Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking

• URL: http://arxiv.org/abs/2204.13765v1
• Date: Thu, 28 Apr 2022 20:38:34 GMT
• Title: Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
• Authors: Ali Vardasbi, Fatemeh Sarvi, Maarten de Rijke
• Abstract summary: We present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to PL, our distribution representation, called PPG, can be used for black-box optimization of fairness.
• Score: 53.94413894017409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. PL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, PL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to PL, our distribution representation, called PPG, can be used for black-box optimization of fairness. Different from PL, where pointwise logits are used as the distribution parameters, in PPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making PPG suitable for both deterministic and stochastic rankings. Our experiments show that PPG, while comparable to PL for larger session repetitions (i.e., stochastic ranking), improves over PL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of PPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item $d_1$ should always be ranked higher than item $d_2$." Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.

Related papers

• New Metrics for Assessing Projection Pursuit Indexes, and Guiding Optimisation Choices [0.0]
  The projection pursuit (PP) guided tour interactively optimises a criterion function known as the PP index, to explore high-dimensional data by revealing interesting projections. optimisation of some PP indexes can be non-trivial, if they are non-smooth functions, or the optimum has a small "squint angle", detectable only from close proximity. This study investigates the performance of a recently introduced swarm-based algorithm, Jellyfish Search Optimiser (JSO), for optimising PP indexes.
  arXiv  Detail & Related papers  (2024-07-18T16:39:01Z)
• Adaptive Online Bayesian Estimation of Frequency Distributions with Local Differential Privacy [0.4604003661048266]
  We propose a novel approach for the adaptive and online estimation of the frequency distribution of a finite number of categories under the local differential privacy (LDP) framework. The proposed algorithm performs Bayesian parameter estimation via posterior sampling and adapts the randomization mechanism for LDP based on the obtained posterior samples. We provide a theoretical analysis showing that (i) the posterior distribution targeted by the algorithm converges to the true parameter even for approximate posterior sampling, and (ii) the algorithm selects the optimal subset with high probability if posterior sampling is performed exactly.
  arXiv  Detail & Related papers  (2024-05-11T13:59:52Z)
• Poisson Process for Bayesian Optimization [126.51200593377739]
  We propose a ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO) Compared to the classic GP-BO method, our PoPBO has lower costs and better robustness to noise, which is verified by abundant experiments.
  arXiv  Detail & Related papers  (2024-02-05T02:54:50Z)
• Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment [54.179859639868646]
  We propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories.
  arXiv  Detail & Related papers  (2023-07-27T07:42:44Z)
• Provable benefits of score matching [30.317535687908755]
  We give the first example of a natural exponential family of distributions such that score matching loss is computationally efficient to optimize. We show that designing a zeroth-order or first-order oracle for optimizing the likelihood loss is NP-hard. Minimizing the score matching loss is both computationally and statistically efficient, with complexity in the ambient dimension.
  arXiv  Detail & Related papers  (2023-06-03T03:42:30Z)
• Bayesian Optimization with Conformal Prediction Sets [44.565812181545645]
  Conformal prediction is an uncertainty quantification method with coverage guarantees even for misspecified models. We propose conformal Bayesian optimization, which directs queries towards regions of search space where the model predictions have guaranteed validity. In many cases we find that query coverage can be significantly improved without harming sample-efficiency.
  arXiv  Detail & Related papers  (2022-10-22T17:01:05Z)
• Optimizing Partial Area Under the Top-k Curve: Theory and Practice [151.5072746015253]
  We develop a novel metric named partial Area Under the top-k Curve (AUTKC) AUTKC has a better discrimination ability, and its Bayes optimal score function could give a correct top-K ranking with respect to the conditional probability. We present an empirical surrogate risk minimization framework to optimize the proposed metric.
  arXiv  Detail & Related papers  (2022-09-03T11:09:13Z)
• You May Not Need Ratio Clipping in PPO [117.03368180633463]
  Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. Ratio clipping PPO is a popular variant that clips the probability ratios between the target policy and the policy used to collect samples. We show in this paper that such ratio clipping may not be a good option as it can fail to effectively bound the ratios. We show that ESPO can be easily scaled up to distributed training with many workers, delivering strong performance as well.
  arXiv  Detail & Related papers  (2022-01-31T20:26:56Z)
• Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence [77.06203118175335]
  Variational Inference (VI) is a popular alternative to exact sampling in Bayesian inference. Importance sampling (IS) is often used to fine-tune and de-bias the estimates of approximate Bayesian inference procedures. We propose a novel combination of optimization and sampling techniques for approximate Bayesian inference.
  arXiv  Detail & Related papers  (2021-06-30T11:00:24Z)
• Preferential Bayesian optimisation with Skew Gaussian Processes [0.225596179391365]
  We show that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP) We derive an efficient method to compute the exact SkewGP posterior and use it as surrogate model for PBO employing standard acquisition functions. We also show that our framework can be extended to deal with mixed preferential-categorical BO.
  arXiv  Detail & Related papers  (2020-08-15T08:23:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.