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Title: Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning Abstract: The prevalence and success of Deep Neural Network (DNN) applications in recent years have motivated research on DNN compression, such as pruning and quantization. These techniques accelerate model infere... |
Title: Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers Abstract: Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD\&R) tools that air traffic controllers (ATC... |
Title: "Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction Abstract: Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of... |
Title: Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks Abstract: This paper described a method for reconstruction of detailed-resolution depth structure maps, usually obtained after the 3D seismic surveys, using the data from 2D seismic depth maps. The method uses two algorithms bas... |
Title: The Manifold Hypothesis for Gradient-Based Explanations Abstract: When do gradient-based explanation algorithms provide meaningful explanations? We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we... |
Title: Finite-Sample Guarantees for High-Dimensional DML Abstract: Debiased machine learning (DML) offers an attractive way to estimate treatment effects in observational settings, where identification of causal parameters requires a conditional independence or unconfoundedness assumption, since it allows to control fl... |
Title: Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning Abstract: Keeping risk under control is often more crucial than maximizing expected reward in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is varianc... |
Title: Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems Abstract: Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems. Existing methods use convolutional neural networks (CNNs) for square lattices due to the... |
Title: DiffWire: Inductive Graph Rewiring via the Lovász Bound Abstract: Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains. Most GNNs use a message passin... |
Title: Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases Abstract: Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many... |
Title: Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data Abstract: The promising potential of Deep Learning for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering the complexity of collecting training datasets measurements. Simulati... |
Title: Automatic Detection of Rice Disease in Images of Various Leaf Sizes Abstract: Fast, accurate and affordable rice disease detection method is required to assist rice farmers tackling equipment and expertise shortages problems. In this paper, we focused on the solution using computer vision technique to detect ric... |
Title: Cautious Learning of Multiattribute Preferences Abstract: This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as a subset of attributes). By "cautious", we mean that the model learned to ... |
Title: On Numerical Integration in Neural Ordinary Differential Equations Abstract: The combination of ordinary differential equations and neural networks, i.e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles. However, deciphering the numerical integration in Neural ODE... |
Title: Detection of magnetohydrodynamic waves by using machine learning Abstract: Nonlinear wave interactions, such as shock refraction at an inclined density interface, in magnetohydrodynamic (MHD) lead to a plethora of wave patterns with myriad wave types. Identification of different types of MHD waves is an importan... |
Title: A Survey : Neural Networks for AMR-to-Text Abstract: AMR-to-text is one of the key techniques in the NLP community that aims at generating sentences from the Abstract Meaning Representation (AMR) graphs. Since AMR was proposed in 2013, the study on AMR-to-Text has become increasingly prevalent as an essential br... |
Title: A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing Abstract: Knowledge of mixtures' phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental data on ac... |
Title: Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection Abstract: Evasion attacks against machine learning models often succeed via iterative probing of a fixed target model, whereby an attack that succeeds once will succeed repeatedly. One promising approach to counter th... |
Title: Online Contextual Decision-Making with a Smart Predict-then-Optimize Method Abstract: We study an online contextual decision-making problem with resource constraints. At each time period, the decision-maker first predicts a reward vector and resource consumption matrix based on a given context vector and then so... |
Title: Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack Abstract: The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational cost (e.g., 100 times more than that of the... |
Title: Can pruning improve certified robustness of neural networks? Abstract: With the rapid development of deep learning, the sizes of neural networks become larger and larger so that the training and inference often overwhelm the hardware resources. Given the fact that neural networks are often over-parameterized, on... |
Title: Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models Abstract: Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much less efficient than other DGMs suc... |
Title: VCT: A Video Compression Transformer Abstract: We show how transformers can be used to vastly simplify neural video compression. Previous methods have been relying on an increasing number of architectural biases and priors, including motion prediction and warping operations, resulting in complex models. Instead,... |
Title: Diffusion Transport Alignment Abstract: The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domai... |
Title: Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook Abstract: Knowledge management systems are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making. However, existing systems have limitations in categorizing and organizin... |
