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Found 20 papers, 5 papers with code
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
The success of the collective is measured by the increase in test-time recommendations of the targeted song.
Performative Prediction: Past and Future
A consequence of performative prediction is a natural equilibrium notion that gives rise to new optimization challenges.
Questioning the Survey Responses of Large Language Models
As large language models increase in capability, researchers have started to conduct surveys of all kinds on these models in order to investigate the population represented by their responses.
Causal Inference out of Control: Estimating the Steerability of Consumption
Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption.
Algorithmic Collective Action in Machine Learning
We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms.
Anticipating Performativity by Predicting from Predictions
Predictions about people, such as their expected educational achievement or their credit risk, can be performative and shape the outcome that they aim to predict.
Performative Power
We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to cause change in a population of participants.
Regret Minimization with Performative Feedback
Our main contribution is an algorithm that achieves regret bounds scaling only with the complexity of the distribution shifts and not that of the reward function.
Alternative Microfoundations for Strategic Classification
When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory underlying classification.
Test-time Collective Prediction
An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points.
Revisiting Design Choices in Proximal Policy Optimization
We explain why standard design choices are problematic in these cases, and show that alternative choices of surrogate objectives and policy parameterizations can prevent the failure modes.
Randomized Block-Diagonal Preconditioning for Parallel Learning
We study preconditioned gradient-based optimization methods where the preconditioning matrix has block-diagonal form.
Stochastic Optimization for Performative Prediction
In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions.
Differentially Private Stochastic Coordinate Descent
In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private.
Performative Prediction
When predictions support decisions they may influence the outcome they aim to predict.
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability.
Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures.
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
To address this, we propose Chicle, a new elastic distributed training framework which exploits the nature of machine learning algorithms to implement elasticity and load balancing without micro-tasks.
On Linear Learning with Manycore Processors
A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator.
Sampling Acquisition Functions for Batch Bayesian Optimization
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process.
