â Criteo â 0 â share . What is the most statistically efficient way to do off-policy optimization with batch data from bandit feedback? 2.1. Please note that this is a technical blog post aimed at educating about concepts and ⦠We demonstrate this technique in the domain ... quadratic programming optimization needed with LH allowing it to scale more easily, while achiev-ing â¦
DiCE supports Python 3+. In addition, the tutorial will demonstrate the production of these algorithms in industry use cases. changing the solver, the maximum number of iterations, etc. However, the precise formulation of counterfactual expla-nations as optimization problems given a classiï¬er, source instance and target class (such as the one we follow in sec-tion 3.3) and the variousworks exploringthis researchtopic are quite recent. To extract an optimal pair, we propose a mixed-integer linear optimization approach with our objective function. Causal and counterfactual methods for fairness in machine learning. The tutorial will cover the topics including conditional treatment effect estimators by meta-learners and tree-based algorithms, model validations and sensitivity analysis, optimization algorithms including policy leaner and cost optimization. The last requirement is that a counterfactual instance should have feature values that are likely . It would not make sense to generate a counterfactual explanation for the rent example where the size of an apartment is negative or the number of rooms is set to 200. Keep in mind that it is still an experimental feature at the moment. Pricing and revenue optimization. Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study Lihong Li1 Shunbao Chen1 Jim Kleban2 Microsoft Corp. Redmond, WA 98052 Facebook Inc. Seattle, WA 98101 Ankur Gupta1 ABSTRACT Optimizing an interactive system against a predefined online metric is particularly challenging, especially when the metric is ⦠We then present an algorithm for minimizing counterfactual regret in poker. To make the learning efï¬cient, we also developed several novel techniques including a robust sampling method and a mini-batch Monte Carlo Counterfactual Re-gret Minimization (MCCFR) method, which may beofindependentinterests. To do so, we'll use causal inference techniques to estimate the impact of what statisticians call a "treatment," in this case a policy decision.. DiCE supports Python 3+. The first term of the loss function represents the quadratic distance between the model prediction fâ(xâ) and the expected output yâ. Furthermore, the counterfactual network is estimated separately, and it is not easy to continue improving both counterfactual network and the tabular strategy proï¬le in an end-to-end optimization framework. In the context of counterfactual learn-to-rank, we refer to the algorithm generating the log data as the âbehav-ioral policyâ.
... Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals. In-processing methods for fairness in machine learning. achieving the right balance between average and 5 th percentile. The top-left panel of Figure 3 takes the same Lorenz curve as in Figure 2 (the solid black line just above the shaded regions) and splits it into the fraction of wealth held by White/non-Hispanic households (blue), Black/non-Hispanic â¦
counterfactual optimization technique allows us to guarantee a. minimum rate constraint, which adapts to the network size, hence. Unfortunately, existing CF explanation techniques are ill-suited for real-world deployment due to three major limita-tions. We conclude with future directions and ⦠Similar to the other application contexts, counterfactual esti-mation for the sponsored search optimization can be modeled as a causal inference problem [16].
To install the latest (dev) version of DiCE and its dependencies, clone this repo and run pip install from the top-most folder of the repo: pip install -e . optimization (also called policy learning ), i.e. CTR optimization in recommender systems, we argue that the user experience may be hurt unintentionally due to the clickbait issue. carry out end-to-end optimization. Challenges. In particular, in addition to highlighting the most important features used ⦠We introduce a new objective function that evaluates a pair of an action and an order based on feature interaction. FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles .
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System Tianxin Wei1, Fuli Feng2â, Jiawei Chen1, Ziwei Wu1, Jinfeng Yi3 and Xiangnan He1â 1University of Science and Technology of China, 2National University of Singapore, 3JD AI Research rouseau@mail.ustc.edu.cn,fulifeng93@gmail.com,cjwustc@ustc.edu.cn Weaknesses: 1. It is not clear how the exist-ing optimization procedures can be used. Therefore, numerous approaches have been proposed that map a salient region of an image to a diagnostic classification. The last part emphasizes that counterfactual learning is a rich research area, and discuss several important research topics, such as optimization for counterfactual learning, counterfactual meta learning, stable learning, fairness, unbiased learning to rank, offline policy evaluation. We present a decomposition of the POEM objective that enables efficient stochastic gradient optimization. Since we do not have access to the parametric forms of the underlying structural functions f V nor the exogenous distri-bution P(u), solving the optimization problem in Eq.
