26 research outputs found
Scalable and interpretable product recommendations via overlapping co-clustering
We consider the problem of generating interpretable recommendations by
identifying overlapping co-clusters of clients and products, based only on
positive or implicit feedback. Our approach is applicable on very large
datasets because it exhibits almost linear complexity in the input examples and
the number of co-clusters. We show, both on real industrial data and on
publicly available datasets, that the recommendation accuracy of our algorithm
is competitive to that of state-of-art matrix factorization techniques. In
addition, our technique has the advantage of offering recommendations that are
textually and visually interpretable. Finally, we examine how to implement our
technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201
Primal-Dual Rates and Certificates
We propose an algorithm-independent framework to equip existing optimization
methods with primal-dual certificates. Such certificates and corresponding rate
of convergence guarantees are important for practitioners to diagnose progress,
in particular in machine learning applications. We obtain new primal-dual
convergence rates, e.g., for the Lasso as well as many L1, Elastic Net, group
Lasso and TV-regularized problems. The theory applies to any norm-regularized
generalized linear model. Our approach provides efficiently computable duality
gaps which are globally defined, without modifying the original problems in the
region of interest.Comment: appearing at ICML 2016 - Proceedings of the 33rd International
Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 4
Performative Prediction: Past and Future
Predictions in the social world generally influence the target of prediction,
a phenomenon known as performativity. Self-fulfilling and self-negating
predictions are examples of performativity. Of fundamental importance to
economics, finance, and the social sciences, the notion has been absent from
the development of machine learning. In machine learning applications,
performativity often surfaces as distribution shift. A predictive model
deployed on a digital platform, for example, influences consumption and thereby
changes the data-generating distribution. We survey the recently founded area
of performative prediction that provides a definition and conceptual framework
to study performativity in machine learning. A consequence of performative
prediction is a natural equilibrium notion that gives rise to new optimization
challenges. Another consequence is a distinction between learning and steering,
two mechanisms at play in performative prediction. The notion of steering is in
turn intimately related to questions of power in digital markets. We review the
notion of performative power that gives an answer to the question how much a
platform can steer participants through its predictions. We end on a discussion
of future directions, such as the role that performativity plays in contesting
algorithmic systems
Collaborative Learning via Prediction Consensus
We consider a collaborative learning setting where each agent's goal is to
improve their own model by leveraging the expertise of collaborators, in
addition to their own training data. To facilitate the exchange of expertise
among agents, we propose a distillation-based method leveraging unlabeled
auxiliary data, which is pseudo-labeled by the collective. Central to our
method is a trust weighting scheme which serves to adaptively weigh the
influence of each collaborator on the pseudo-labels until a consensus on how to
label the auxiliary data is reached. We demonstrate that our collaboration
scheme is able to significantly boost individual model's performance with
respect to the global distribution, compared to local training. At the same
time, the adaptive trust weights can effectively identify and mitigate the
negative impact of bad models on the collective. We find that our method is
particularly effective in the presence of heterogeneity among individual
agents, both in terms of training data as well as model architectures