text stringlengths 0 4.09k |
|---|
Title: Stochastic Zeroth order Descent with Structured Directions Abstract: We introduce and analyze Structured Stochastic Zeroth order Descent (S-SZD), a finite difference approach which approximates a stochastic gradient on a set of $l\leq d$ orthogonal directions, where $d$ is the dimension of the ambient space. The... |
Title: PAVI: Plate-Amortized Variational Inference Abstract: Given some observed data and a probabilistic generative model, Bayesian inference aims at obtaining the distribution of a model's latent parameters that could have yielded the data. This task is challenging for large population studies where thousands of meas... |
Title: Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy Abstract: Multi-agent deep reinforcement learning has been applied to address a variety of complex problems with either discrete or continuous action spaces and achieved great success. However, most real-world environments... |
Title: Saccade Mechanisms for Image Classification, Object Detection and Tracking Abstract: We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed approach is based on the ideas of attention-drive... |
Title: Federated Momentum Contrastive Clustering Abstract: We present federated momentum contrastive clustering (FedMCC), a learning framework that can not only extract discriminative representations over distributed local data but also perform data clustering. In FedMCC, a transformed data pair passes through both the... |
Title: Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging Abstract: Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes only observe the messages sent by... |
Title: Tensor Train for Global Optimization Problems in Robotics Abstract: The convergence of many numerical optimization techniques is highly sensitive to the initial guess provided to the solver. We propose an approach based on tensor methods to initialize the existing optimization solvers close to global optima. The... |
Title: Diffeomorphic Counterfactuals with Generative Models Abstract: Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transfo... |
Title: We Cannot Guarantee Safety: The Undecidability of Graph Neural Network Verification Abstract: Graph Neural Networks (GNN) are commonly used for two tasks: (whole) graph classification and node classification. We formally introduce generically formulated decision problems for both tasks, corresponding to the foll... |
Title: From Labels to Priors in Capsule Endoscopy: A Prior Guided Approach for Improving Generalization with Few Labels Abstract: The lack of generalizability of deep learning approaches for the automated diagnosis of pathologies in Wireless Capsule Endoscopy (WCE) has prevented any significant advantages from tricklin... |
Title: Scalable Deep Gaussian Markov Random Fields for General Graphs Abstract: Machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models ... |
Title: Weighted Ensembles for Active Learning with Adaptivity Abstract: Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects... |
Title: The Generalized Eigenvalue Problem as a Nash Equilibrium Abstract: The generalized eigenvalue problem (GEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such as canonical correlation analysis, independent components analysis, partial l... |
Title: Zero-Shot Audio Classification using Image Embeddings Abstract: Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typically requires manual labeling which is expensive and time-consuming. Z... |
Title: Convolutional Layers Are Not Translation Equivariant Abstract: The purpose of this paper is to correct a misconception about convolutional neural networks (CNNs). CNNs are made up of convolutional layers which are shift equivariant due to weight sharing. However, contrary to popular belief, convolutional layers ... |
Title: Refining neural network predictions using background knowledge Abstract: Recent work has showed we can use logical background knowledge in learning system to compensate for a lack of labeled training data. Many such methods work by creating a loss function that encodes this knowledge. However, often the logic is... |
Title: Causal Discovery in Hawkes Processes by Minimum Description Length Abstract: Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of events in the future. Discovery of the underlying influenc... |
Title: Flexible Differentiable Optimization via Model Transformations Abstract: We introduce DiffOpt.jl, a Julia library to differentiate through the solution of convex optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInterface, thus ... |
Title: Deep Learning-based Massive MIMO CSI Acquisition for 5G Evolution and 6G Abstract: Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mec... |
Title: Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models Abstract: Foundation models are becoming the dominant deep learning technologies. Pretraining a foundation model is always time-consumed due to the large scale of both the model parameter and training... |
Title: Evolutionary Echo State Network: evolving reservoirs in the Fourier space Abstract: The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have recently received significant attention due to t... |
Title: Deep Multi-view Semi-supervised Clustering with Sample Pairwise Constraints Abstract: Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the signi... |
Title: Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation Abstract: Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local centers can vary from each oth... |
Title: Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022) Abstract: Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features us... |
Title: MAREO: Memory- and Attention- based visual REasOning Abstract: Humans continue to vastly outperform modern AI systems in their ability to parse and understand complex visual scenes flexibly. Attention and memory are two systems known to play a critical role in our ability to selectively maintain and manipulate b... |
