检索结果-内蒙古大学图书馆

Improving conditional coverage via orthogonal quantile regression

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Feldman, Shai Bates, Stephen Romano, Yaniv Department of Computer Science Technion Israel Departments of Statistics EECS UC Berkeley Departments of Electrical Engineering and of Computer Science Technion Israel

We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typical approach to this task is to estimate the conditional quantiles with quantile regression - it is well-known that this leads to correct coverage in the large-sample limit, although it may not be accurate in finite samples. We find in experiments that traditional quantile regression can have poor conditional coverage. To remedy this, we modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event. For the true conditional quantiles, these two quantities are independent (orthogonal), so the modified loss function continues to be valid. Moreover, we empirically show that the modified loss function leads to improved conditional coverage, as evaluated by several metrics. We also introduce two new metrics that check conditional coverage by looking at the strength of the dependence between the interval size and the indicator of miscoverage. Copyright © 2021, The Authors. All rights reserved.

关键词： Sampling

The Sample Complexity of Online Contract Design

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhu, Banghua Bates, Stephen Yang, Zhuoran Wang, Yixin Jiao, Jiantao Jordan, Michael I. Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Statistics UC Berkeley United States Department of Statistics and Data Science Yale University United States Department of Statistics University of Michigan United States

We study the hidden-action principal-agent contract design problem in an online setting. In each round, the principal posts a contract that specifies the payment to the agent based on each outcome. The agent then makes a strategic choice of action that maximizes her own utility, but the action is not directly observable by the principal. The principal observes the outcome and receives utility from the agent’s choice of action. Based on past observations, the principal dynamically adjusts the contracts with the goal of maximizing her utility. We treat this problem as a continuum-armed bandit problem, where we think of each potential contract as an arm. We introduce an online learning algorithm and provide an upper bound on its Stackelberg regret. In particular, we show that when the contract space is [0, 1]m, the Stackelberg regret is upper bounded by Oe(√m · T1−1/(2m+1)), and lower bounded by Ω(T1−1/(m+2)), where Oe omits logarithmic factors. This result shows that exponential-in-m samples are both sufficient and necessary to learn a near-optimal contract, resolving an open problem in Ho et al. [2016] on the hardness of online contract design. Moreover, when contracts are restricted to some subset F ⊂ [0, 1]m, we define an intrinsic dimension of F that depends on the covering number of the spherical code in the space and bound the regret in terms of this intrinsic dimension. When F is the family of linear contracts, we show that the Stackelberg regret grows exactly as Θ(T2/3). Technically, the contract design problem is challenging because the utility function is discontinuous. Indeed, bounding the discretization error in this setting has been an open problem [Ho et al., 2016]. In this paper, we identify a limited set of directions in which the utility function is continuous, allowing us to design a new discretization method and bound its error. This approach enables the first upper bound with no restrictions on the contract and action space. The techniques we intro

关键词： Learning algorithms

Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Childs, Andrew M. Li, Tongyang Liu, Jin-Peng Wang, Chunhao Zhang, Ruizhe Joint Center for Quantum Information and Computer Science Department of Computer Science Institute for Advanced Computer Studies University of Maryland United States Center on Frontiers of Computing Studies School of Computer Science Peking University China Simons Institute Department of Mathematics UC Berkeley United States Department of Computer Science and Engineering Pennsylvania State University United States Department of Computer Science The University of Texas Austin United States

Given a convex function f: Rd → R, the problem of sampling from a distribution ∝ e−f (x) is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants (Equation presented). First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number κ and dimension d) of analogous classical algorithms that use gradient (first-order) queries, even though the quantum algorithms use only evaluation (zeroth-order) queries. For estimating normalizing constants, these algorithms also achieve quadratic speedup in the multiplicative error ∊. Second, we develop quantum Metropolis-adjusted Langevin algorithms with query complexity (Equation presented) and (Equation presented) for log-concave sampling and normalizing constant estimation, respectively, achieving polynomial speedups in κ, d, ∊ over the best known classical algorithms by exploiting quantum analogs of the Monte Carlo method and quantum walks. We also prove a 1/∊1−o(1) quantum lower bound for estimating normalizing constants, implying near-optimality of our quantum algorithms in ǫ. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

LIMO: Latent Inceptionism for Targeted Molecule Generation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Eckmann, Peter Sun, Kunyang Zhao, Bo Feng, Mudong Gilson, Michael K. Yu, Rose Department of Computer Science and Engineering UC San Diego La Jolla CA United States Department of Chemistry and Biochemistry UC San Diego La Jolla CA United States Skaggs School of Pharmacy and Pharmaceutical Sciences UC San Diego La Jolla CA United States

Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted KD (a measure of binding affinity) of 6 · 10−14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://***/Rose-STL-Lab/LIMO. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecules

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTIMATION 9

学校读者我要写书评

暂无评论

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTI...

9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Sarafan, Sadaf Vuong, Hoang Jilani, Daniel Malhotra, Samir Lau, Michael P.H. Vishwanath, Manoj Ghirmai, Tadesse Cao, Hung The Electrical Engineering Department The Samueli School of Engineering UC Irvine IrvineCA92697 United States Sensoriis Inc EdmondsWA98026 United States The School of STEM University of Washington BothellWA98011 United States The Biomedical Engineering Department UC Irvine CA92697 United States The Computer Science Department UC Irvine CA92697 United States

Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting;however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts. © 2022, CC BY-NC-ND.