Title: FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank Abstract: FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data. We present a customized FOLD-R++ algori... |
Title: Differentiable Top-k Classification Learning Abstract: The top-k classification accuracy is one of the core metrics in machine learning. Here, k is conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives. In this work, we relax this assumption and optimize the model for m... |
Title: A Survey on Gradient Inversion: Attacks, Defenses and Future Directions Abstract: Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensive surveys covering recent advances and thorough an... |
Title: Global Convergence of Federated Learning for Mixed Regression Abstract: This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure. We contextualize this problem in mixed regression, where each client has limited local data generated from one of $k$ unknown r... |
Title: ALASCA: Rethinking Label Smoothing for Deep Learning Under Label Noise Abstract: As label noise, one of the most popular distribution shifts, severely degrades deep neural networks' generalization performance, robust training with noisy labels is becoming an important task in modern deep learning. In this paper,... |
Title: CARD: Classification and Regression Diffusion Models Abstract: Learning the distribution of a continuous or categorical response variable $\boldsymbol y$ given its covariates $\boldsymbol x$ is a fundamental problem in statistics and machine learning. Deep neural network-based supervised learning algorithms have... |
Title: Resource-Constrained Edge AI with Early Exit Prediction Abstract: By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. However, intermediate classifiers of the early exits introduce additiona... |
Title: Latency Control for Keyword Spotting Abstract: Conversational agents commonly utilize keyword spotting (KWS) to initiate voice interaction with the user. For user experience and privacy considerations, existing approaches to KWS largely focus on accuracy, which can often come at the expense of introduced latency... |
Title: On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation Abstract: Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters. We demonstrate that recasting the optimization prob... |
Title: CLNode: Curriculum Learning for Node Classification Abstract: Node classification is a fundamental graph-based task that aims to predict the classes of unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods. In current GNNs, training nodes (or training samples) are treated equal... |
Title: Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey Abstract: We apply the variational autoencoder (VAE) to the LAMOST-K2 low-resolution spectra to detect the magnetic activity of the stars in the K2 field. After the training on the spectra of the... |
Title: TeKo: Text-Rich Graph Neural Networks with External Knowledge Abstract: Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants follow a message-passing manner that obtains network representations by... |
Title: Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions Abstract: Stochastic gradient descent (SGD) is a pillar of modern machine learning, serving as the go-to optimization algorithm for a diverse array of problems. While the empirical success of SGD is often attribut... |
Title: Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking Abstract: Rankings have become the primary interface in two-sided online markets. Many have noted that the rankings not only affect the satisfaction of the users (e.g., customers, listeners, employers, travelers), but that the position in... |
Title: Test-Time Adaptation for Visual Document Understanding Abstract: Self-supervised pretraining has been able to produce transferable representations for various visual document understanding (VDU) tasks. However, the ability of such representations to adapt to new distribution shifts at test-time has not been stud... |
Title: A Multiple kernel testing procedure for non-proportional hazards in factorial designs Abstract: In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously.... |
Title: Location-based Twitter Filtering for the Creation of Low-Resource Language Datasets in Indonesian Local Languages Abstract: Twitter contains an abundance of linguistic data from the real world. We examine Twitter for user-generated content in low-resource languages such as local Indonesian. For NLP to work in In... |
Title: Query-Adaptive Predictive Inference with Partial Labels Abstract: The cost and scarcity of fully supervised labels in statistical machine learning encourage using partially labeled data for model validation as a cheaper and more accessible alternative. Effectively collecting and leveraging weakly supervised data... |
Title: Training Discrete Deep Generative Models via Gapped Straight-Through Estimator Abstract: While deep generative models have succeeded in image processing, natural language processing, and reinforcement learning, training that involves discrete random variables remains challenging due to the high variance of its g... |
Title: Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints Abstract: There is a disconnect between how researchers and practitioners handle privacy-utility tradeoffs. Researchers primarily operate from a privacy first perspective, setting strict privacy requirements and minimizing risk subject ... |
Title: Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning Abstract: Emotion classification of speech and assessment of the emotion strength are required in applications such as emotional text-to-speech and voice conversion. The emotion attribute ranking function based on ... |
Title: A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects Abstract: The recent development of deep learning combined with compressed sensing enables fast reconstruction of undersampled MR images and has achieved state-of-the-art performance for Carte... |
Title: Explainable expected goal models for performance analysis in football analytics Abstract: The expected goal provides a more representative measure of the team and player performance which also suit the low-scoring nature of football instead of score in modern football. The score of a match involves randomness an... |
