Search Results for author:
Found 34 papers, 2 papers with code
How Does China's Household Portfolio Selection Vary with Financial Inclusion?
Portfolio underdiversification is one of the most costly losses accumulated over a household's life cycle.
$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment.
Calibration-Aware Margin Loss: Pushing the Accuracy-Calibration Consistency Pareto Frontier for Deep Metric Learning
In this paper, we propose a novel metric named Operating-Point-Incosistency-Score (OPIS) that measures the variance in the operating characteristics across different classes in a target calibration range, and demonstrate that high accuracy of a metric learning embedding model does not guarantee calibration consistency for both seen and unseen classes.
Learning for Open-World Calibration with Graph Neural Networks
We tackle the problem of threshold calibration for open-world recognition by incorporating representation compactness measures into clustering.
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in real-world applications.
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits.
Lightweight Monocular Depth Estimation
Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images.
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics.
A Survey for Efficient Open Domain Question Answering
Thus, a trade-off between accuracy, memory consumption and processing speed is pursued.
Semantic-Enhanced Image Clustering
In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model.
Communication-Efficient Collaborative Best Arm Identification
We investigate top-$m$ arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function.
Collaborative Best Arm Identification with Limited Communication on Non-IID Data
This is in stark contrast to the IID data setting, where the speedup is always $\tilde{\Omega}(1)$ regardless of $R$ and the number of agents $K$.
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well.
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis
In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner.
Deep Unsupervised Hashing with Latent Semantic Components
Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited.
A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement
After that, the second stage is applied to further suppress the residual noise components and estimate the clean phase by a complex spectral mapping network, which is a pure complex-valued network composed of complex 2D convolution/deconvolution and complex temporal-frequency attention blocks.
Learning to Cluster via Same-Cluster Queries
We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries.
Deep Self-Adaptive Hashing for Image Retrieval
Secondly, we measure the priorities of data pairs with PIC and assign adaptive weights to them, which is relies on the assumption that more dissimilar data pairs contain more discriminative information for hash learning.
Batched Thompson Sampling for Multi-Armed Bandits
We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small number of policy changes (or, batches).
Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade.
Batched Coarse Ranking in Multi-Armed Bandits
We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we have a set of arms each of which is associated with an unknown distribution.
A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation.
Multinomial Logit Bandit with Low Switching Cost
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
Collaborative Top Distribution Identifications with Limited Interaction
We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants.
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits
Best arm identification (or, pure exploration) in multi-armed bandits is a fundamental problem in machine learning.
Distinct Sampling on Streaming Data with Near-Duplicates
In this paper we study how to perform distinct sampling in the streaming model where data contain near-duplicates.
Data Structures and Algorithms
A Practical Algorithm for Distributed Clustering and Outlier Detection
We study the classic $k$-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers.
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
We study the collaborative PAC learning problem recently proposed in Blum et al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously.
Adaptive Multiple-Arm Identification
We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game.
Prediction of Sea Surface Temperature using Long Short-Term Memory
We formulate the SST prediction problem as a time series regression problem.
EmbedJoin: Efficient Edit Similarity Joins via Embeddings
Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems.
Databases
Communication-Optimal Distributed Clustering
We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site.
