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Title: Universal Early Warning Signals of Phase Transitions in Climate Systems Abstract: The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and potentially irreversible shift is well established, but prediction of these events using standard forecast modeling te... |
Title: FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems Abstract: Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e... |
Title: On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models Abstract: Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art performance in generative modeling. Their main strength comes from their unique setup in which a model (the backward diffusion process) is t... |
Title: Generative Models with Information-Theoretic Protection Against Membership Inference Attacks Abstract: Deep generative models, such as Generative Adversarial Networks (GANs), synthesize diverse high-fidelity data samples by estimating the underlying distribution of high dimensional data. Despite their success, G... |
Title: To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier Abstract: Algorithmic fairness has emerged as an important consideration when using machine learning to make high-stakes societal decisions. Yet, improved fairness often comes at the expense of mod... |
Title: Semantically-enhanced Topic Recommendation System for Software Projects Abstract: Software-related platforms have enabled their users to collaboratively label software entities with topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance... |
Title: Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals Abstract: Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discr... |
Title: Easy Variational Inference for Categorical Models via an Independent Binary Approximation Abstract: We pursue tractable Bayesian analysis of generalized linear models (GLMs) for categorical data. Thus far, GLMs are difficult to scale to more than a few dozen categories due to non-conjugacy or strong posterior de... |
Title: Provably and Practically Efficient Neural Contextual Bandits Abstract: We consider the neural contextual bandit problem. In contrast to the existing work which primarily focuses on ReLU neural nets, we consider a general set of smooth activation functions. Under this more general setting, (i) we derive non-asymp... |
Title: MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption Abstract: Microarchitectural attacks have become more threatening the hardware security than before with the increasing diversity of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new thre... |
Title: Deep learning pipeline for image classification on mobile phones Abstract: This article proposes and documents a machine-learning framework and tutorial for classifying images using mobile phones. Compared to computers, the performance of deep learning model performance degrades when deployed on a mobile phone a... |
Title: Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry Abstract: Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be funct... |
Title: Principle of Relevant Information for Graph Sparsification Abstract: Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective information-theoretic formulation of graph sparsification, by taking inspi... |
Title: Decentralized Competing Bandits in Non-Stationary Matching Markets Abstract: Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial interest. To that end, in this paper, we introduce the fra... |
Title: Near-Optimal Collaborative Learning in Bandits Abstract: This paper introduces a general multi-agent bandit model in which each agent is facing a finite set of arms and may communicate with other agents through a central controller in order to identify, in pure exploration, or play, in regret minimization, its o... |
Title: Communication-efficient distributed eigenspace estimation with arbitrary node failures Abstract: We develop an eigenspace estimation algorithm for distributed environments with arbitrary node failures, where a subset of computing nodes can return structurally valid but otherwise arbitrarily chosen responses. Not... |
Title: ForestPrune: Compact Depth-Controlled Tree Ensembles Abstract: Tree ensembles are versatile supervised learning algorithms that achieve state-of-the-art performance. These models are extremely powerful but can grow to enormous sizes. As a result, tree ensembles are often post-processed to reduce memory footprint... |
Title: Fairness Transferability Subject to Bounded Distribution Shift Abstract: Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? In this paper, we study the transferability of statistical gr... |
Title: Pre-training via Denoising for Molecular Property Prediction Abstract: Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique that utilizes large datasets of 3... |
Title: AVIDA: Alternating method for Visualizing and Integrating Data Abstract: High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this chall... |
Title: End-to-end Optimization of Machine Learning Prediction Queries Abstract: Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (M... |
Title: Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria Abstract: Although many fairness criteria have been proposed to ensure that machine learning algorithms do not exhibit or amplify our existing social biases, these algorithms are trained on datasets that can themselv... |
Title: IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents Abstract: We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way. The environment features visual agent embodiment, interactive learning through c... |
Title: CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences Abstract: Backdoor attacks have been a critical threat to deep neural network (DNN). However, most existing countermeasures focus on source-agnostic backdoor attacks (SABAs) and fail to defeat source-specific backdoor a... |
Title: Hands-Up: Leveraging Synthetic Data for Hands-On-Wheel Detection Abstract: Over the past few years there has been major progress in the field of synthetic data generation using simulation based techniques. These methods use high-end graphics engines and physics-based ray-tracing rendering in order to represent t... |
Title: A Kernelised Stein Statistic for Assessing Implicit Generative Models Abstract: Synthetic data generation has become a key ingredient for training machine learning procedures, addressing tasks such as data augmentation, analysing privacy-sensitive data, or visualising representative samples. Assessing the qualit... |
