Search Results for author:
Found 61 papers, 10 papers with code
Hessian-aware Quantized Node Embeddings for Recommendation
To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability.
Adversarial Collaborative Filtering for Free
In this paper, we present Sharpness-aware Collaborative Filtering (SharpCF), a simple yet effective method that conducts adversarial training without extra computational cost over the base optimizer.
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities.
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters).
Sharpness-Aware Graph Collaborative Filtering
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering.
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL algorithms.
Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience.
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data.
In-Sample Policy Iteration for Offline Reinforcement Learning
In this paper, we propose a novel algorithm employing in-sample policy iteration that substantially enhances behavior-regularized methods in offline RL.
Prompt Injection attack against LLM-integrated Applications
We deploy HouYi on 36 actual LLM-integrated applications and discern 31 applications susceptible to prompt injection.
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories.
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL
Recently, diffusion model shines as a promising backbone for the sequence modeling paradigm in offline reinforcement learning(RL).
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions.
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Multi-head self-attentions are then applied to the sequence to learn the attention between patches.
Neural Episodic Control with State Abstraction
Generally, episodic control-based approaches are solutions that leverage highly-rewarded past experiences to improve sample efficiency of DRL algorithms.
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries.
Denoising Self-attentive Sequential Recommendation
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem.
ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
The state representation conveys expressive common features of the environment learned by all the agents collectively; the linear policy representation provides a favorable space for efficient policy optimization, where novel behavior-level crossover and mutation operations can be performed.
Neural Volumetric Mesh Generator
We first propose a diffusion-based generative model to tackle this problem by generating voxelized shapes with close-to-reality outlines and structures.
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes
First, we propose a unified policy abstraction theory, containing three types of policy abstraction associated to policy features at different levels.
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.
HIFI-Net: A Novel Network for Enhancement to Underwater Images
As this network make "Haar Images into Fusion Images", it is called HIFI-Net.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
Relational Representation Learning in Visually-Rich Documents
To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views.
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF.
Revealing Reliable Signatures by Learning Top-Rank Pairs
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields.
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
However, we reveal sub-optimal collaborative behaviors also emerge with strong correlations, and simply maximizing the MI can, surprisingly, hinder the learning towards better collaboration.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space.
Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework
With these metrics, one can easily identify meta-features with the most complementary behaviors in two classifiers, and use them to better ensemble the classifiers.
Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series
The matrix profile is an effective data mining tool that provides similarity join functionality for time series data.
ED2: An Environment Dynamics Decomposition Framework for World Model Construction
To reduce the model error, previous works use a single well-designed network to fit the entire environment dynamics, which treats the environment dynamics as a black box.
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning
Such a reconstruction exploits the underlying structure of value matrix to improve the value approximation, thus leading to a more efficient learning process of value function.
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
Many exploration strategies are built upon the optimism in the face of the uncertainty (OFU) principle for reinforcement learning.
Embedding Compression with Hashing for Efficient Representation Learning in Graph
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database.
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI.
Towards Book Cover Design via Layout Graphs
Book covers are intentionally designed and provide an introduction to a book.
Normalization of Language Embeddings for Cross-Lingual Alignment
Learning a good transfer function to map the word vectors from two languages into a shared cross-lingual word vector space plays a crucial role in cross-lingual NLP.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning
Recently, the principle of optimism in the face of (aleatoric and epistemic) uncertainty has been utilized to design efficient exploration strategies for Reinforcement Learning (RL).
Merchant Category Identification Using Credit Card Transactions
In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i. e., affinity) to verify the self-reported business type (i. e., merchant category) of a given merchant.
Towards a Flexible Embedding Learning Framework
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed.
Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction.
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to fine-tune suboptimal prior knowledge.
Constrained Non-Affine Alignment of Embeddings
Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.
Gradient Boosting Survival Tree with Applications in Credit Scoring
In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm.
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
In multiagent domains, coping with non-stationary agents that change behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.
Towards Efficient Detection and Optimal Response against Sophisticated Opponents
This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies.
Multiagent Systems
End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings
The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search.
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention.
Interactive Deep Colorization With Simultaneous Global and Local Inputs
To solve this problem, we present a novel deep colorization method, which allows simultaneous global and local inputs to better control the output colorized images.
Joint convolutional neural pyramid for depth map super-resolution
The convolutional neural pyramids extract information from large receptive fields of the depth map and guidance map, while the convolutional neural network effectively transfers useful structures of the guidance image to the depth image.
Coresets for Kernel Regression
Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data.
Subsampling in Smoothed Range Spaces
We consider smoothed versions of geometric range spaces, so an element of the ground set (e. g. a point) can be contained in a range with a non-binary value in $[0, 1]$.
