoring each candidate with an uncapped inverse-propensity estimate of its utility. With an informed prior the chosen cap matches the best fixed cap to within seed noise (12.23 against 12.19 utility/h at ε = 0.005; 11.45 against 11.84 at 0.002, with a wider spread). With an uninformed prior it does not rescue learning (between −21 and −1 utility/h with seed standard deviations of 24–51, against about 12 for a randomly sampled log), and the validation score is noisy enough that chosen caps scatter across the grid. Stylised, simulated only; 20 seeds, no significance tests.
A figure from its own output
Paper, code and results
More in Ambient AI assistant
- 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).
- Which of score, fold count and fold type made cap selection better in ambient-drvalid?Ablating the three changes in
ambient-drvalid(score, fold count, fold type): in the uninformed cells random folds help more than the DR score (IPS on 8 random folds is as good as DR on 8), no factor wins everywhere, and every selector stays far below the label-matched control (stylised, negative). - Do repeated random fold assignments stabilise validation-based cap selection?Repeated random fold assignments for cap selection in the ambient-assistant world (follow-up to
ambient-drablate): averaging 5 assignments helps the IPS-score selector at ε = 0.02 (3.5 → 5.3 utility/h) and nothing else beyond noise; stylised, simulated only. - Does fitting only on the windows an assistant explored at random fix learning from its own feedback?Fitting only on the windows an assistant explored at random fixes the uninformed-prior failure of learning from its own feedback (11.99 vs −13.08 utility/h at ε = 0.02), at an exploration cost (stylised)
- How many false wakes a day does an always-on trigger get when its frame scores are correlated, and what does requiring m frames in a row buy?How many false wakes a day does an always-on trigger get when its frame scores are correlated? Exact grid-chain rates for "m frames in a row" on AR(1) scores, checked by Monte Carlo: a threshold set by the independent-frames rule gives 613 / 4,793 / 20,993 wakes a day (not 1) for m = 2 / 3 / 5 at phi = 0.9, while a single-frame threshold is nearly unaffected. Stylised.
- How bursty are an always-on trigger's false-wake counts, and does a lockout help?How bursty are an always-on trigger's false-wake counts? On AR(1) frame scores the Fano factor is near 1 (1.05–1.5), but a 5% window-to-window drift in noise scale lifts it to about 9 for m = 3 (38 at 10%); a 10 s lockout cuts it to 2.5 but also cuts the mean by a third. Stylised.
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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.