Title: Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus Abstract: This paper considers offline multi-agent reinforcement learning. We propose the strategy-wise concentration principle which directly builds a confidence interval for the joint strategy, in contrast to the point-wise c... |
Title: PAGER: Progressive Attribute-Guided Extendable Robust Image Generation Abstract: This work presents a generative modeling approach based on successive subspace learning (SSL). Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distributio... |
Title: A Theoretical Framework for Inference Learning Abstract: Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiology. This difficulty has stimulated interest in more biologically plausible al... |
Title: Byzantine-Robust Online and Offline Distributed Reinforcement Learning Abstract: We consider a distributed reinforcement learning setting where multiple agents separately explore the environment and communicate their experiences through a central server. However, $\alpha$-fraction of agents are adversarial and c... |
Title: Discovering the Hidden Vocabulary of DALLE-2 Abstract: We discover that DALLE-2 seems to have a hidden vocabulary that can be used to generate images with absurd prompts. For example, it seems that \texttt{Apoploe vesrreaitais} means birds and \texttt{Contarra ccetnxniams luryca tanniounons} (sometimes) means bu... |
Title: Learning Sparse Nonlinear Dynamics via Mixed-Integer Optimization Abstract: Discovering governing equations of complex dynamical systems directly from data is a central problem in scientific machine learning. In recent years, the sparse identification of nonlinear dynamics (SINDy) framework, powered by heuristic... |
Title: On Gap-dependent Bounds for Offline Reinforcement Learning Abstract: This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior work showed when the density ratio between an optimal policy and the behavior policy is upper bounded (the optimal policy coverag... |
Title: DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training Abstract: Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user. However, existing works are often bui... |
Title: Transformer with Fourier Integral Attentions Abstract: Multi-head attention empowers the recent success of transformers, the state-of-the-art models that have achieved remarkable success in sequence modeling and beyond. These attention mechanisms compute the pairwise dot products between the queries and keys, wh... |
Title: Adaptive Online Learning of Quantum States Abstract: In the fundamental problem of shadow tomography, the goal is to efficiently learn an unknown $d$-dimensional quantum state using projective measurements. However, it is rarely the case that the underlying state remains stationary: changes may occur due to meas... |
Title: Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks Abstract: The research for characterizing GNN expressiveness attracts much attention as graph neural networks achieve a champion in the last five years. The number of linear regions has been considered a good measure for the exp... |
Title: DM$^2$: Distributed Multi-Agent Reinforcement Learning for Distribution Matching Abstract: Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of distributed multi-agent learning without res... |
Title: Continuous Prediction with Experts' Advice Abstract: Prediction with experts' advice is one of the most fundamental problems in online learning and captures many of its technical challenges. A recent line of work has looked at online learning through the lens of differential equations and continuous-time analysi... |
Title: Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble Abstract: Inverse reinforcement learning (IRL) recovers the underlying reward function from expert demonstrations. A generalizable reward function is even desired as it captures the fundamental motivation of the expert. However, classical I... |
Title: Privacy for Free: How does Dataset Condensation Help Privacy? Abstract: To prevent unintentional data leakage, research community has resorted to data generators that can produce differentially private data for model training. However, for the sake of the data privacy, existing solutions suffer from either expen... |
Title: Asymptotic Properties for Bayesian Neural Network in Besov Space Abstract: Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the... |
Title: Fair Comparison between Efficient Attentions Abstract: Transformers have been successfully used in various fields and are becoming the standard tools in computer vision. However, self-attention, a core component of transformers, has a quadratic complexity problem, which limits the use of transformers in various ... |
Title: Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images Abstract: Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses. However, currently, th... |
Title: Star algorithm for NN ensembling Abstract: Neural network ensembling is a common and robust way to increase model efficiency. In this paper, we propose a new neural network ensemble algorithm based on Audibert's empirical star algorithm. We provide optimal theoretical minimax bound on the excess squared risk. Ad... |
Title: CoNSoLe: Convex Neural Symbolic Learning Abstract: Learning the underlying equation from data is a fundamental problem in many disciplines. Recent advances rely on Neural Networks (NNs) but do not provide theoretical guarantees in obtaining the exact equations owing to the non-convexity of NNs. In this paper, we... |
Title: IDANI: Inference-time Domain Adaptation via Neuron-level Interventions Abstract: Large pre-trained models are usually fine-tuned on downstream task data, and tested on unseen data. When the train and test data come from different domains, the model is likely to struggle, as it is not adapted to the test domain. ... |
Title: Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization Abstract: In this paper, we study multi-block min-max bilevel optimization problems, where the upper level is non-convex strongly-concave minimax objective and the lower level is a strongly convex objective, and there ... |
Title: Self-supervised Learning for Label Sparsity in Computational Drug Repositioning Abstract: The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of v... |
Title: Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation Abstract: We study lifelong reinforcement learning (RL) in a regret minimization setting of linear contextual Markov decision process (MDP), where the agent needs to learn a multi-task policy while solving a streaming sequence ... |
