Paper Citation Record · LEDGER

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression

As of 14 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.12653.

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

pith.paper-citation-record.v1
2411.12653 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:23:47.940397Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f894ed7e-4825-4776-b2ad-dffdcd57c7f9 · outbound

This paper cites predict, then o ptimize.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression predict, then o ptimize

Reference 1

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Observation d892b2c1-d972-421e-bae1-ec6575891f8b · outbound

This paper cites Risk bounds and calibration f or a smart predict-then-optimize method.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Risk bounds and calibration f or a smart predict-then-optimize method

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.208867Z

Source-reported events for the cited work

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Observation 8e0bf205-881b-4e53-9856-93583e4e0191 · outbound

This paper cites Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimizatio n.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimizatio n

Reference 3

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Observation 66832adb-2e73-4238-8728-028188871ccc · outbound

This paper cites Differentiation of blackbox combinatorial solvers.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Differentiation of blackbox combinatorial solvers

Reference 4

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Observation 9c16c7fc-b5aa-4376-99e0-a0ce5059735b · outbound

This paper cites Optnet: Differentiable o ptimization as a layer in neural networks.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Optnet: Differentiable o ptimization as a layer in neural networks

Reference 5

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Observation 734c1b5f-5e09-4cde-96e1-80d2c243b938 · outbound

This paper cites Task-based end-to-end model learning in stochastic optimization.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Task-based end-to-end model learning in stochastic optimization

Reference 6

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Source-reported events for the cited work

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Observation c062da03-0b8f-454f-8c00-2f264f9a6e52 · outbound

This paper cites From predictive t o prescriptive analytics.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression From predictive t o prescriptive analytics

Reference 7

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Source-reported events for the cited work

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Observation aefde6c7-98e6-4f82-a518-635d2670798b · outbound

This paper cites Generalization bounds in the predict-then-optimize framework.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Generalization bounds in the predict-then-optimize framework

Reference 8

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Observation 691e5df3-0e09-45c7-bdd4-fb55e51ae071 · outbound

This paper cites Fast rates for contextual linear optimization.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Fast rates for contextual linear optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.149992Z

Source-reported events for the cited work

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Observation 24c534a8-6d24-44ee-b176-3026bdbaf410 · outbound

This paper cites En- ergy forecasting: A review and outlook.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression En- ergy forecasting: A review and outlook

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.139888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21af5dc2-6e3a-4a79-8a95-05d65d669247 · outbound

This paper cites Discrepancy-base d theory and algorithms for forecast- ing non-stationary time series.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Discrepancy-base d theory and algorithms for forecast- ing non-stationary time series

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.131155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4b54cc90-b20a-4e17-9d06-d066ff40c723 · outbound

This paper cites Rademacher com plexity bounds for non-iid pro- cesses.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Rademacher com plexity bounds for non-iid pro- cesses

Reference 12

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Observation fb0b4084-fe3b-4483-9e27-b8dc94399df0 · outbound

This paper cites Theory and Algorithms for Forecasting Time Series.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Theory and Algorithms for Forecasting Time Series

Reference 13

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Observation bf4c79b5-5645-4763-911a-68654bc3892c · outbound

This paper cites Convergence and consistency of regularized boosting algorithms with stationary b-mixi ng observations.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Convergence and consistency of regularized boosting algorithms with stationary b-mixi ng observations

Reference 14

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Source-reported events for the cited work

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Observation a03ecfd1-a3c5-460d-98d0-a3f4fe0293d3 · outbound

This paper cites Rates of convergence for empirical processes of stationary mixing sequences.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Rates of convergence for empirical processes of stationary mixing sequences

Reference 15

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Observation 90edf4ec-11aa-467b-9ead-7b3d089b385d · outbound

This paper cites Stability boun ds for non-iid processes.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Stability boun ds for non-iid processes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.097928Z

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Observation f1b45696-92c8-4071-bcdd-712015f297bb · outbound

This paper cites Cope: Trafﬁc engineering in dynamic networks.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Cope: Trafﬁc engineering in dynamic networks

Reference 17

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Observation 7c3bc1c1-b570-435c-ba66-8cf630472bf7 · outbound

This paper cites Prioritized allocation of emergency respon- ders based on a continuous-time incident prediction model.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Prioritized allocation of emergency respon- ders based on a continuous-time incident prediction model

Reference 18

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Observation 87709fd6-ab5e-4d86-a1d1-d81edb892bf2 · outbound

This paper cites Risk guarantee s for end-to-end prediction and optimization processes.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Risk guarantee s for end-to-end prediction and optimization processes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.070737Z

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Observation 5973848b-729d-4e30-b6a2-49d0f239f0e2 · outbound

This paper cites Sur l’extension du théorème limite du calcul des probabilités aux sommes de quantités dépendantes.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Sur l’extension du théorème limite du calcul des probabilités aux sommes de quantités dépendantes

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.061879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d8d1099-cd12-4777-be45-7787508d3d5a · outbound

This paper cites Nonparametric risk bounds for time-series forecasting.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Nonparametric risk bounds for time-series forecasting

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d3f22e7-91bd-4a35-888e-90001e0e1a60 · outbound

This paper cites How to compare different loss function s and their risks.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression How to compare different loss function s and their risks

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f286e2e-4958-4fc6-9659-a75d86729428 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Adam: A Method for Stochastic Optimization

Reference 23

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This paper cites PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

Reference 24

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Source-reported events for the cited work

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Observation a66b928f-cd5f-4836-b2ef-2830d7be1d19 · outbound

This paper cites Mixing properties of arma proces ses.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Mixing properties of arma proces ses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.035145Z

Source-reported events for the cited work

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Observation 75354551-90a5-417a-a86d-60eacabfbcf0 · outbound

This paper cites Learning without mixing: Towards a sharp analysis of linear system id entiﬁcation.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Learning without mixing: Towards a sharp analysis of linear system id entiﬁcation

Reference 26

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Source-reported events for the cited work

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Observation e9c9e27f-2f3e-45d8-8d25-e1b70a5bc569 · outbound

This paper cites Sample complexity of kalman ﬁltering for unknown systems.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Sample complexity of kalman ﬁltering for unknown systems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.021769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 384986a4-2a6b-4d6d-b750-d013272464df · outbound

This paper cites System id entiﬁcation: A machine learning per- spective.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression System id entiﬁcation: A machine learning per- spective

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.013138Z

Source-reported events for the cited work

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Observation e2aeec4e-4061-4b32-990a-f39b43007595 · outbound

This paper cites Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss

Reference 29

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Source-reported events for the cited work

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Observation 6ef804bd-aefb-4dc8-bf37-55ee1f0092cb · outbound

This paper cites Least squares regression with markovian data: Fundamental limits and alg orithms.

Smart Predict-then-Optimize Method with Dependent Data: Risk Bounds and Calibration of Autoregression Least squares regression with markovian data: Fundamental limits and alg orithms

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:23:48.004311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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