Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T18:06:35.994946Z
Paper Citation Record · LEDGER
On the Efficiency of ERM in Feature Learning
As of 16 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2411.12029.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
10 of 10 outbound references displayed
- verified exact1
- verified fuzzy6
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f0c7ce2b-539c-4faf-bb59-e5c65288ba5a · outbound
This paper cites Using properties of the quantile function (e.g.
On the Efficiency of ERM in Feature Learning Using properties of the quantile function (e.g
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.179024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cb627515-15a7-46e7-9b4a-7ad7b6e5c960 · outbound
This paper cites 20 G Proof of Lemma 2 We prove a slightly more general result, from which Lemma 2 can be immediately deduced.
On the Efficiency of ERM in Feature Learning 20 G Proof of Lemma 2 We prove a slightly more general result, from which Lemma 2 can be immediately deduced
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.123971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1320a0a6-78ef-40d2-9132-7190f385e696 · outbound
This paper cites Then the statements ( 16) together with the continuous mapping theorem show that limn→∞ P(Bn(ε)) = 0.
On the Efficiency of ERM in Feature Learning Then the statements ( 16) together with the continuous mapping theorem show that limn→∞ P(Bn(ε)) = 0
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.191844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 62594c44-5fe2-4e40-8fcb-7813593ebdf7 · outbound
This paper cites Iterating the procedure we just described k times, we obtain that on an event An(t∗) ∩ (∩k j=1Bn,j ), where P(Bn,j ) ≥ 1 − δ/2k for all j ∈ [k] ˆtn ∈ F k n,δ/ 2k(T ) = Sn,δ,k.
On the Efficiency of ERM in Feature Learning Iterating the procedure we just described k times, we obtain that on an event An(t∗) ∩ (∩k j=1Bn,j ), where P(Bn,j ) ≥ 1 − δ/2k for all j ∈ [k] ˆtn ∈ F k n,δ/ 2k(T ) = Sn,δ,k
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.152725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7884d3ed-2957-4905-a0ca-df18d2c2cc5e · outbound
This paper cites With this knowl- edge, we now reuse the bound (.
On the Efficiency of ERM in Feature Learning With this knowl- edge, we now reuse the bound (
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.166596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 57c0e42c-4ec6-4ae6-bfca-f6d38052c774 · outbound
This paper cites an unresolved cited work.
On the Efficiency of ERM in Feature Learning Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T18:06:36.136746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d8be3dec-c41c-45a8-a741-4fd6b1f89bf1 · outbound
This paper cites Learning subgaussian classes : Upper and minimax bounds.
On the Efficiency of ERM in Feature Learning Learning subgaussian classes : Upper and minimax bounds
Reference 2012
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:06:35.969380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc37f180-9dfe-4c51-bdeb-25e80614afaa · outbound
This paper cites Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition.
On the Efficiency of ERM in Feature Learning Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:06:35.964865Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ff68e90-7ead-4e13-8d66-9ffe292a70af · outbound
This paper cites When Do Neural Networks Outperform Kernel Methods?.
On the Efficiency of ERM in Feature Learning When Do Neural Networks Outperform Kernel Methods?
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T18:06:36.205107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d8b89ee4-97eb-45d8-baf4-ed6f0bfc1f1a · outbound
This paper cites Persistence in High-Di mensional Linear Predictor Selection and the Virtue of Overparametrization.
On the Efficiency of ERM in Feature Learning Persistence in High-Di mensional Linear Predictor Selection and the Virtue of Overparametrization
Reference 2020
- Resolution
- verified exact
- raw_fallback, observed 2026-08-12T18:06:36.108255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.