关键词： Electrocardiograms

Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Al-Battal, Abdullah F. Duong, Soan T.M. Tang, Van Ha Tran, Quang Duc Truong, Steven Q.H. Phan, Chien Nguyen, Truong Q. An, Cheolhong Electrical and Computer Engineering Department UC San Diego La Jolla CA United States Electrical Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia Vinbrain JSC Hanoi Viet Nam Department of Computer Science Le Quy Don Technical University Hanoi Viet Nam Diagnostic Imaging Department University Medical Center Ho Chi Minh City Viet Nam

Multi-phase computed tomography (CT) scans use contrast agents to highlight different anatomical structures within the body to improve the probability of identifying and detecting anatomical structures of interest and abnormalities such as liver lesions. Yet, detecting these lesions remains a challenging task as these lesions vary significantly in their size, shape, texture, and contrast with respect to surrounding tissue. Therefore, radiologists need to have an extensive experience to be able to identify and detect these lesions. Segmentation-based neural networks can assist radiologists with this task. Current state-of-the-art lesion segmentation networks use the encoder-decoder design paradigm based on the UNet architecture where the multi-phase CT scan volume is fed to the network as a multi-channel input. Although this approach utilizes information from all the phases and outperform single-phase segmentation networks, we demonstrate that their performance is not optimal and can be further improved by incorporating the learning from models trained on each single-phase individually. Our approach comprises three stages. The first stage identifies the regions within the liver where there might be lesions at three different scales (4, 8, and 16 mm). The second stage includes the main segmentation model trained using all the phases as well as a segmentation model trained on each of the phases individually. The third stage uses the multi-phase CT volumes together with the predictions from each of the segmentation models to generate the final segmentation map. To demonstrate its ability to generalize beyond liver lesions, we also test our approach on brain lesions in multi-contrast magnetic resonance imaging (MRI). Overall, our approach improves relative liver lesion segmentation performance by 1.6% while reducing performance variability across subjects by 8% when compared to the current state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Xronos: Predictable Coordination for Safety-Critical Distributed Embedded Systems

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Bateni, Soroush Lohstroh, Marten Wong, Hou Seng Tabish, Rohan Kim, Hokeun Lin, Shaokai Menard, Christian Liu, Cong Lee, Edward A. EECS Department UC Berkeley United States Department of Computer Science UIUC United States Department of Electronic Engineering Hanyang University Korea Republic of Compiler Construction TU Dresden Germany Department of Computer Science UT Dallas United States

Asynchronous frameworks for distributed embedded systems, like ROS and MQTT, are increasingly used in safety-critical applications such as autonomous driving, where the cost of unintended behavior is high. The coordination mechanism between the components in these frameworks, however, gives rise to nondeterminism, where factors such as communication timing can lead to arbitrary ordering in the handling of messages. In this paper, we demonstrate the significance of this problem in an open-source full-stack autonomous driving software, *** 1.0, which relies on ROS 2. We give an alternative: Xronos, an open-source framework for distributed embedded systems that has a novel coordination strategy with predictable properties under clearly stated assumptions. If these assumptions are violated, Xronos provides for application-specific fault handlers to be invoked. We port *** to Xronos and show that it avoids the identified problems with manageable cost in end-to-end latency. Furthermore, we compare the maximum throughput of Xronos to ROS 2 and MQTT using microbenchmarks under different settings, including on three different hardware configurations, and find that it can match or exceed those frameworks in terms of throughput. © 2022, CC BY.

关键词： Autonomous vehicles

Fast Interpretable Greedy-Tree Sums

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tan, Yan Shuo Singh, Chandan Nasseri, Keyan Agarwal, Abhineet Duncan, James Ronen, Omer Epland, Matthew Kornblith, Aaron Yu, Bin Department of Statistics and Data Science National University of Singapore Singapore Department of Electrical Engineering and Computer Sciences UC Berkeley United States Microsoft Research United States Department of Statistics UC Berkeley United States Graduate Group in Biostatistics UC Berkeley United States Overjet Department of Emergency Medicine UC San Francisco United States Department of Pediatrics UC San Francisco United States

Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure. To overcome this bias, we propose Fast Interpretable Greedy-Tree Sums (FIGS), which generalizes the CART algorithm to simultaneously grow a flexible number of trees in summation. By combining logical rules with addition, FIGS is able to adapt to additive structure while remaining highly interpretable. Extensive experiments on real-world datasets show that FIGS achieves state-of-the-art prediction performance. To demonstrate the usefulness of FIGS in high-stakes domains, we adapt FIGS to learn clinical decision instruments (CDIs), which are tools for guiding clinical decision-making. Specifically, we introduce a variant of FIGS known as G-FIGS that accounts for the heterogeneity in medical data. G-FIGS derives CDIs that reflect domain knowledge and enjoy improved specificity (by up to 20% over CART) without sacrificing sensitivity or interpretability. To provide further insight into FIGS, we prove that FIGS learns components of additive models, a property we refer to as disentanglement. Further, we show (under oracle conditions) that unconstrained tree-sum models leverage disentanglement to generalize more efficiently than single decision tree models when fitted to additive regression functions. Finally, to avoid overfitting with an unconstrained number of splits, we develop Bagging-FIGS, an ensemble version of FIGS that borrows the variance reduction techniques of random forests. Bagging-FIGS enjoys competitive performance with random forests and XGBoost on real-world datasets. © 2022, CC BY.

关键词： Decision trees