Title: Task-Specific Expert Pruning for Sparse Mixture-of-Experts Abstract: The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of parameters, MoE is hard to be deployed on cloud or mobile environment. T... |
Title: On the Perils of Cascading Robust Classifiers Abstract: Ensembling certifiably robust neural networks has been shown to be a promising approach for improving the \emph{certified robust accuracy} of neural models. Black-box ensembles that assume only query-access to the constituent models (and their robustness ce... |
Title: Stochastic Gradient Methods with Preconditioned Updates Abstract: This work considers non-convex finite sum minimization. There are a number of algorithms for such problems, but existing methods often work poorly when the problem is badly scaled and/or ill-conditioned, and a primary goal of this work is to intro... |
Title: Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks Abstract: Neural Architecture Search (NAS) is increasingly popular to automatically explore the accuracy versus computational complexity trade-off of Deep Learning (DL) architectures. When targeting tiny edge devices, the... |
Title: Predecessor Features Abstract: Any reinforcement learning system must be able to identify which past events contributed to observed outcomes, a problem known as credit assignment. A common solution to this problem is to use an eligibility trace to assign credit to recency-weighted set of experienced events. Howe... |
Title: Federated Learning in Satellite Constellations Abstract: Distributed machine learning (DML) results from the synergy between machine learning and connectivity. Federated learning (FL) is a prominent instance of DML in which intermittently connected mobile clients contribute to the training of a common learning m... |
Title: Contextual Bandits with Knapsacks for a Conversion Model Abstract: We consider contextual bandits with knapsacks, with an underlying structure between rewards generated and cost vectors suffered. We do so motivated by sales with commercial discounts. At each round, given the stochastic i.i.d.\ context $\mathbf{x... |
Title: Model Generation with Provable Coverability for Offline Reinforcement Learning Abstract: Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could adapt to different dynamics enumerated at the ... |
Title: On Layer Normalizations and Residual Connections in Transformers Abstract: In the perspective of a layer normalization (LN) position, the architecture of Transformers can be categorized into two types: Post-LN and Pre-LN. Recent Transformers prefer to select Pre-LN because the training in Post-LN with deep Trans... |
Title: Control of Two-way Coupled Fluid Systems with Differentiable Solvers Abstract: We investigate the use of deep neural networks to control complex nonlinear dynamical systems, specifically the movement of a rigid body immersed in a fluid. We solve the Navier Stokes equations with two way coupling, which gives rise... |
Title: Support Vector Machines under Adversarial Label Contamination Abstract: Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood. Intelligent and adapt... |
Title: A Survey on Deep Learning for Skin Lesion Segmentation Abstract: Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence o... |
Title: DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks Abstract: Deep clustering has recently emerged as a promising technique for complex image clustering. Despite the significant progress, previous deep clustering works mostly tend to construct the final clustering by utilizing a... |
Title: Augmenting Message Passing by Retrieving Similar Graphs Abstract: Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing. In this paper, motivated by the success of retrieval-based models, we propose... |
Title: Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization Abstract: We study differentially private (DP) algorithms for smooth stochastic minimax optimization, with stochastic minimization as a byproduct. The holy grail of these settings is to guarantee the optimal trade-off bet... |
Title: Elucidating the Design Space of Diffusion-Based Generative Models Abstract: We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices. This lets... |
Title: Strongly Augmented Contrastive Clustering Abstract: Deep clustering has attracted increasing attention in recent years due to its capability of joint representation learning and clustering via deep neural networks. In its latest developments, the contrastive learning has emerged as an effective technique to subs... |
Title: Neural Improvement Heuristics for Preference Ranking Abstract: In recent years, Deep Learning based methods have been a revolution in the field of combinatorial optimization. They learn to approximate solutions and constitute an interesting choice when dealing with repetitive problems drawn from similar distribu... |
Title: A Generalized Supervised Contrastive Learning Framework Abstract: Based on recent remarkable achievements of contrastive learning in self-supervised representation learning, supervised contrastive learning (SupCon) has successfully extended the batch contrastive approaches to the supervised context and outperfor... |
Title: Transfer without Forgetting Abstract: This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue... |
Title: A comparative study between vision transformers and CNNs in digital pathology Abstract: Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficient amounts of data. In comparison to convolutional neural networks, vision transformers have a w... |
Title: Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis Abstract: Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by deep learning, has ma... |
Title: Towards Generalisable Audio Representations for Audio-Visual Navigation Abstract: In audio-visual navigation (AVN), an intelligent agent needs to navigate to a constantly sound-making object in complex 3D environments based on its audio and visual perceptions. While existing methods attempt to improve the naviga... |
Title: Optimization with access to auxiliary information Abstract: We investigate the fundamental optimization question of minimizing a target function $f(x)$ whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h(x)$ whose gradients are cheap or more avai... |
Title: Predicting Political Ideology from Digital Footprints Abstract: This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information ... |
