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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
orid: string
arxiv_id: string
title: string
area: string
authors: list<item: string>
arxiv_url: string
openreview_url: string
text_source: string
full_text: bool
vs
beta: double
kappa: double
alpha: double
trH: double
gamma: double
floor_vs_d_fixed_trH: struct<rows: list<item: struct<d: int64, shape: string, trH_check: double, trH2: double, beta_check: double, alpha_check: double, kl_floor: double, eqF5_term: double, ratio: double, eqF5_holds: bool, sharp_const: double>>, slope: double, r2: double, ci95: double, h: double>
floor_spread_all_shapes: double
eqF5_holds_everywhere: bool
sharp_const_range: list<item: double>
floor_vs_d_isotropic: struct<rows: list<item: struct<d: int64, trH: double, kl_floor: double, eqF5_term: double, ratio: double>>, slope: double, r2: double, ci95: double>
floor_vs_trH_fixed_d: struct<rows: list<item: struct<trH: double, trH2: double, kl_floor: double>>, slope: double, r2: double, ci95: double, slope_vs_trH2: double, r2_vs_trH2: double, ci95_vs_trH2: double>
complexity_vs_d_fixed_trH: struct<rows: list<item: struct<d: int64, N: int64, h: double, kl_floor: double, N_thm43: double, ratio: double, W2: double>>, slope: double, r2: double, ci95: double, eps: double>
complexity_ratio_spread: double
N_growth_observed: double
N_growth_if_sqrt_d: double
complexity_vs_d_log_corrected: struct<slope: double, r2: double, ci95: double, log_factor: list<item: double>>
runtime_s: double
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 662, in write_rows_on_file
table = pa.concat_tables(self.current_rows)
File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
orid: string
arxiv_id: string
title: string
area: string
authors: list<item: string>
arxiv_url: string
openreview_url: string
text_source: string
full_text: bool
vs
beta: double
kappa: double
alpha: double
trH: double
gamma: double
floor_vs_d_fixed_trH: struct<rows: list<item: struct<d: int64, shape: string, trH_check: double, trH2: double, beta_check: double, alpha_check: double, kl_floor: double, eqF5_term: double, ratio: double, eqF5_holds: bool, sharp_const: double>>, slope: double, r2: double, ci95: double, h: double>
floor_spread_all_shapes: double
eqF5_holds_everywhere: bool
sharp_const_range: list<item: double>
floor_vs_d_isotropic: struct<rows: list<item: struct<d: int64, trH: double, kl_floor: double, eqF5_term: double, ratio: double>>, slope: double, r2: double, ci95: double>
floor_vs_trH_fixed_d: struct<rows: list<item: struct<trH: double, trH2: double, kl_floor: double>>, slope: double, r2: double, ci95: double, slope_vs_trH2: double, r2_vs_trH2: double, ci95_vs_trH2: double>
complexity_vs_d_fixed_trH: struct<rows: list<item: struct<d: int64, N: int64, h: double, kl_floor: double, N_thm43: double, ratio: double, W2: double>>, slope: double, r2: double, ci95: double, eps: double>
complexity_ratio_spread: double
N_growth_observed: double
N_growth_if_sqrt_d: double
complexity_vs_d_log_corrected: struct<slope: double, r2: double, ci95: double, log_factor: list<item: double>>
runtime_s: double
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string | status string |
|---|---|
Theorem 4.3 establishes the first dimension-free KL-divergence convergence bound for standard underdamped Langevin Monte Carlo (ULMC) under strong convexity, with sample complexity depending on tr(H) rather than the ambient dimension d, where H is a known upper bound on the Hessian (Theorem 4.3, Assumption 3.1). | unverified |
The strongly-convex ULMC sample complexity bound is Otilde(kappa^{3/2} beta^{-1/2} [tr(H)]^{1/2} / epsilon) to reach KL(mu (P')^N || pi) <= epsilon^2 (Theorem 4.3). | unverified |
Under randomized midpoint discretization, Theorem 5.2 improves the condition-number dependence relative to the bound of Liu et al. (2023), achieving complexity Otilde(kappa [beta^{-1} tr(H)]^{1/3} epsilon^{-2/3}) (Theorem 5.2). | unverified |
Theorems 4.4 and 5.4 extend the dimension-free guarantees to the general (non-strongly) convex setting (alpha = 0), the first such dimension-free result for underdamped Langevin dynamics in this regime (Theorem 4.4, Theorem 5.4). | unverified |
The dimension-free change-of-measure argument (Lemma 6.1) bounds E_mu[||grad V(x)||^2] and E_mu[p^T H p] by tr(H) + beta*KL(mu||pi), avoiding the explicit dimension dependence introduced by standard Gaussian moment bounds (Lemma 6.1, Section 6). | unverified |
The paper shows underdamped Langevin Monte Carlo attains better iteration complexity than composite overdamped Langevin Monte Carlo when tr(H) << d, e.g. in ridge-separable target distributions (Section 3.3, Theorem 3.5 framework). | unverified |
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