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Title: Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost Abstract: Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study ... |
Title: Robust Phi-Divergence MDPs Abstract: In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic pro... |
Title: ALMA: Hierarchical Learning for Composite Multi-Agent Tasks Abstract: Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solving tasks where agents interact with all other agents and entitie... |
Title: Targeted Adaptive Design Abstract: Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must... |
Title: StarGraph: A Coarse-to-Fine Representation Method for Large-Scale Knowledge Graph Abstract: Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in neighbor entities. We propose a method named StarGraph, wh... |
Title: MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers Abstract: Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristic... |
Title: KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal Abstract: In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (MDVI) by Geist et al. (2019) and Vieillard et al. (2020... |
Title: Diffusion-LM Improves Controllable Text Generation Abstract: Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been lit... |
Title: Will Bilevel Optimizers Benefit from Loops Abstract: Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve solving one or two sub-problems, and consequently, whether we solve these proble... |
Title: Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction Abstract: Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems. Recent works demonstrate that adversarial attacks on trajectory prediction, where small crafted pertur... |
Title: Competitive Gradient Optimization Abstract: We study the problem of convergence to a stationary point in zero-sum games. We propose competitive gradient optimization (CGO ), a gradient-based method that incorporates the interactions between the two players in zero-sum games for optimization updates. We provide c... |
Title: FedControl: When Control Theory Meets Federated Learning Abstract: To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. ... |
Title: Provably Sample-Efficient RL with Side Information about Latent Dynamics Abstract: We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is ... |
Title: Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference Abstract: We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from... |
Title: Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration Abstract: The deep learning boom motivates researchers and practitioners of computational fluid dynamics eager to integrate the two areas.The PINN (physics-informed neural network) method is one such attempt. Whi... |
Title: On the Symmetries of Deep Learning Models and their Internal Representations Abstract: Symmetry has been a fundamental tool in the exploration of a broad range of complex systems. In machine learning, symmetry has been explored in both models and data. In this paper we seek to connect the symmetries arising from... |
Title: Personalized PageRank Graph Attention Networks Abstract: There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data. However, GNNs typically only use the information of a ... |
Title: NeuPSL: Neural Probabilistic Soft Logic Abstract: We present Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To explicitly model the boundary between neural and symbolic repres... |
Title: Towards Communication-Learning Trade-off for Federated Learning at the Network Edge Abstract: In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learni... |
Title: Image Keypoint Matching using Graph Neural Networks Abstract: Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When images are represented as graphs, image matching boils down to the prob... |
Title: So3krates -- Self-attention for higher-order geometric interactions on arbitrary length-scales Abstract: The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some q... |
Title: Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems Abstract: This paper studies the uniform convergence and generalization bounds for nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax optimization. We first establish the uniform convergence between the empirical minimax p... |
Title: Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Abstract: Sparse modeling for signal processing and machine learning has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning comprises two major pa... |
Title: Provably Auditing Ordinary Least Squares in Low Dimensions Abstract: Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i.e. against infinitesimal changes in the data), or are only interpretab... |
Title: Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models Abstract: We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face a dramatic drop under the so-called ``near-distribution" setting, where the differences between nor... |
Title: MC-GEN:Multi-level Clustering for Private Synthetic Data Generation Abstract: Nowadays, machine learning is one of the most common technology to turn raw data into useful information in scientific and industrial processes. The performance of the machine learning model often depends on the size of dataset. Compan... |
Title: Deep Learning with Label Noise: A Hierarchical Approach Abstract: Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. ... |
Title: A Quadrature Perspective on Frequency Bias in Neural Network Training with Nonuniform Data Abstract: Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the low-frequency misfit before red... |
Title: Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model Abstract: Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled theory-guided neural network (Tg... |
Title: Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks Abstract: Many complex systems in the real world can be characterized by attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters ... |
Title: Ensemble2: Anomaly Detection via EVT-Ensemble Framework for Seasonal KPIs in Communication Network Abstract: KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the ... |
Title: TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph Abstract: Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, whi... |
Title: Federated Neural Bandit Abstract: Recent works on neural contextual bandit have achieved compelling performances thanks to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandit involve multiple agents who collaborate witho... |
Title: Approximate Conditional Coverage via Neural Model Approximations Abstract: Constructing reliable prediction sets is an obstacle for applications of neural models: Distribution-free conditional coverage is theoretically impossible, and the exchangeability assumption underpinning the coverage guarantees of standar... |
Title: Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition Abstract: With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs... |
Title: A Confidence Machine for Sparse High-Order Interaction Model Abstract: In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the confidence of prediction results with fewer theoretical assumpt... |
Title: Learning from Self-Sampled Correct and Partially-Correct Programs Abstract: Program synthesis aims to generate executable programs that are consistent with the user specification. While there are often multiple programs that satisfy the same user specification, existing neural program synthesis models are often ... |