Title: Attributions Beyond Neural Networks: The Linear Program Case Abstract: Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exist solvers for even high-dimensional LPs, understanding said h... |
Title: Using Machine Learning to Augment Dynamic Time Warping Based Signal Classification Abstract: Modern applications such as voice recognition rely on the ability to compare signals to pre-recorded ones to classify them. However, this comparison typically needs to ignore differences due to signal noise, temporal off... |
Title: Benefits of Additive Noise in Composing Classes with Bounded Capacity Abstract: We observe that given two (compatible) classes of functions $\mathcal{F}$ and $\mathcal{H}$ with small capacity as measured by their uniform covering numbers, the capacity of the composition class $\mathcal{H} \circ \mathcal{F}$ can ... |
Title: Improving Solar Flare Prediction by Time Series Outlier Detection Abstract: Solar flares not only pose risks to outer space technologies and astronauts' well being, but also cause disruptions on earth to our hight-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning ... |
Title: Can Foundation Models Talk Causality? Abstract: Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are wo... |
Title: Towards a Solution to Bongard Problems: A Causal Approach Abstract: To date, Bongard Problems (BP) remain one of the few fortresses of AI history yet to be raided by the powerful models of the current era. We present a systematic analysis using modern techniques from the intersection of causality and AI/ML in a ... |
Title: Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation Abstract: Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the literature, it was recently argued that c... |
Title: Machines Explaining Linear Programs Abstract: There has been a recent push in making machine learning models more interpretable so that their performance can be trusted. Although successful, these methods have mostly focused on the deep learning methods while the fundamental optimization methods in machine learn... |
Title: Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework Abstract: In this paper we describe our work towards building a generic framework for both multi-modal embedding and multi-label binary classification tasks, while participating in task 5 (Multimedia Automatic Mi... |
Title: Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising Abstract: Neural network policies trained using Deep Reinforcement Learning (DRL) are well-known to be susceptible to adversarial attacks. In this paper, we consider attacks manifesting as perturbations in the observation sp... |
Title: To Aggregate or Not? Learning with Separate Noisy Labels Abstract: The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). Typically one would first aggregate the separate noisy labels into one and apply standard training me... |
Title: Proximal Splitting Adversarial Attacks for Semantic Segmentation Abstract: Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately s... |
Title: Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design Abstract: Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimiz... |
Title: Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment during the learning proc... |
Title: DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method Abstract: Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. Howeve... |
Title: Category-Agnostic 6D Pose Estimation with Conditional Neural Processes Abstract: We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to "instance-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it wit... |
Title: GraphFM: Improving Large-Scale GNN Training via Feature Momentum Abstract: Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding the neighborhood explosion problem. Here, we propose a new... |
Title: Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features Abstract: Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcome of interest in the training data, but ar... |
Title: An Intelligent Assistant for Converting City Requirements to Formal Specification Abstract: As more and more monitoring systems have been deployed to smart cities, there comes a higher demand for converting new human-specified requirements to machine-understandable formal specifications automatically. However, t... |
Title: Flatten the Curve: Efficiently Training Low-Curvature Neural Networks Abstract: The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, existing methods to solve these issues, such as adversari... |
Title: MBGDT:Robust Mini-Batch Gradient Descent Abstract: In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vu... |
Title: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt Abstract: Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training, we introduce Reducible Holdout Loss S... |
Title: Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Abstract: Per-example gradient clipping is a key algorithmic step that enables practical differential private (DP) training for deep learning models. The choice of clipping norm $R$, however, is shown to be vital for achieving high... |
Title: Stability of image reconstruction algorithms Abstract: Robustness and stability of image reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ($\ell_2$ and $\ell_1$ ... |
Title: Lazy Queries Can Reduce Variance in Zeroth-order Optimization Abstract: A major challenge of applying zeroth-order (ZO) methods is the high query complexity, especially when queries are costly. We propose a novel gradient estimation technique for ZO methods based on adaptive lazy queries that we term as LAZO. Di... |
Title: Loss Functions for Classification using Structured Entropy Abstract: Cross-entropy loss is the standard metric used to train classification models in deep learning and gradient boosting. It is well-known that this loss function fails to account for similarities between the different values of the target. We prop... |
