Paper Citation Record · LEDGER

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2210.12624.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2210.12624 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:18:56.981749Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T03:08:59.622920Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b3819773-ed19-4c9f-a01e-e73112e156bf · inbound

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs cites this paper.

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:08:29.086635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d36e84d6-c568-4ce0-8392-7db6826a2023 · inbound

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning cites this paper.

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:44:45.160704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b312f9f0-544a-41a5-864e-8d2443c97cfe · inbound

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

Reference 226

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:08:59.625813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a41a207-3824-4022-bc7d-98a32ce01593 · inbound

Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Level cites this paper.

Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Level Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-16T04:18:56.981749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.