Title: Automatic Expert Selection for Multi-Scenario and Multi-Task Search Abstract: Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize m... |
Title: Multi-agent Databases via Independent Learning Abstract: Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. essential database components, such as the optimizer, sche... |
Title: Differentially Private Covariance Revisited Abstract: In this paper, we present three new error bounds, in terms of the Frobenius norm, for covariance estimation under differential privacy: (1) a worst-case bound of $\tilde{O}(d^{1/4}/\sqrt{n})$, which improves the standard Gaussian mechanism $\tilde{O}(d/n)$ fo... |
Title: Feature subset selection for kernel SVM classification via mixed-integer optimization Abstract: We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this ... |
Title: Survival Analysis on Structured Data using Deep Reinforcement Learning Abstract: Survival analysis is playing a major role in manufacturing sector by analyzing occurrence of any unwanted event based on the input data. Predictive maintenance, which is a part of survival analysis, helps to find any device failure ... |
Title: Teaching Models to Express Their Uncertainty in Words Abstract: We show that a GPT-3 model can learn to express uncertainty about its own answers in natural language -- without use of model logits. When given a question, the model generates both an answer and a level of confidence (e.g. "90% confidence" or "high... |
Title: Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers Abstract: Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatical increases in model size without significant incr... |
Title: List-Decodable Sparse Mean Estimation Abstract: Robust mean estimation is one of the most important problems in statistics: given a set of samples $\{x_1, \dots, x_n\} \subset \mathbb{R}^d$ where an $\alpha$ fraction are drawn from some distribution $D$ and the rest are adversarially corrupted, it aims to estima... |
Title: Object-wise Masked Autoencoders for Fast Pre-training Abstract: Self-supervised pre-training for images without labels has recently achieved promising performance in image classification. The success of transformer-based methods, ViT and MAE, draws the community's attention to the design of backbone architecture... |
Title: Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories Abstract: Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound (B&B) algorithm seeks to exactly solve... |
Title: Estimation of 3D Body Shape and Clothing Measurements from Frontal- and Side-view Images Abstract: The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several condit... |
Title: Fair Labeled Clustering Abstract: Numerous algorithms have been produced for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studied is group fairness, in which proportional group representation is ensured in every cluster. We ex... |
Title: Granular Generalized Variable Precision Rough Sets and Rational Approximations Abstract: Rational approximations are introduced and studied in granular graded sets and generalizations thereof by the first author in recent research papers. The concept of rationality is determined by related ontologies and coheren... |
Title: Going Deeper into Permutation-Sensitive Graph Neural Networks Abstract: The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite can be satisfied by the invariant operations over node permuta... |
Title: Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models Abstract: Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in... |
Title: WaveMix-Lite: A Resource-efficient Neural Network for Image Analysis Abstract: Gains in the ability to generalize on image analysis tasks for neural networks have come at the cost of increased number of parameters and layers, dataset sizes, training and test computations, and GPU RAM. We introduce a new architec... |
Title: Deep Learning-based Spatially Explicit Emulation of an Agent-Based Simulator for Pandemic in a City Abstract: Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) an... |
Title: Rethinking the Setting of Semi-supervised Learning on Graphs Abstract: We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of tuning hyp... |
Title: Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning Abstract: Recent progress in deep model-based reinforcement learning allows agents to be significantly more sample efficient by constructing world models of high-dimensional environments from visual observations, which enables agents to l... |
Title: Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting Abstract: Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribu... |
Title: Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead Abstract: Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient har... |
Title: Approximation of Functionals by Neural Network without Curse of Dimensionality Abstract: In this paper, we establish a neural network to approximate functionals, which are maps from infinite dimensional spaces to finite dimensional spaces. The approximation error of the neural network is $O(1/\sqrt{m})$ where $m... |
Title: Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregation Abstract: Deep convolutional neural networks (CNNs) have brought breakthroughs in processing clinical electrocardiograms (ECGs), speaker-independent speech and complex images. However, typical CNNs require a fixe... |
Title: Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty Abstract: Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibili... |
Title: Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks Abstract: Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden priv... |
Title: Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations Abstract: In this paper, we propose MAGICSTYLEGAN and MAGICSTYLEGAN-ADA - both incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to experiment with their capacity of transfer learning into a rather different do... |
Title: Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems Abstract: We propose two stochastic gradient algorithms to solve a class of saddle-point problems in a decentralized setting (without a central server). The proposed algorithms are the first to achieve sub-linear/li... |
Title: Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image Captioning Abstract: Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decay... |
Title: CyCLIP: Cyclic Contrastive Language-Image Pretraining Abstract: Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require jo... |
Title: Visual Perception of Building and Household Vulnerability from Streets Abstract: In developing countries, building codes often are outdated or not enforced. As a result, a large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. Assessing housing quality is ... |
Title: Collaborative likelihood-ratio estimation over graphs Abstract: Assuming we have i.i.d observations from two unknown probability density functions (pdfs), $p$ and $p'$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs just by relying on the available data, and without knowing ... |
Title: ByteComp: Revisiting Gradient Compression in Distributed Training Abstract: Gradient compression (GC) is a promising approach to addressing the communication bottleneck in distributed deep learning (DDL). However, it is challenging to find the optimal compression strategy for applying GC to DDL because of the in... |
