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Learning Conference Reviewer Assignments

Learning Conference Reviewer Assignments. Adith Swaminathan Guide : Prof. Soumen Chakrabarti. Department of Computer Science and Engineering, Indian Institute of Technology, Bombay. Future Work (from BTP1). Given WWW2010’s assignments, learn Affinity_Param, Topic_Param and Irritation

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Learning Conference Reviewer Assignments

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  1. Learning Conference Reviewer Assignments Adith Swaminathan Guide : Prof. Soumen Chakrabarti Department of Computer Science and Engineering, Indian Institute of Technology, Bombay

  2. Future Work (from BTP1) • Given WWW2010’s assignments, learn Affinity_Param, Topic_Param and Irritation • Citations as edge features • Load-Constrained Partial Assignments • Better estimation of Assignment Quality

  3. Background • Conference Reviewer-Paper Assignment as a Many-Many-matching [1] • Minimum Cost Network Flow (MCF)

  4. Conference Reviewer Assignment • Set of Reviewers, R, max #papers = L_i • Set of Papers, P, min #reviews = K • Assumption : Only require #reviews, not quality • Suppose we have cost function A_ij(y) for <R_i, P_j>

  5. ILP-> Assumption -> MCF

  6. Two problems • Integer Linear Programs are NP-Hard! • Relax? • More assumptions? • How to determine A_ij? • M * N ~ 10000 • Multimodal clues

  7. ILP ->Assumption-> MCF • Enforce structure on A_ij • Better model multimodality • Fewer parameters to fix • “Learn” A_ij using Structured Learning Techniques • A_ij = wTΦ(R_i, P_j, y_ij)

  8. Ramifications of Structured Costs • Costs decompose over <R_i, P_j> pairs • Decomposable Preference Auction • Polynomial Algorithms for DPAs [2] • Restricted notion of optimality • Per-reviewer/Per-paper constraint could be combinatorial • Stability?

  9. ILP -> Assumption ->MCF

  10. Minimum Cost Network Flow • Directed graph G=(V,E), capacities u(E)>= 0, costs c(E) • Nodes have numbers b(V) : Sum(b(V)) = 0 • Task : Find a function f: E->R+ which satisfies the b-flow at minimum cost • Successive Shortest Path Algorithm

  11. Node features and Edge features Affinity Cites Bid Topic Overlap

  12. The Loss Function • L_ij = w_1 * exp(-Affinity_ij) + w_2 * [[1 – Topic_Overlap_ij]] + w_3 * Bid_Cost • Bid_Cost = Potential(R_i, P_j, y_ij) • Irritation (I) and Disappointment (D) needs to be set

  13. Assignment Quality Measures • Number of Bids Violated? • Not a reliable measure. • +ve Bids Violated • –ve Bids Violated • Assignments satisfying Topic Match • Confidence?

  14. Confidence == Quality? • Very sparse • Fewer than 5% observed • Extrapolated Confidence? • Reliable • Bids as a precursor of Confidence [3] • Confidence-Augmented Loss?

  15. Learning w’s • Transductive Ordinal Regression • Assume : Assignments are independent (Naïve) • Heuristic : Augment observed dataset • Extrapolate observed Confidence [4] • Learn w over extrapolated dataset • Support Vector Machine for Structured Outputs • Cast as soft-margin SVM formulation [5] • Upper-bound objective with a convex fn (Optimality?) • Minimize, using Cutting Plane (Approximate)

  16. Transductive Ordinal Regression [6]

  17. SVM Struct. [7] Loss Augmented Inference ~ Most Violated Constraint Loss is decomposable -> Modified MCF

  18. PARA : Paper Assignment to Reviewers Apparatus

  19. Results

  20. Bimodal Behaviour • Reviewer either gets few or L_i papers • Load Penalties [8] • Introduce more parameters • Infer using modified MCF • Learning parameters? • Load Rebalancing • Tradeoff between MCF optimum and old assignment

  21. Penalise Reviewer Loads

  22. Load Constrained Assignments

  23. Avenues for Future Work • Document Modelling for Affinity Scores • Objective Assignment Evaluation • Transitive Citation Scores • Load Penalty Parameter Estimation

  24. References • The Conference Paper Assignment Problem, J. Goldsmith, R.H. Sloan, 2007 • MultiAgent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Y. Shoham, K. Leyton-Brown, 2009 • Automating the Assignment of Submitted Manuscripts to Reviewers, S.T. Dumais, J. Nielson, 1992 • Semisupervised Regression with cotraining algorithms, Z. Zhou, M. Li, 2007

  25. References – contd. 5. Learning structured prediction models : A Large Margin Approach, B. Taskar, et al, 2005 6. Ologit : Ordinal Logistic Regression for Zelig, G. King, et al, 2007 7. SVM Learning for Interdependant and Structured Output Spaces, I. Tsochantaridis, et al, 2004 8. Word Alignment via Quadratic Assignment, S. Lacoste-Julien, et al, 2006

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