Title: NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks Abstract: The advancements of deep neural networks (DNNs) have led to their deployment in diverse settings, including safety and security-critical applications. As a result, the characteristics of these models have become sensitive intell... |
Title: In the Eye of the Beholder: Robust Prediction with Causal User Modeling Abstract: Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recommendation systems, media services, and online market... |
Title: Open Environment Machine Learning Abstract: Conventional machine learning studies generally assume close world scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open world sce... |
Title: Semantic Probabilistic Layers for Neuro-Symbolic Learning Abstract: We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can... |
Title: Evaluating Gaussian Grasp Maps for Generative Grasping Models Abstract: Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the ce... |
Title: Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder Abstract: Interpreting computations in the visual cortex as learning and inference in a generative model of the environment has received wide support both in neuroscience and cognitive science. However, hierarchical comput... |
Title: Algorithmic Foundation of Deep X-Risk Optimization Abstract: X-risk is a term introduced to represent a family of compositional measures or objectives, in which each data point is compared with a set of data points explicitly or implicitly for defining a risk function. It includes many widely used measures or ob... |
Title: Ultrahyperbolic Knowledge Graph Embeddings Abstract: Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple di... |
Title: Towards Context-Aware Neural Performance-Score Synchronisation Abstract: Music can be represented in multiple forms, such as in the audio form as a recording of a performance, in the symbolic form as a computer readable score, or in the image form as a scan of the sheet music. Music synchronisation provides a wa... |
Title: A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations Abstract: Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainstream strate... |
Title: Automatic differentiation of nonsmooth iterative algorithms Abstract: Differentiation along algorithms, i.e., piggyback propagation of derivatives, is now routinely used to differentiate iterative solvers in differentiable programming. Asymptotics is well understood for many smooth problems but the nondifferenti... |
Title: An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits Abstract: We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors. This setting reveals a combinatorial NP-hard problem that... |
Title: Good Intentions: Adaptive Parameter Servers via Intent Signaling Abstract: Parameter servers (PSs) ease the implementation of distributed training for large machine learning (ML) tasks by providing primitives for shared parameter access. Especially for ML tasks that access parameters sparsely, PSs can achieve hi... |
Title: Contrastive Principal Component Learning: Modeling Similarity by Augmentation Overlap Abstract: Traditional self-supervised contrastive learning methods learn embeddings by pulling views of the same sample together and pushing views of different samples away. Since views of a sample are usually generated via dat... |
Title: Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer Abstract: Vision Transformers (ViTs) enabled the use of transformer architecture on vision tasks showing impressive performances when trained on big datasets. However, on relatively small datasets, ViTs are less accurate g... |
Title: DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems Abstract: Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, howev... |
Title: Rotate the ReLU to implicitly sparsify deep networks Abstract: In the era of Deep Neural Network based solutions for a variety of real-life tasks, having a compact and energy-efficient deployable model has become fairly important. Most of the existing deep architectures use Rectifier Linear Unit (ReLU) activatio... |
Title: Incentivizing Combinatorial Bandit Exploration Abstract: Consider a bandit algorithm that recommends actions to self-interested users in a recommendation system. The users are free to choose other actions and need to be incentivized to follow the algorithm's recommendations. While the users prefer to exploit, th... |
Title: Realistic Deep Learning May Not Fit Benignly Abstract: Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine the benign overfitting phenomena in real-world settings. We found that for tasks such as training a ResNet model on ImageNet da... |
Title: Proximally Sensitive Error for Anomaly Detection and Feature Learning Abstract: Mean squared error (MSE) is one of the most widely used metrics to expression differences between multi-dimensional entities, including images. However, MSE is not locally sensitive as it does not take into account the spatial arrang... |
Title: Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition Abstract: Stein Variational Gradient Descent (SVGD) is an important alternative to the Langevin-type algorithms for sampling from probability distributions of the form $\pi(x) \propto \exp(-V(x))$. In the existing theory of Lan... |
Title: Differentially Private Shapley Values for Data Evaluation Abstract: The Shapley value has been proposed as a solution to many applications in machine learning, including for equitable valuation of data. Shapley values are computationally expensive and involve the entire dataset. The query for a point's Shapley v... |
Title: Neural Network Verification with Proof Production Abstract: Deep neural networks (DNNs) are increasingly being employed in safety-critical systems, and there is an urgent need to guarantee their correctness. Consequently, the verification community has devised multiple techniques and tools for verifying DNNs. Wh... |
Title: The robust way to stack and bag: the local Lipschitz way Abstract: Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. We exploit this relationship to construct an ensemble of neural networks which not only improves the accuracy, b... |
Title: Feature Selection for Discovering Distributional Treatment Effect Modifiers Abstract: Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms. Existing methods seek such features by measuring how greatly the feature attributes affect the degree... |
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