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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
value: double
lbfgs_iters: int64
grad_inf: double
n_test: int64
eps: double
vs
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
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
return check_status(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:
value: double
lbfgs_iters: int64
grad_inf: double
n_test: int64
eps: double
vs
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
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 |
|---|---|
The paper defines a Safe KL divergence with a bounded-density constraint (Definition 2.1, Equations 3-4) whose induced approximation to LogSumExp satisfies F_ρ − O(ρ) ≤ F ≤ F_ρ (Proposition 2.4, Section 2). | unverified |
The rescaled SoftPlus-based conjugate of the Safe KL function is proven to be ρ-strongly convex, with its own conjugate being (1/ρ)-smooth (Lemma 2.7, Section 2.2). | unverified |
The approximation family is shown to bound and connect to the CVaR functional, recovering CVaR and LogSumExp as limiting cases (Proposition 2.6, Section 2.1). | unverified |
In continuous entropy-regularized optimal transport experiments with regularization ε=0.01, the proposed semi-dual formulation (Equations 14-15) avoids the numerical overflow issues of the baseline and reaches a converged objective in roughly 10^4 iterations (Figure 2, Section 3.1). | unverified |
In KL-regularized distributionally robust optimization on California Housing, the proposed method converges with a stepsize of η=10⁻⁴ across batch sizes of 10, 100, and 1000, whereas the baseline requires a much smaller η=10⁻⁶ at batch size 10 (Figure 3, Section 3.2). | unverified |
In unbalanced optimal-transport-based DRO on MNIST with label noise, the proposed approach (Equation 22) converges faster than the baseline (Equation 21) using η=10⁻⁴ versus the baseline's required η=10⁻⁵ (Figure 4, Section 3.3). | unverified |
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