Wasserstein Learning of Determinantal Point Processes
- URL: http://arxiv.org/abs/2011.09712v1
- Date: Thu, 19 Nov 2020 08:30:57 GMT
- Title: Wasserstein Learning of Determinantal Point Processes
- Authors: Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy, Ugo Tanielian,
Cl\'ement Calauz\`enes
- Abstract summary: We present a novel approach for learning DPPs that minimizes the Wasserstein distance between the model and data composed of observed subsets.
We show that our Wasserstein learning approach provides significantly improved predictive performance on a generative task compared to DPPs trained using MLE.
- Score: 14.790452282691252
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Determinantal point processes (DPPs) have received significant attention as
an elegant probabilistic model for discrete subset selection. Most prior work
on DPP learning focuses on maximum likelihood estimation (MLE). While efficient
and scalable, MLE approaches do not leverage any subset similarity information
and may fail to recover the true generative distribution of discrete data. In
this work, by deriving a differentiable relaxation of a DPP sampling algorithm,
we present a novel approach for learning DPPs that minimizes the Wasserstein
distance between the model and data composed of observed subsets. Through an
evaluation on a real-world dataset, we show that our Wasserstein learning
approach provides significantly improved predictive performance on a generative
task compared to DPPs trained using MLE.
Related papers
- Near-Optimal Learning and Planning in Separated Latent MDPs [70.88315649628251]
We study computational and statistical aspects of learning Latent Markov Decision Processes (LMDPs)
In this model, the learner interacts with an MDP drawn at the beginning of each epoch from an unknown mixture of MDPs.
arXiv Detail & Related papers (2024-06-12T06:41:47Z) - Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is.
This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
arXiv Detail & Related papers (2024-01-18T08:12:23Z) - Optimal Sample Selection Through Uncertainty Estimation and Its
Application in Deep Learning [22.410220040736235]
We present a theoretically optimal solution for addressing both coreset selection and active learning.
Our proposed method, COPS, is designed to minimize the expected loss of a model trained on subsampled data.
arXiv Detail & Related papers (2023-09-05T14:06:33Z) - Provably Efficient Representation Learning with Tractable Planning in
Low-Rank POMDP [81.00800920928621]
We study representation learning in partially observable Markov Decision Processes (POMDPs)
We first present an algorithm for decodable POMDPs that combines maximum likelihood estimation (MLE) and optimism in the face of uncertainty (OFU)
We then show how to adapt this algorithm to also work in the broader class of $gamma$-observable POMDPs.
arXiv Detail & Related papers (2023-06-21T16:04:03Z) - Nonparametric estimation of continuous DPPs with kernel methods [0.0]
Parametric and nonparametric inference methods have been proposed in the finite case, i.e. when the point patterns live in a finite ground set.
We show that a restricted version of this maximum likelihood (MLE) problem falls within the scope of a recent representer theorem for nonnegative functions in an RKHS.
We propose, analyze, and demonstrate a fixed point algorithm to solve this finite-dimensional problem.
arXiv Detail & Related papers (2021-06-27T11:57:14Z) - Towards Deterministic Diverse Subset Sampling [14.236193187116049]
In this paper, we discuss a greedy deterministic adaptation of k-DPP.
We demonstrate the usefulness of the model on an image search task.
arXiv Detail & Related papers (2021-05-28T16:05:58Z) - DEALIO: Data-Efficient Adversarial Learning for Imitation from
Observation [57.358212277226315]
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms.
This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk.
We propose a more data-efficient IfO algorithm
arXiv Detail & Related papers (2021-03-31T23:46:32Z) - Optimal transport framework for efficient prototype selection [21.620708125860066]
We develop an optimal transport (OT) based framework to select informative examples that best represent a given target dataset.
We show that our objective function enjoys a key property of submodularity and propose a parallelizable greedy method that is both computationally fast and possess deterministic approximation guarantees.
arXiv Detail & Related papers (2021-03-18T10:50:14Z) - Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method.
PCL implicitly encodes semantic structures of the data into the learned embedding space.
PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
arXiv Detail & Related papers (2020-05-11T09:53:36Z) - User-Level Privacy-Preserving Federated Learning: Analysis and
Performance Optimization [77.43075255745389]
Federated learning (FL) is capable of preserving private data from mobile terminals (MTs) while training the data into useful models.
From a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs.
We propose a user-level differential privacy (UDP) algorithm by adding artificial noise to the shared models before uploading them to servers.
arXiv Detail & Related papers (2020-02-29T10:13:39Z)
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.