pwc-archive/papers-with-abstracts huggingface dataset:
ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations
Authors: Asem Alaa, Erik Mayer, Mauricio Barahona
Date: 2022-07-05 00:00:00

Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution.
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Authors: Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter
Date: 2022-07-05 00:00:00
Tasks: AutoML, Bayesian Inference, General Classification, GPU, In-Context Learning, Meta-Learning, Missing Values, tabular-classification

We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN performs in-context learning (ICL), it learns to make predictions using sequences of labeled examples (x, f(x)) given in the input, without requiring further parameter updates. TabPFN is fully entailed in the weights of our network, which accepts training and test samples as a set-valued input and yields predictions for the entire test set in a single forward pass. TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. This prior incorporates ideas from causal reasoning: It entails a large space of structural causal models with a preference for simple structures. On the 18 datasets in the OpenML-CC18 suite that contain up to 1 000 training data points, up to 100 purely numerical features without missing values, and up to 10 classes, we show that our method clearly outperforms boosted trees and performs on par with complex state-of-the-art AutoML systems with up to 230$\times$ speedup. This increases to a 5 700$\times$ speedup when using a GPU. We also validate these results on an additional 67 small numerical datasets from OpenML. We provide all our code, the trained TabPFN, an interactive browser demo and a Colab notebook at https://github.com/automl/TabPFN.
PoF: Post-Training of Feature Extractor for Improving Generalization
Authors: Ikuro Sato, Ryota Yamada, Masayuki Tanaka, Nakamasa Inoue, Rei Kawakami
Date: 2022-07-05 00:00:00

It has been intensively investigated that the local shape, especially flatness, of the loss landscape near a minimum plays an important role for generalization of deep models. We developed a training algorithm called PoF: Post-Training of Feature Extractor that updates the feature extractor part of an already-trained deep model to search a flatter minimum. The characteristics are two-fold: 1) Feature extractor is trained under parameter perturbations in the higher-layer parameter space, based on observations that suggest flattening higher-layer parameter space, and 2) the perturbation range is determined in a data-driven manner aiming to reduce a part of test loss caused by the positive loss curvature. We provide a theoretical analysis that shows the proposed algorithm implicitly reduces the target Hessian components as well as the loss. Experimental results show that PoF improved model performance against baseline methods on both CIFAR-10 and CIFAR-100 datasets for only 10-epoch post-training, and on SVHN dataset for 50-epoch post-training. Source code is available at: \url{https://github.com/DensoITLab/PoF-v1
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Authors: Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe, Jun Morimoto, Takamitsu Matsubara
Date: 2022-07-05 00:00:00
Tasks: Model Predictive Control

Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. To alleviate this problem, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized images. We then propose Kalman Randomized-to-Canonical Model Predictive Control (KRC-MPC) as a zero-shot sim-to-real transferable visual MPC using KRC-model. The effectiveness of our method is evaluated through a valve rotation task by a robot hand in both simulation and the real world, and a block mating task in simulation. The experimental results show that KRC-MPC can be applied to various real domains and tasks in a zero-shot manner.
Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery
Authors: Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty, Arcot Sowmya
Date: 2022-07-05 00:00:00
Tasks: feature selection

Healthcare datasets present many challenges to both machine learning and statistics as their data are typically heterogeneous, censored, high-dimensional and have missing information. Feature selection is often used to identify the important features but can produce unstable results when applied to high-dimensional data, selecting a different set of features on each iteration. The stability of feature selection can be improved with the use of feature selection ensembles, which aggregate the results of multiple base feature selectors. A threshold must be applied to the final aggregated feature set to separate the relevant features from the redundant ones. A fixed threshold, which is typically applied, offers no guarantee that the final set of selected features contains only relevant features. This work develops several data-driven thresholds to automatically identify the relevant features in an ensemble feature selector and evaluates their predictive accuracy and stability. To demonstrate the applicability of these methods to clinical data, they are applied to data from two real-world Alzheimer's disease (AD) studies. AD is a progressive neurodegenerative disease with no known cure, that begins at least 2-3 decades before overt symptoms appear, presenting an opportunity for researchers to identify early biomarkers that might identify patients at risk of developing AD. Features identified by applying these methods to both datasets reflect current findings in the AD literature.
Multimodal Frame-Scoring Transformer for Video Summarization
Authors: Jeiyoon Park, Kiho Kwoun, Chanhee Lee, Heuiseok Lim
Date: 2022-07-05 00:00:00
Tasks: Video Summarization

As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors. To address these two problems, this paper proposes the Multimodal Frame-Scoring Transformer (MFST), a framework exploiting visual, text, and audio features and scoring a video with respect to frames. Our MFST framework first extracts each modality features (audio-visual-text) using pretrained encoders. Then, MFST trains the multimodal frame-scoring transformer that uses multimodal representation based on extracted features as inputs and predicts frame-level scores. Our extensive experiments with previous models and ablation studies on TVSum and SumMe datasets demonstrate the effectiveness and superiority of our proposed method by a large margin in both F1 score and Rank-based evaluation.
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding
Authors: Yiren Wang, Fatima Tuz-Zahra, Rong Zablocki, Chongzhi Di, Marta M. Jankowska, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Loki Natarajan
Date: 2022-07-05 00:00:00
Tasks: Time Series, Time Series Analysis