Title: A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model Abstract: The nervous system, more specifically, the brain, is capable of solving complex problems simply and efficiently, far surpassing modern computers. In this regard, neuromorphic engineering is a research field that foc... |
Title: Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality Abstract: Goal-oriented Reinforcement Learning, where the agent needs to reach the goal state while simultaneously minimizing the cost, has received significant attention in real-world applications. Its theoretical formulation, stochas... |
Title: Fisher SAM: Information Geometry and Sharpness Aware Minimisation Abstract: Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness. SAM essentially modifies the loss function by reporting the maximum loss value within the smal... |
Title: NAGphormer: Neighborhood Aggregation Graph Transformer for Node Classification in Large Graphs Abstract: Graph Transformers have demonstrated superiority on various graph learning tasks in recent years. However, the complexity of existing Graph Transformers scales quadratically with the number of nodes, making i... |
Title: Dynamic stability of power grids -- new datasets for Graph Neural Networks Abstract: One of the key challenges for the success of the energy transition towards renewable energies is the analysis of the dynamic stability of power grids. However, dynamic solutions are intractable and exceedingly expensive for larg... |
Title: Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers Abstract: Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. However, ... |
Title: Learning to Estimate Shapley Values with Vision Transformers Abstract: Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem. Current explanation approaches rely on attention values or input gradients, but these give a li... |
Title: Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes Abstract: Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outcome metric. However, in... |
Title: Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering Abstract: To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image features, which is ... |
Title: Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model Abstract: Current architectures for multi-modality tasks such as visual question answering suffer from their high complexity. As a result, these architectures are difficult to train and require high computational resources. To address these pr... |
Title: Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns Abstract: We study the problem of consistently recovering the sparsity pattern of a regression parameter vector from correlated observations governed by deterministic missing data patterns using Lasso. We consider the case in whic... |
Title: Explaining Neural Networks without Access to Training Data Abstract: We consider generating explanations for neural networks in cases where the network's training data is not accessible, for instance due to privacy or safety issues. Recently, $\mathcal{I}$-Nets have been proposed as a sample-free approach to pos... |
Title: Adversarial Counterfactual Environment Model Learning Abstract: A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, recommender systems, and patients' treatment selection. We can take unli... |
Title: Deep Leakage from Model in Federated Learning Abstract: Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn increasing attentions from both academia and industry. In this context, federat... |
Title: $\mathsf{G^2Retro}$: Two-Step Graph Generative Models for Retrosynthesis Prediction Abstract: Retrosynthesis is a procedure where a molecule is transformed into potential reactants and thus the synthesis routes are identified. We propose a novel generative framework, denoted as $\mathsf{G^2Retro}$, for one-step ... |
Title: Efficient Per-Shot Convex Hull Prediction By Recurrent Learning Abstract: Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints. The traditional method of content dependent bitrate ladder selection requi... |
Title: Imitation Learning via Differentiable Physics Abstract: Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this w... |
Title: Multi-fidelity Hierarchical Neural Processes Abstract: Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-fidelity surrogate modeling reduces the computational cost by fusing different ... |
Title: Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction Abstract: Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However, SL still has limitations such as potential data pr... |
Title: Symbolic image detection using scene and knowledge graphs Abstract: Sometimes the meaning conveyed by images goes beyond the list of objects they contain; instead, images may express a powerful message to affect the viewers' minds. Inferring this message requires reasoning about the relationships between the obj... |
Title: Conformal Prediction Intervals for Markov Decision Process Trajectories Abstract: Before delegating a task to an autonomous system, a human operator may want a guarantee about the behavior of the system. This paper extends previous work on conformal prediction for functional data and conformalized quantile regre... |
Title: Mixed integer linear optimization formulations for learning optimal binary classification trees Abstract: Decision trees are powerful tools for classification and regression that attract many researchers working in the burgeoning area of machine learning. One advantage of decision trees over other methods is the... |
Title: Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition Abstract: Human activity recognition (HAR) through wearable devices has received much interest due to its numerous applications in fitness tracking, wellness screening, and... |
Title: Neural Laplace: Learning diverse classes of differential equations in the Laplace domain Abstract: Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities, whic... |
