739 research outputs found
Personalized Treatment-Response Trajectories: Errors-in-variables, Interpretability, and Causality
One fundamental problem in many applications is to estimate treatment-response trajectories given multidimensional treatment variables.
However, in reality, the estimation suffers severely from measurement error both in treatment timing and covariates, for example when the treatment data are self-reported by users.
We introduce a novel data-driven method to tackle this challenging problem, which models personalized treatment-response trajectories as a sum of a parametric response function, based on restored true treatment timing and covariates and sharing information across individuals under a hierarchical structure, and a counterfactual trend fitted by a sparse Gaussian Process.
In a real-life dataset where the impact of diet on continuous blood glucose is estimated, our model achieves a superior performance in estimation accuracy and prediction
RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces
This paper presents the novel Riemannian Fusion Network (RFNet), a deep
neural architecture for learning spatial and temporal information from
Electroencephalogram (EEG) for a number of different EEG-based Brain Computer
Interface (BCI) tasks and applications. The spatial information relies on
Spatial Covariance Matrices (SCM) of multi-channel EEG, whose space form a
Riemannian Manifold due to the Symmetric and Positive Definite structure. We
exploit a Riemannian approach to map spatial information onto feature vectors
in Euclidean space. The temporal information characterized by features based on
differential entropy and logarithm power spectrum density is extracted from
different windows through time. Our network then learns the temporal
information by employing a deep long short-term memory network with a soft
attention mechanism. The output of the attention mechanism is used as the
temporal feature vector. To effectively fuse spatial and temporal information,
we use an effective fusion strategy, which learns attention weights applied to
embedding-specific features for decision making. We evaluate our proposed
framework on four public datasets from three popular fields of BCI, notably
emotion recognition, vigilance estimation, and motor imagery classification,
containing various types of tasks such as binary classification, multi-class
classification, and regression. RFNet approaches the state-of-the-art on one
dataset (SEED) and outperforms other methods on the other three datasets
(SEED-VIG, BCI-IV 2A, and BCI-IV 2B), setting new state-of-the-art values and
showing the robustness of our framework in EEG representation learning
Sparse Convolution for Approximate Sparse Instance
Computing the convolution of two vectors of dimension is one
of the most important computational primitives in many fields. For the
non-negative convolution scenario, the classical solution is to leverage the
Fast Fourier Transform whose time complexity is . However, the
vectors and could be very sparse and we can exploit such property to
accelerate the computation to obtain the result. In this paper, we show that
when and holds,
we can approximately recover the all index in with point-wise error of in
time. We further show that we can iteratively correct the error and recover all
index in correctly in time
Finding Favourite Tuples on Data Streams with Provably Few Comparisons
One of the most fundamental tasks in data science is to assist a user with
unknown preferences in finding high-utility tuples within a large database. To
accurately elicit the unknown user preferences, a widely-adopted way is by
asking the user to compare pairs of tuples. In this paper, we study the problem
of identifying one or more high-utility tuples by adaptively receiving user
input on a minimum number of pairwise comparisons. We devise a single-pass
streaming algorithm, which processes each tuple in the stream at most once,
while ensuring that the memory size and the number of requested comparisons are
in the worst case logarithmic in , where is the number of all tuples. An
important variant of the problem, which can help to reduce human error in
comparisons, is to allow users to declare ties when confronted with pairs of
tuples of nearly equal utility. We show that the theoretical guarantees of our
method can be maintained for this important problem variant. In addition, we
show how to enhance existing pruning techniques in the literature by leveraging
powerful tools from mathematical programming. Finally, we systematically
evaluate all proposed algorithms over both synthetic and real-life datasets,
examine their scalability, and demonstrate their superior performance over
existing methods.Comment: To appear in KDD 202
Ranking with submodular functions on a budget
Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items. On the other hand, many real-world applications require a solution that is a ranking over a set of items. The problem of ranking in the context of submodular function maximization has been considered before, but to a much lesser extent than item-selection formulations. In this paper, we explore a novel formulation for ranking items with submodular valuations and budget constraints. We refer to this problem as max-submodular ranking (MSR). In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints. For the MSR problem with cardinality- and knapsack-type budget constraints we propose practical algorithms with approximation guarantees. In addition, we perform an empirical evaluation, which demonstrates the superior performance of the proposed algorithms against strong baselines.Peer reviewe
Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction
The indeterminate nature of human motion requires trajectory prediction
systems to use a probabilistic model to formulate the multi-modality phenomenon
and infer a finite set of future trajectories. However, the inference processes
of most existing methods rely on Monte Carlo random sampling, which is
insufficient to cover the realistic paths with finite samples, due to the long
tail effect of the predicted distribution. To promote the sampling process of
stochastic prediction, we propose a novel method, called BOsampler, to
adaptively mine potential paths with Bayesian optimization in an unsupervised
manner, as a sequential design strategy in which new prediction is dependent on
the previously drawn samples. Specifically, we model the trajectory sampling as
a Gaussian process and construct an acquisition function to measure the
potential sampling value. This acquisition function applies the original
distribution as prior and encourages exploring paths in the long-tail region.
This sampling method can be integrated with existing stochastic predictive
models without retraining. Experimental results on various baseline methods
demonstrate the effectiveness of our method
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