Title: Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors Abstract: Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-... |
Title: Efficient-Adam: Communication-Efficient Distributed Adam with Complexity Analysis Abstract: Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding $\varepsilon$-statio... |
Title: Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit Abstract: In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian disinformation outlets output torrents of misleading and outright false information.... |
Title: Noise-Aware Statistical Inference with Differentially Private Synthetic Data Abstract: While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing... |
Title: Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline Abstract: We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability stemming from task agnosticism, as well as additional difficulties of continual learni... |
Title: SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech Abstract: Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attacke... |
Title: Optimal Decision Diagrams for Classification Abstract: Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth. Accordingly, decision diagrams are us... |
Title: Introducing Non-Linearity into Quantum Generative Models Abstract: The evolution of an isolated quantum system is linear, and hence quantum algorithms are reversible, including those that utilize quantum circuits as generative machine learning models. However, some of the most successful classical generative mod... |
Title: Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG Abstract: The adoption of deep learning-based healthcare decision support systems such as the detection of irregular cardiac rhythm is hindered by challenges such as lack of access to quality data and the high costs associated with th... |
Title: Additive Higher-Order Factorization Machines Abstract: In the age of big data and interpretable machine learning, approaches need to work at scale and at the same time allow for a clear mathematical understanding of the method's inner workings. While there exist inherently interpretable semi-parametric regressio... |
Title: History-Restricted Online Learning Abstract: We introduce the concept of history-restricted no-regret online learning algorithms. An online learning algorithm $\mathcal{A}$ is $M$-history-restricted if its output at time $t$ can be written as a function of the $M$ previous rewards. This class of online learning ... |
Title: Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations Abstract: Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fideli... |
Title: Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization Abstract: Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our ... |
Title: A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization Abstract: Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation. In previous work, researchers have develo... |
Title: Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction Abstract: Representation (feature) space is an environment where data points are vectorized, distances are computed, patterns are characterized, and geometric structures are embedded. Extracting a good rep... |
Title: Improving VAE-based Representation Learning Abstract: Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the representations learned by VAE are less competitive than other non-latent vari... |
Title: SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners Abstract: Self-supervised Masked Autoencoders (MAE) are emerging as a new pre-training paradigm in computer vision. MAE learns semantics implicitly via reconstructing local patches, requiring thousands of pre-training epochs to achieve favorabl... |
Title: Functional Linear Regression of CDFs Abstract: The estimation of cumulative distribution functions (CDF) is an important learning task with a great variety of downstream applications, e.g., risk assessments in predictions and decision making. We study functional regression of contextual CDFs where each data poin... |
Title: The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization Abstract: Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training. Recently, the technique of Inv... |
Title: Machine Learning for Microcontroller-Class Hardware -- A Review Abstract: The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering ... |
Title: A Model of One-Shot Generalization Abstract: We provide a theoretical framework to study a phenomenon that we call one-shot generalization. This phenomenon refers to the ability of an algorithm to perform transfer learning within a single task, meaning that it correctly classifies a test point that has a single ... |
Title: Representation Gap in Deep Reinforcement Learning Abstract: Deep reinforcement learning gives the promise that an agent learns good policy from high-dimensional information. Whereas representation learning removes irrelevant and redundant information and retains pertinent information. We consider the representat... |
Title: Calibrated Predictive Distributions via Diagnostics for Conditional Coverage Abstract: Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. A large body of work (including normalizing flows and Bayesian neural networks) has been devoted to describing the entire predictive ... |
Title: AutoDisc: Automatic Distillation Schedule for Large Language Model Compression Abstract: Driven by the teacher-student paradigm, knowledge distillation is one of the de facto ways for language model compression. Recent studies have uncovered that conventional distillation is less effective when facing a large ca... |
Title: Provable Benefits of Representational Transfer in Reinforcement Learning Abstract: We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a target task. We propos... |
Title: No-regret Learning in Repeated First-Price Auctions with Budget Constraints Abstract: Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions. Although there has been a line of works concerning online bidding strategies in first-price auctions, it s... |
Title: 3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction Abstract: Recently, the transformer model has been successfully employed for the multi-view 3D reconstruction problem. However, challenges remain on designing an attention mechanism to explore the multiview features and exploit their relations ... |
Title: Learning Locality and Isotropy in Dialogue Modeling Abstract: Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produc... |
Title: Masked Distillation with Receptive Tokens Abstract: Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However, not every pixel contributes equally to the performance, and a good student shou... |
Title: Independent and Decentralized Learning in Markov Potential Games Abstract: We propose a multi-agent reinforcement learning dynamics, and analyze its convergence properties in infinite-horizon discounted Markov potential games. We focus on the independent and decentralized setting, where players can only observe ... |
Title: Joint Abductive and Inductive Neural Logical Reasoning Abstract: Neural logical reasoning (NLR) is a fundamental task in knowledge discovery and artificial intelligence. NLR aims at answering multi-hop queries with logical operations on structured knowledge bases based on distributed representations of queries a... |
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