Title: Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering Abstract: To avoid the curse of dimensionality, a common approach to clustering high-dimensional data is to first project the data into a space of reduced dimension, and then cluster the projected data. Although effective, th... |
Title: A Correlation-Ratio Transfer Learning and Variational Stein's Paradox Abstract: A basic condition for efficient transfer learning is the similarity between a target model and source models. In practice, however, the similarity condition is difficult to meet or is even violated. Instead of the similarity conditio... |
Title: In Defense of Core-set: A Density-aware Core-set Selection for Active Learning Abstract: Active learning enables the efficient construction of a labeled dataset by labeling informative samples from an unlabeled dataset. In a real-world active learning scenario, considering the diversity of the selected samples i... |
Title: Communication Efficient Distributed Learning for Kernelized Contextual Bandits Abstract: We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distributed bandit learning, existing solutions are ... |
Title: Training Neural Networks using SAT solvers Abstract: We propose an algorithm to explore the global optimization method, using SAT solvers, for training a neural net. Deep Neural Networks have achieved great feats in tasks like-image recognition, speech recognition, etc. Much of their success can be attributed to... |
Title: The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon Abstract: The grokking phenomenon as reported by Power et al. ( arXiv:2201.02177 ) refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization. In this p... |
Title: Empirical Bayes approach to Truth Discovery problems Abstract: When aggregating information from conflicting sources, one's goal is to find the truth. Most real-value \emph{truth discovery} (TD) algorithms try to achieve this goal by estimating the competence of each source and then aggregating the conflicting i... |
Title: Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems Abstract: Data assimilation (DA) is a key component of many forecasting models in science and engineering. DA allows one to estimate better initial conditions using an imperfect dynamical model of the system ... |
Title: CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation Abstract: Human annotated data plays a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into dataset annota... |
Title: Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream Abstract: Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based... |
Title: Comprehensive Fair Meta-learned Recommender System Abstract: In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation sce... |
Title: Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation Abstract: Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera. Although existing works use intermediate heatmap... |
Title: On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks Abstract: We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the... |
Title: ReFace: Real-time Adversarial Attacks on Face Recognition Systems Abstract: Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an input-dependent optimization problem using gradient descent w... |
Title: Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations Abstract: Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, to date, offline reinf... |
Title: Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models Abstract: We study the problem of learning generalized linear models under adversarial corruptions. We analyze a classical heuristic called the iterative trimmed maximum likelihood estimator which is known to be effective agai... |
Title: What should AI see? Using the Public's Opinion to Determine the Perception of an AI Abstract: Deep neural networks (DNN) have made impressive progress in the interpretation of image data, so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. Fr... |
Title: Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis Abstract: Creating a meaningful representation by fusing single modalities (e.g., text, images, or audio) is the core concept of multimodal learning. Although several techniques for building multimodal representations have be... |
Title: Joint Entropy Search For Maximally-Informed Bayesian Optimization Abstract: Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy... |
Title: Neural Bregman Divergences for Distance Learning Abstract: Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algor... |
Title: Data-Efficient Double-Win Lottery Tickets from Robust Pre-training Abstract: Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate such enormous pre-trained models can be replaced by extreme... |
Title: An Empirical Study on Disentanglement of Negative-free Contrastive Learning Abstract: Negative-free contrastive learning has attracted a lot of attention with simplicity and impressive performance for large-scale pretraining. But its disentanglement property remains unexplored. In this paper, we take different n... |
Title: HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration Abstract: HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on ... |
Title: Mildly Conservative Q-Learning for Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necess... |
Title: Strong Memory Lower Bounds for Learning Natural Models Abstract: We give lower bounds on the amount of memory required by one-pass streaming algorithms for solving several natural learning problems. In a setting where examples lie in $\{0,1\}^d$ and the optimal classifier can be encoded using $\kappa$ bits, we s... |
Title: Mobility Improves the Convergence of Asynchronous Federated Learning Abstract: This paper studies asynchronous Federated Learning (FL) subject to clients' individual arbitrary communication patterns with the parameter server. We propose FedMobile, a new asynchronous FL algorithm that exploits the mobility attrib... |
Title: Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning Abstract: We present a full implementation and simulation of a novel quantum reinforcement learning (RL) method and mathematically prove a quantum advantage. Our approach shows in detail ho... |