Cohort studies are increasingly using accelerometers for physical activity and sedentary behavior estimation. These devices tend to be less error-prone than self-report, can capture activity throughout the day, and are economical. However, previous methods for estimating sedentary behavior based on hip-worn data are often invalid or suboptimal under free-living situations and subject-to-subject variation. In this paper, we propose a local Markov switching model that takes this situation into account, and introduce a general procedure for posture classification and sedentary behavior analysis that fits the model naturally. Our method features changepoint detection methods in time series and also a two stage classification step that labels data into 3 classes(sitting, standing, stepping). Through a rigorous training-testing paradigm, we showed that our approach achieves > 80% accuracy. In addition, our method is robust and easy to interpret.
Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization
Authors: Richard Y. Zhang
Date: 2022-07-05 00:00:00

We consider minimizing a twice-differentiable, $L$-smooth, and $\mu$-strongly convex objective $\phi$ over an $n\times n$ positive semidefinite matrix $M\succeq0$, under the assumption that the minimizer $M^{\star}$ has low rank $r^{\star}\ll n$. Following the Burer--Monteiro approach, we instead minimize the nonconvex objective $f(X)=\phi(XX^{T})$ over a factor matrix $X$ of size $n\times r$. This substantially reduces the number of variables from $O(n^{2})$ to as few as $O(n)$ and also enforces positive semidefiniteness for free, but at the cost of giving up the convexity of the original problem. In this paper, we prove that if the search rank $r\ge r^{\star}$ is overparameterized by a \emph{constant factor} with respect to the true rank $r^{\star}$, namely as in $r>\frac{1}{4}(L/\mu-1)^{2}r^{\star}$, then despite nonconvexity, local optimization is guaranteed to globally converge from any initial point to the global optimum. This significantly improves upon a previous rank overparameterization threshold of $r\ge n$, which we show is sharp in the absence of smoothness and strong convexity, but would increase the number of variables back up to $O(n^{2})$. Conversely, without rank overparameterization, we prove that such a global guarantee is possible if and only if $\phi$ is almost perfectly conditioned, with a condition number of $L/\mu<3$. Therefore, we conclude that a small amount of overparameterization can lead to large improvements in theoretical guarantees for the nonconvex Burer--Monteiro factorization.
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Authors: Jun Wu, Jingrui He
Date: 2022-07-05 00:00:00
Tasks: Meta-Learning, Transfer Learning

Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a stationary task distribution, thereby leading to the sub-optimal performance for dynamic tasks drawn from a non-stationary task distribution in real scenarios. To bridge this gap, in this paper, we study a more realistic and challenging transfer learning setting with dynamic tasks, i.e., source and target tasks are continuously evolving over time. We theoretically show that the expected error on the dynamic target task can be tightly bounded in terms of source knowledge and consecutive distribution discrepancy across tasks. This result motivates us to propose a generic meta-learning framework L2E for modeling the knowledge transferability on dynamic tasks. It is centered around a task-guided meta-learning problem with a group of meta-pairs of tasks, based on which we are able to learn the prior model initialization for fast adaptation on the newest target task. L2E enjoys the following properties: (1) effective knowledge transferability across dynamic tasks; (2) fast adaptation to the new target task; (3) mitigation of catastrophic forgetting on historical target tasks; and (4) flexibility in incorporating any existing static transfer learning algorithms. Extensive experiments on various image data sets demonstrate the effectiveness of the proposed L2E framework.
On A Mallows-type Model For (Ranked) Choices
Authors: Yifan Feng, Yuxuan Tang
Date: 2022-07-05 00:00:00
Tasks: model, Vocal Bursts Type Prediction

We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates. (The set can be different for every participant.) We identify a distance-based ranking model for the population's preferences and their (ranked) choice behavior. The ranking model resembles the Mallows model but uses a new distance function called Reverse Major Index (RMJ). We find that despite the need to sum over all permutations, the RMJ-based ranking distribution aggregates into (ranked) choice probabilities with simple closed-form expression. We develop effective methods to estimate the model parameters and showcase their generalization power using real data, especially when there is a limited variety of display sets.
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
Authors: Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C. H. Hoi
Date: 2022-07-05 00:00:00
Tasks: Code Generation, Decoder, Deep Reinforcement Learning, mbpp, Program Synthesis, reinforcement-learning, Reinforcement Learning (RL)

Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs. Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks. To address the limitations, we propose "CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL). Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor. During inference, we introduce a new generation procedure with a critical sampling strategy that allows a model to automatically regenerate programs based on feedback from example unit tests and critic scores. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives, larger model sizes, and better pretraining data. Our method not only achieves new SOTA results on the challenging APPS benchmark, but also shows strong zero-shot transfer capability with new SOTA results on the simpler MBPP benchmark.
Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games
Authors: Lei Zhang, Mukesh Ghimire, Wenlong Zhang, Zhe Xu, Yi Ren
Date: 2022-07-05 00:00:00
Tasks: Autonomous Driving, Self-Supervised Learning

Finding Nash equilibrial policies for two-player differential games requires solving Hamilton-Jacobi-Isaacs (HJI) PDEs. Self-supervised learning has been used to approximate solutions of such PDEs while circumventing the curse of dimensionality. However, this method fails to learn discontinuous PDE solutions due to its sampling nature, leading to poor safety performance of the resulting controllers in robotics applications when player rewards are discontinuous. This paper investigates two potential solutions to this problem: a hybrid method that leverages both supervised Nash equilibria and the HJI PDE, and a value-hardening method where a sequence of HJIs are solved with a gradually hardening reward. We compare these solutions using the resulting generalization and safety performance in two vehicle interaction simulation studies with 5D and 9D state spaces, respectively. Results show that with informative supervision (e.g., collision and near-collision demonstrations) and the low cost of self-supervised learning, the hybrid method achieves better safety performance than the supervised, self-supervised, and value hardening approaches on equal computational budget. Value hardening fails to generalize in the higher-dimensional case without informative supervision. Lastly, we show that the neural activation function needs to be continuously differentiable for learning PDEs and its choice can be case dependent.
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy
Authors: Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas Diggavi
Date: 2022-07-05 00:00:00
Tasks: Federated Learning, Knowledge Distillation

A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various personalization methods proposed in literature, with seemingly very different forms and methods ranging from use of a single global model for local regularization and model interpolation, to use of multiple global models for personalized clustering, etc. In this work, we begin with a generative framework that could potentially unify several different algorithms as well as suggest new algorithms. We apply our generative framework to personalized estimation, and connect it to the classical empirical Bayes' methodology. We develop private personalized estimation under this framework. We then use our generative framework for learning, which unifies several known personalized FL algorithms and also suggests new ones; we propose and study a new algorithm AdaPeD based on a Knowledge Distillation, which numerically outperforms several known algorithms. We also develop privacy for personalized learning methods with guarantees for user-level privacy and composition. We numerically evaluate the performance as well as the privacy for both the estimation and learning problems, demonstrating the advantages of our proposed methods.
TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
Authors: Ziyue Liu, Xinling Yu, Zheng Zhang
Date: 2022-07-04 00:00:00
Tasks: Edge-computing

Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems. To achieve better expressiveness, increasingly larger network sizes are required in many problems. This has caused challenges when we need to train PINNs on edge devices with limited memory, computing and energy resources. To enable training PINNs on edge devices, this paper proposes an end-to-end compressed PINN based on Tensor-Train decomposition. In solving a Helmholtz equation, our proposed model significantly outperforms the original PINNs with few parameters and achieves satisfactory prediction with up to 15$\times$ overall parameter reduction.
Machine Learning in Access Control: A Taxonomy and Survey
Authors: Mohammad Nur Nobi, Maanak Gupta, Lopamudra Praharaj, Mahmoud Abdelsalam, Ram Krishnan, Ravi Sandhu
Date: 2022-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Survey

An increasing body of work has recognized the importance of exploiting machine learning (ML) advancements to address the need for efficient automation in extracting access control attributes, policy mining, policy verification, access decisions, etc. In this work, we survey and summarize various ML approaches to solve different access control problems. We propose a novel taxonomy of the ML model's application in the access control domain. We highlight current limitations and open challenges such as lack of public real-world datasets, administration of ML-based access control systems, understanding a black-box ML model's decision, etc., and enumerate future research directions.
A Causal Approach for Business Optimization: Application on an Online Marketplace
Authors: Naama Parush, Ohad Levinkron-Fisch, Hanan Shteingart, Amir Bar Sela, Amir Zilberman, Jake Klein
Date: 2022-07-04 00:00:00
Tasks: Causal Inference

A common sales strategy involves having account executives (AEs) actively reach out and contact potential customers. However, not all contact attempts have a positive effect: some attempts do not change customer decisions, while others might even interfere with the desired outcome. In this work we propose using causal inference to estimate the effect of contacting each potential customer and setting the contact policy accordingly. We demonstrate this approach on data from Worthy, an online jewelry marketplace. We examined the Worthy business process to identify relevant decisions and outcomes, and formalized assumptions on how they were made. Using causal tools, we selected a decision point where improving AE contact activity appeared to be promising. We then generated a personalized policy and recommended reaching out only to customers for whom it would be beneficial. Finally, we validated the results in an A\B test over a 3-month period, resulting in an increase in item delivery rate of the targeted population by 22% (p-value=0.026). This policy is now being used on an ongoing basis.