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Found 23 papers, 10 papers with code
Classification Under Strategic Self-Selection
When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes.
One-Shot Strategic Classification Under Unknown Costs
The goal of strategic classification is to learn decision rules which are robust to strategic input manipulation.
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
Recent studies show that instruction tuning (IT) and reinforcement learning from human feedback (RLHF) improve the abilities of large language models (LMs) dramatically.
Delegated Classification
When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance.
Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces
The power of a platform is limited to controlling representations -- the subset of information about items presented by default to users.
Causal Strategic Classification: A Tale of Two Shifts
When users can benefit from certain predictive outcomes, they may be prone to act to achieve those outcome, e. g., by strategically modifying their features.
Performative Recommendation: Diversifying Content via Strategic Incentives
The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity.
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits.
Strategic Representation
In our main setting of interest, the system represents attributes of an item to the user, who then decides whether or not to consume.
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Accurately predicting the relevance of items to users is crucial to the success of many social platforms.
Strategic Classification with Graph Neural Networks
Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions.
Generalized Strategic Classification and the Case of Aligned Incentives
Our generalized model subsumes most current models but includes other novel settings; among these, we identify and target one intriguing sub-class of problems in which the interests of users and the system are aligned.
Strategic Classification Made Practical
Our approach directly minimizes the "strategic" empirical risk, achieved by differentiating through the strategic response of users.
Strategic Classification in the Dark
Strategic classification studies the interaction between a classification rule and the strategic agents it governs.
From Predictions to Decisions: Using Lookahead Regularization
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks.
A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone
Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media.
Predicting Choice with Set-Dependent Aggregation
Providing users with alternatives to choose from is an essential component in many online platforms, making the accurate prediction of choice vital to their success.
Learning Representations by Humans, for Humans
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy.
Label Propagation Networks
Graph networks have recently attracted considerable interest, and in particular in the context of semi-supervised learning.
Learning to Optimize Combinatorial Functions
Submodular functions have become a ubiquitous tool in machine learning.
Discriminative Learning of Prediction Intervals
Most current methods for constructing prediction intervals offer guarantees for a single new test point.
Semi-Supervised Learning with Competitive Infection Models
The goal in semi-supervised learning is to effectively combine labeled and unlabeled data.
Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
The conventional way to answer this counterfactual question is to estimate the effect of the new treatment in comparison to that of the conventional treatment by running a controlled, randomized experiment.
