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# Learning Conference Reviewer Assignments - PowerPoint PPT Presentation

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

Guide :

Prof. Soumen Chakrabarti

Department of Computer Science and

Engineering,

Indian Institute of Technology, Bombay

• Given WWW2010’s assignments, learn Affinity_Param, Topic_Param and Irritation

• Citations as edge features

• Better estimation of Assignment Quality

• Conference Reviewer-Paper Assignment as a Many-Many-matching [1]

• Minimum Cost Network Flow (MCF)

• 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>

ILP-> Assumption -> MCF

• Integer Linear Programs are NP-Hard!

• Relax?

• More assumptions?

• How to determine A_ij?

• M * N ~ 10000

• Multimodal clues

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)

• 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?

• 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

Affinity

Cites

Bid

Topic Overlap

• 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

• Number of Bids Violated?

• Not a reliable measure.

• +ve Bids Violated

• –ve Bids Violated

• Assignments satisfying Topic Match

• Confidence?

• Very sparse

• Fewer than 5% observed

• Extrapolated Confidence?

• Reliable

• Bids as a precursor of Confidence [3]

• Confidence-Augmented Loss?

• 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)

SVM Struct. [7]

Loss Augmented Inference ~ Most Violated Constraint

Loss is decomposable -> Modified MCF

• Reviewer either gets few or L_i papers

• Introduce more parameters

• Infer using modified MCF

• Learning parameters?

• Tradeoff between MCF optimum and old assignment

• Document Modelling for Affinity Scores

• Objective Assignment Evaluation

• Transitive Citation Scores

• 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

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