That is, some items with attractive exposure features (e.g., title and cover image) are easy to attract user clicks [20, 57], and thus are more likely to be recommended, but their actual content Counterfactual Risk Minimization e cient decomposition of the objective for stochastic optimization. However, the original CRF algorithm only works for discrete states and action spaces, and the resulting strategy Furthermore, they prove the generalization bounds for the proposed adversarial learning and analyze the minimax optimization properties. Published in arXiv preprint, 2019. Obtaining counterfactual explanation involves identifying the closest point to the input at hand that results in a different prediction. Our approach, based on generative adversarial networks, generates examples that are close to training set examples yet do not belong to any training category. Many methods proceed in this way but differ in their definition of the loss function and optimization method. As a sample-based explanation method, counterfactual learning (CL) is designed to evaluate how the modelâs decision could be altered through minimal changes to the input features artelt2019computation.
[pdf, code] We frame the problem of finding counterfactual explanations as an optimization task and extend previous work that could only be applied to differentiable models. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. For log data generated by contextual bandit algorithms, we consider offline estimators for the expected reward from a counterfactual policy.
Whereas the ML layers are focused on making individual estimates for each order, the optimization layer is focused on making system-wide decisions for the whole marketplace. the optimization problem that obtains counterfactual explanations for any score-based classiï¬cation model. Among post-hoc explanation techniques, counterfactual explanations are becoming one of the most popular methods to achieve this objective. Optimizing an interactive system against a predefined online metric is particularly challenging, especially when the metric is computed from user feedback such as clicks and payments. technique that we call counterfactual image generation. Robert Lewis Phillips. depends a lot on the initial quality of the counterfactual network. DiCE requires the following packages: 1. jsonschema 2. numpy 3. scikit-learn 4. pandas 5. h5py 6. tqdm 7.
These counterfactual techniques provide a wellfounded way to evaluate and optimize online metrics by exploiting logs of past user interactions. Contrasting model interpretation methods that involve feature attributions as discussed in a previous entry, counterfactuals are an interesting part of interpretation that allows post-hoc explanation to machine learning models. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same ⦠Therefore, numerous approaches have been proposed that map a salient region of an image to a diagnostic classification. Nearest Instance Counterfactual Explanations (NICE) NICE is an algorithm to generate Counterfactual Explanations for heterogeneous tabular data.
we address the case of counterfactual explanations that isolate the minimal differences for an individ-ual. This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem.
This can be done by passing the optional argument optimizer_args to the ceml.sklearn.models.generate_counterfactual() function. A new multi-agent actor-critic method called counterfactual multi- agent (COMA) policy gradients, which uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. Double Neural Counterfactual Regret Minimization Hui Li 1Kailiang Hu Yuan Qi Le Song1 2 Abstract Counterfactual Regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG). Change optimization parameters¶ Sometimes it might become necessary to change to default parameters of the optimization methods - e.g. In particular, we show that for functions of the form of the sum of weighted binary variables, which in-cludes frameworks such as weighted MaxSAT, a The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same ⦠We frame the problem of finding counterfactual explanations as an optimization task and extend previous work that could only be applied to differentiable models. Research work in counterfactual learn-to-rank For this purpose, we propose a new framework called Ordered Counterfactual Explanation (OrdCE). The AI would then either remove the trojan signal or ignore it. In this paper, we address the problem of counterfactual risk minimization for learning a stochastic policy with a continuous action space. possible values of the target counterfactual P(y x;:::;z w). Specifically, I am focusing on Model Optimization (such as model compression), where my goal is to build more efficient models or use the existing models more efficiently! sophisticated algorithms for joint optimization of user relevance, satisfaction and fairness. Counterfactual reasoning from logged data has become increasingly important for a large range of applications such as web advertising or healthcare. Whereas previous works have mostly focused on deriving statistical estimators with â¦
Text Counterfactuals via Latent Optimization and Shapley-Guided Search. technique that we call counterfactual image generation. Abstract: Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. This can be done by passing the optional argument optimizer_args to the :func:`ceml.sklearn.models.generate_counterfactual` function. 06/14/2019 â by Louis Faury, et al. At a high-level, the main concept for this method involves integrating an additional loss function term that accounts for these prototypes into a modified version of the basic counterfactual equation from the first section. This provides guidance by the prototype in the counterfactual search process. Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
To install the latest (dev) version of DiCE and its dependencies, clone this repo and run pip install from the top-most folder of the repo: If you face any problems, try installing dependencies manually. If you face any problems, try installing dependencies manually. pip install dice-ml. We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. By attracting users with rewards, marketing methods are effective to boost user activity in the ⦠(2) ap-pears theoretically challenging. We show how the counterfactual optimization technique allows us to guarantee a minimum rate constraint, which adapts to the network size, hence achieving the right balance between average and $5^{th}$ percentile user rates throughout a range of network configurations.
[opt⦠Empirically, ongames tractable to tabular approaches, neural strategies
Empirical evaluations are reported in Section 6 and a real-world application is described in Section 7. Counterfactual Estimation and Optimization of Click Metrics for Search Engines. ACM Reference Format: RishabhMehrotra1,JamesMcInerney1,HuguesBouchard1,MouniaLalmas1, Fernando Diaz2.
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