SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Inference strategy	Instance @3	Macro @3	Time/Speed-up @3	Instance @5	Macro @5	Time/Speed-up @5
P	R	P	R	F1	Cov	T	k' = 100	k' = 1000	P	R	P	R	F1	Cov	T	k' = 100	k' = 1000
EURLEX-4K
PLT-Top-1000	-	-	-	-	-	27.80	0.58	1.00	-	-	-	-	-	-	-	27.80	0.58	1.00
PLT-Top-100	-	-	-	-	-	16.19	1.00	1.72	-	-	-	-	-	-	-	16.19	1.00	1.72
PLT-Top-K	67.28	39.78	20.65	12.59	14.71	39.66	2.12	7.62	13.09	56.28	54.53	23.51	20.02	20.41	50.73	2.95	5.50	9.44
PLT-PS-K	67.45	39.90	21.27	13.36	15.43	41.19	5.82	2.78	4.77	56.53	54.85	25.77	25.16	24.09	59.71	5.82	2.78	4.77
PLT-POW-K β=0.5	67.03	39.61	21.79	14.57	16.45	43.43	8.00	2.03	3.48	54.63	53.03	25.62	27.39	24.93	63.19	8.00	2.03	3.48
PLT-LOG-K	67.42	39.86	20.87	12.87	14.98	40.18	4.15	3.90	6.70	56.70	54.92	24.53	23.01	22.50	55.48	4.15	3.90	6.70
PLT-MACRO-PGREED	19.68	11.39	25.49	16.05	14.94	57.95	14.02	1.16	1.98	13.86	13.33	28.92	18.09	18.15	61.50	16.74	0.97	1.66
PLT-MACRO-RPRIOR	54.53	32.09	24.61	21.72	20.96	56.38	14.21	1.14	1.96	38.04	36.97	24.76	28.69	23.40	67.90	14.21	1.14	1.96
PLT-COVGERED	34.19	19.84	23.80	20.84	18.65	63.11	6.58	2.46	4.22	24.79	23.88	21.10	25.91	18.76	68.84	8.79	1.84	3.16
AMAZONCAT-13K
PLT-Top-1000	-	-	-	-	-	2952.01	0.26	1.00	-	-	-	-	-	-	-	2952.01	0.26	1.00
PLT-Top-100	-	-	-	-	-	768.51	1.00	3.84	-	-	-	-	-	-	-	768.51	1.00	3.84
PLT-Top-K	78.44	59.42	33.16	11.09	14.79	40.97	66.31	11.59	44.52	63.71	74.66	44.40	30.69	33.00	61.09	87.49	8.78	33.74
PLT-PS-K	78.27	59.32	37.09	13.08	17.39	46.23	229.04	3.36	12.89	63.56	74.55	50.10	48.12	45.94	78.46	229.04	3.36	12.89
PLT-POW-K β=0.5	72.20	54.56	41.86	22.84	27.46	60.19	481.53	1.60	6.13	56.06	66.33	40.20	60.10	45.28	84.52	481.53	1.60	6.13
PLT-LOG-K	76.15	57.63	37.90	14.01	18.59	47.89	142.18	5.41	20.76	61.44	72.13	48.26	42.91	42.68	72.71	142.18	5.41	20.76
PLT-MACRO-PGREED	3.98	2.07	42.12	32.69	20.55	85.07	1135.02	0.68	2.60	2.67	2.30	54.90	34.99	32.58	86.40	1127.85	0.68	2.62
PLT-MACRO-RPRIOR	46.83	31.15	34.79	49.92	37.54	79.72	1248.41	0.62	2.36	31.02	33.37	27.86	68.51	35.42	90.11	1248.41	0.62	2.36
PLT-COVGERED	6.20	3.26	29.86	45.04	20.32	89.03	965.45	0.80	3.06	3.96	3.42	24.46	48.22	17.08	91.33	1082.03	0.71	2.73
WIKI-31K
PLT-Top-1000	-	-	-	-	-	638.46	0.38	1.00	-	-	-	-	-	-	-	638.46	0.38	1.00
PLT-Top-100	-	-	-	-	-	239.52	1.00	2.67	-	-	-	-	-	-	-	239.52	1.00	2.67
PLT-Top-K	72.30	12.55	1.97	0.31	0.48	3.06	16.45	14.56	38.82	63.30	18.05	2.98	0.69	1.01	4.91	25.57	9.37	24.97
PLT-PS-K	72.00	12.53	3.47	0.88	1.24	5.47	100.75	2.38	6.34	63.83	18.36	6.79	2.97	3.70	11.44	100.75	2.38	6.34
PLT-POW-K β=0.5	64.46	11.25	5.94	2.35	3.00	10.17	227.97	1.05	2.80	53.91	15.51	9.58	5.78	6.52	17.42	227.97	1.05	2.80
PLT-LOG-K	65.28	11.26	3.22	0.82	1.17	5.39	73.84	3.24	8.65	57.32	16.28	5.68	2.33	2.99	10.17	73.84	3.24	8.65
PLT-MACRO-PGREED	24.66	4.15	10.19	4.81	5.71	17.64	268.05	0.89	2.38	18.83	5.24	11.63	6.86	7.57	21.83	337.87	0.71	1.89
PLT-MACRO-RPRIOR	30.96	5.33	9.79	6.01	6.38	17.37	422.81	0.57	1.51	22.97	6.52	11.88	9.57	8.77	23.24	422.81	0.57	1.51
PLT-COVGERED	35.13	5.99	7.79	3.76	4.52	18.01	152.66	1.57	4.18	27.92	7.90	8.98	5.82	6.30	24.38	193.64	1.24	3.30
top  $k' = 1000$  inference. Unfortunately, they are also often slower than  $k' = 100$  inference. As we showed in the previous section,  $k' = 100$  is usually enough to get good predictive performance on macro measures using BCA and Greedy algorithms. However, here, the PLT-based methods obtain the exact solution for finding the prediction with the highest expected gain. While it is worth noting that tree structure and the type of node estimator have an impact on the performance of PLT-based algorithms and we conducted this experiment with just a single setup, we believe that this experiment shows that PLT-based inference, in some cases, can be an alternative to inference with sparse marginal estimates.