Title: Combining Counterfactuals With Shapley Values To Explain Image Models Abstract: With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task. Models trained on tabular data have witnessed significant progress in expla... |
Title: Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction Abstract: Normalization layers (e.g., Batch Normalization, Layer Normalization) were introduced to help with optimization difficulties in very deep nets, but they clearly also help generalization, even in not-so-deep nets. Moti... |
Title: Learning the Structure of Large Networked Systems Obeying Conservation Laws Abstract: Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Kirchoff laws in electric networks and opinion co... |
Title: Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience Abstract: Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which genera... |
Title: ReCo: Retrieve and Co-segment for Zero-shot Transfer Abstract: Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment. Segmentation methods that forgo supervision can side-step these cost... |
Title: Learning Behavior Representations Through Multi-Timescale Bootstrapping Abstract: Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales. While some success has been found in building representations of behavior under constrained or simpl... |
Title: Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abstract: Gradient compression is a popular technique for improving communication complexity of stochastic first-order methods in distributed training of machine learning models. However, the existing works consider only with-repl... |
Title: Exploring Representation of Horn Clauses using GNNs (technique report) Abstract: Learning program semantics from raw source code is challenging due to the complexity of real-world programming language syntax and due to the difficulty of reconstructing long-distance relational information implicitly represented i... |
Title: Two-terminal source coding with common sum reconstruction Abstract: We present the problem of two-terminal source coding with Common Sum Reconstruction (CSR). Consider two terminals, each with access to one of two correlated sources. Both terminals want to reconstruct the sum of the two sources under some averag... |
Title: Highly Efficient Structural Learning of Sparse Staged Trees Abstract: Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first... |
Title: Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Abstract: Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from ... |
Title: ABCinML: Anticipatory Bias Correction in Machine Learning Applications Abstract: The idealization of a static machine-learned model, trained once and deployed forever, is not practical. As input distributions change over time, the model will not only lose accuracy, any constraints to reduce bias against a protec... |
Title: AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data Abstract: Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In t... |
Title: Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models Abstract: Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based applications and systems are re... |
Title: FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents Abstract: Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosu... |
Title: Monitoring Urban Forests from Auto-Generated Segmentation Maps Abstract: We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for sema... |
Title: Bayesian neural networks for the probabilistic forecasting of wind direction and speed using ocean data Abstract: Neural networks are increasingly being used in a variety of settings to predict wind direction and speed, two of the most important factors for estimating the potential power output of a wind farm. H... |
Title: Scaling ResNets in the Large-depth Regime Abstract: Deep ResNets are recognized for achieving state-of-the-art results in complex machine learning tasks. However, the remarkable performance of these architectures relies on a training procedure that needs to be carefully crafted to avoid vanishing or exploding gr... |
Title: Object Scene Representation Transformer Abstract: A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition. Facilitating the learning of such a representation in neural networks holds promise for substantially improving labeled d... |
Title: Manifold Alignment-Based Multi-Fidelity Reduced-Order Modeling Applied to Structural Analysis Abstract: This work presents the application of a recently developed parametric, non-intrusive, and multi-fidelity reduced-order modeling method on high-dimensional displacement and stress fields arising from the struct... |
Title: Grad-GradaGrad? A Non-Monotone Adaptive Stochastic Gradient Method Abstract: The classical AdaGrad method adapts the learning rate by dividing by the square root of a sum of squared gradients. Because this sum on the denominator is increasing, the method can only decrease step sizes over time, and requires a lea... |
Title: Neural interval-censored Cox regression with feature selection Abstract: The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine. One of the most useful characteristics of the model for practitioners is the interpretabilit... |
Title: A Truthful Owner-Assisted Scoring Mechanism Abstract: Alice (owner) has knowledge of the underlying quality of her items measured in grades. Given the noisy grades provided by an independent party, can Bob (appraiser) obtain accurate estimates of the ground-truth grades of the items by asking Alice a question ab... |
Title: Temporal Multimodal Multivariate Learning Abstract: We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcom... |
Title: On Provably Robust Meta-Bayesian Optimization Abstract: Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whet... |
Title: How are policy gradient methods affected by the limits of control? Abstract: We study stochastic policy gradient methods from the perspective of control-theoretic limitations. Our main result is that ill-conditioned linear systems in the sense of Doyle inevitably lead to noisy gradient estimates. We also give an... |
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