Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability.

With the recent evolution of mobile health technologies, health scientists are increasingly interested in delivering interventions via notifications on mobile device at the moments when they can most readily help the user prevent negative health outcomes and promote the adoption and maintenance of healthy behaviors. The type and timing of the mobile health interventions should ideally adapt to the real-time collected user's context, e.g., the time of the day, the location, current activity and stress level. This gives rise to the concept of a justin-time adaptive intervention (JITAI) [28]. Operationally, JITAI includes a sequence of decision rules (e.g., treatment policy) that takes the user's current context as input and specifies whether and what type of an intervention should be provided at the moment. In practice, behavioral theory along with expert opinion and analyses of existing data is often used to design the decision rules. However, these theories are often insufficiently mature to precisely specify which particular intervention and when it should be delivered in order to ensure the interventions have the intended effects and optimize the long-term efficacy of the interventions. As a result, there is much interest in how best to use data to inform the design of JITAIs [12, 39, 3, 35, 26, 41, 33, 10, 34, 42] This paper develops a Reinforcement Learning (RL) algorithm to continuously learn, e.g., online, and optimize the treatment policy in the JITAI as the user experiences the intervention.

Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly damages the performance of trained models when using such dataset without particular treatment. Various of methods have been proposed for learning with noisy labels. However, they only handle limited kinds of noise patterns, require auxiliary information (e.g,, the noise transition matrix), or lack theoretical justification. In this paper, we propose a novel information-theoretic loss function, $\mathcal{L}_{\rm DMI}$, for training deep neural networks robust to label noise. The core of $\mathcal{L}_{\rm DMI}$ is a generalized version of mutual information, termed Determinant based Mutual Information (DMI), which is not only information-monotone but also relatively invariant. \emph{To the best of our knowledge, $\mathcal{L}_{\rm DMI}$ is the first loss function that is provably not sensitive to noise patterns and noise amounts, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}. In addition to theoretical justification, we also empirically show that using $\mathcal{L}_{\rm DMI}$ outperforms all other counterparts in the classification task on Fashion-MNIST, CIFAR-10, Dogs vs. Cats datasets with a variety of synthesized noise patterns and noise amounts as well as a real-world dataset Clothing1M. Codes are available at https://github.com/Newbeeer/L_DMI

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov's accelerated gradient in both its full and limited memory forms for solving large scale non-convex optimization problems in neural networks. The performance of the proposed algorithm is evaluated in Tensorflow on benchmark classification and regression problems. The results show improved performance compared to the classical second order oBFGS and oLBFGS methods and popular first order stochastic methods such as SGD and Adam. The performance with different momentum rates and batch sizes have also been illustrated. Keywords: Neural networks · stochastic method · online training · Nesterov's accelerated gradient · quasi-Newton method · limited memory · Tensorflow 1 Introduction Neural networks have shown to be effective in innumerous real-world applications.

A common problem in training neural networks is the vanishing and/or exploding gradient problem which is more prominently seen in training of Recurrent Neural Networks (RNNs). Thus several algorithms have been proposed for training RNNs. This paper proposes a novel adaptive stochastic Nesterov accelerated quasiNewton (aSNAQ) method for training RNNs. The proposed method aSNAQ is an accelerated method that uses the Nesterov's gradient term along with second order curvature information. The performance of the proposed method is evaluated in Tensorflow on benchmark sequence modeling problems. The results show an improved performance while maintaining a low per-iteration cost and thus can be effectively used to train RNNs.

Implicit feedback plays a critical role to construct recommender systems because this type of feedback is prevalent in the real-world. However, effectively utilizing implicit feedback is challenging because of positive-unlabeled or missing-not-at-random problems. To tackle these challenges, in this paper, we first show that existing approaches are biased toward the true metric. Subsequently, we provide a theoretically principled approach to handle the problems inspired by estimation methods in causal inference. In particular, we propose an unbiased estimator for the true metric of interest solving the above problems simultaneously. Experiments on two standard real-world datasets demonstrate the superiority of the proposed approach against state-of-the-art recommendation algorithms.

Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in contemporary settings where item responses are being continuously collected, such as Massive Open Online Courses (MOOCs), interest will naturally be on the dynamics of ability, thus complicating usage of traditional IRT models. We propose DynAEsti, an augmentation of the traditional IRT Expectation Maximization algorithm that allows ability to be a continuously varying curve over time. In the process, we develop CurvFiFE, a novel non-parametric continuous-time technique that handles the curve-fitting/regression problem extended to address more general probabilistic emissions (as opposed to simply noisy data points). Furthermore, to accomplish this, we develop a novel technique called grafting, which can successfully approximate distributions represented by graphical models when other popular techniques like Loopy Belief Propogation (LBP) and Variational Inference (VI) fail. The performance of DynAEsti is evaluated through simulation, where we achieve results comparable to the optimal of what is observed in the static ability scenario. Finally, DynAEsti is applied to a longitudinal performance dataset (80-years of competitive golf at the 18-hole Masters Tournament) to demonstrate its ability to recover key properties of human performance and the heterogeneous characteristics of the different holes. Python code for CurvFiFE and DynAEsti is publicly available at github.com/chausies/DynAEstiAndCurvFiFE. This is the full version of our ICDM 2019 paper.

We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and gives structural insights that we expect will be useful elsewhere. We show: 1) that differential privacy ensures that the expectation of any query on the posterior distribution on datasets induced by the transcript of the interaction is close to its true value on the data distribution, and 2) sample accuracy on its own ensures that any query answer produced by the mechanism is close to its posterior expectation with high probability. This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers. The transfer theorem then follows by summing these two bounds, and in particular, avoids the "monitor argument" used to derive high probability bounds in prior work. An upshot of our new proof technique is that the concrete bounds we obtain are substantially better than the best previously known bounds, even though the improvements are in the constants, rather than the asymptotics (which are known to be tight). As we show, our new bounds outperform the naive "sample-splitting" baseline at dramatically smaller dataset sizes compared to the previous state of the art, bringing techniques from this literature closer to practicality.

Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior. The model often assumes the variational posterior to be factorized even when the true posterior is not. The learned variational posterior under this assumption does not capture the dependency relationships over latent variables. We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models. We propose Gaussian Copula Variational Autoencoder (VAE) to avert this problem. Copula is widely used to model correlation and dependencies of high-dimensional random variables, and therefore it is helpful to maintain the dependency relationships that are lost in VAE. The empirical results show that by modeling the correlation of latent variables explicitly using a neural parametric copula, we can avert this training difficulty while getting competitive results among all other VAE approaches.

Lecture notes on optimization for machine learning, derived from a course at Princeton University and tutorials given in MLSS, Buenos Aires, as well as Simons Foundation, Berkeley.