Title: A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models Abstract: Responsible use of machine learning requires that models be audited for undesirable properties. However, how to do principled auditing in a general setting has remained ill-understood. In this paper, we propose a formal le... |
Title: I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs Abstract: Although machine learning on hypergraphs has attracted considerable attention, most of the works have focused on (semi-)supervised learning, which may cause heavy labeling costs and poor generalization. Recently, contrast... |
Title: A Novel Partitioned Approach for Reduced Order Model -- Finite Element Model (ROM-FEM) and ROM-ROM Coupling Abstract: Partitioned methods allow one to build a simulation capability for coupled problems by reusing existing single-component codes. In so doing, partitioned methods can shorten code development and v... |
Title: Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination Abstract: Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration, is capable of superb sample efficiency, b... |
Title: AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging Abstract: This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database w... |
Title: Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance Abstract: Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unloc... |
Title: STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer Abstract: Modeling neural population dynamics underlying noisy single-trial spiking activities is essential for relating neural observation and behavior. A recent non-recurrent method - Neural Data Transformers (NDT) - has shown great s... |
Title: COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning Abstract: Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA on various downstream tasks. The graph augmentation step is a vital but scarcely studied step of GCL. In this paper, we show that the... |
Title: On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data Abstract: Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the simple FedAvg algorithm converges very well. This... |
Title: Principal Trade-off Analysis Abstract: This paper develops Principal Trade-off Analysis (PTA), a decomposition method, analogous to Principal Component Analysis (PCA), which permits the representation of any game as the weighted sum of disc games (continuous R-P-S games). Applying PTA to empirically generated to... |
Title: A Resilient Distributed Boosting Algorithm Abstract: Given a learning task where the data is distributed among several parties, communication is one of the fundamental resources which the parties would like to minimize. We present a distributed boosting algorithm which is resilient to a limited amount of noise. ... |
Title: Neural Prompt Search Abstract: The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-efficient tuning methods, such as learning adapter layers or visual prompt tokens, which allow a tiny por... |
Title: Overcoming the Spectral Bias of Neural Value Approximation Abstract: Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm. While multi-layer perceptron networks are uni... |
Title: Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry Abstract: The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algor... |
Title: DiSparse: Disentangled Sparsification for Multitask Model Compression Abstract: Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, w... |
Title: Distillation Decision Tree Abstract: Black-box machine learning models are criticized as lacking interpretability, although they tend to have good prediction accuracy. Knowledge Distillation (KD) is an emerging tool to interpret the black-box model by distilling its knowledge into a transparent model. With well-... |
Title: BigVGAN: A Universal Neural Vocoder with Large-Scale Training Abstract: Despite recent progress in generative adversarial network(GAN)-based vocoders, where the model generates raw waveform conditioned on mel spectrogram, it is still challenging to synthesize high-fidelity audio for numerous speakers across vari... |
Title: VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution Abstract: Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively lo... |
Title: Globally Optimal Algorithms for Fixed-Budget Best Arm Identification Abstract: We consider the fixed-budget best arm identification problem where the goal is to find the arm of the largest mean with a fixed number of samples. It is known that the probability of misidentifying the best arm is exponentially small ... |
Title: Probability flow solution of the Fokker-Planck equation Abstract: The method of choice for integrating the time-dependent Fokker-Planck equation in high-dimension is to generate samples from the solution via integration of the associated stochastic differential equation. Here, we introduce an alternative scheme ... |
Title: Regret Bounds for Information-Directed Reinforcement Learning Abstract: Information-directed sampling (IDS) has revealed its potential as a data-efficient algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for Markov Decision Processes (MDPs) is still limited. We develop novel i... |
Title: Spatial Entropy Regularization for Vision Transformers Abstract: Recent work has shown that the attention maps of Vision Transformers (VTs), when trained with self-supervision, can contain a semantic segmentation structure which does not spontaneously emerge when training is supervised. In this paper, we explici... |
Title: Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations Abstract: Learning from demonstration (LfD) methods have shown promise for solving multi-step tasks; however, these approaches do not guarantee successful reproduction of the task given disturbances. In this work, we identify... |
Title: AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition Abstract: We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., aft... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.