Search Results for author:
Found 126 papers, 36 papers with code
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation.
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
By establishing the connection between backdoor risk and adversarial risk, we derive a novel upper bound for backdoor risk, which mainly captures the risk on the shared adversarial examples (SAEs) between the backdoored model and the purified model.
Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
Recent studies have demonstrated the susceptibility of deep neural networks to backdoor attacks.
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods.
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved.
Information Design in Multi-Agent Reinforcement Learning
To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Diverse Policy Optimization for Structured Action Space
We propose a simple and effective RL method, Diverse Policy Optimization (DPO), to model the policies in structured action space as the energy-based models (EBM) by following the probabilistic RL framework.
Mean Parity Fair Regression in RKHS
We study the fair regression problem under the notion of Mean Parity (MP) fairness, which requires the conditional mean of the learned function output to be constant with respect to the sensitive attributes.
Learning Roles with Emergent Social Value Orientations
Social dilemmas can be considered situations where individual rationality leads to collective irrationality.
Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport
In particular, we propose to estimate the transition flows from aggregated data by learning the cost functions of the MOT framework, which enables us to capture time-varying dynamic patterns.
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
Compared to the state-of-the-art reinforcement learning method, our model's network parameters are reduced to only 37\% of theirs, and the solution gap of our model towards the expert solutions decreases from 6. 8\% to 1. 3\% on average.
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base classes with sufficient examples, where how to decide the transfer weights from base classes to novel classes is the key.
Differentially Private Estimation of Hawkes Process
Point process models are of great importance in real world applications.
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set.
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information.
Learning Neural Hamiltonian Dynamics: A Methodological Overview
The past few years have witnessed an increased interest in learning Hamiltonian dynamics in deep learning frameworks.
Obtaining Dyadic Fairness by Optimal Transport
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.
Multi-Agent Path Finding with Prioritized Communication Learning
The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.
Multi-Agent Path Finding
Multi-agent Reinforcement Learning
+1
VMAgent: Scheduling Simulator for Reinforcement Learning
A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
In this case, GNNs can barely learn useful information, resulting in prohibitive difficulty in making actions for successively rewiring edges under a reinforcement learning context.
Learning Prototype-oriented Set Representations for Meta-Learning
Since our plug-and-play framework can be applied to many meta-learning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling.
Low-rank Matrix Recovery With Unknown Correspondence
We study a matrix recovery problem with unknown correspondence: given the observation matrix $M_o=[A,\tilde P B]$, where $\tilde P$ is an unknown permutation matrix, we aim to recover the underlying matrix $M=[A, B]$.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.
Self-Training with Differentiable Teacher
In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.
Sqrt(d) Dimension Dependence of Langevin Monte Carlo
This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance.
Influence Estimation and Maximization via Neural Mean-Field Dynamics
Directly using information diffusion cascade data, our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities.
Mean-Square Analysis with An Application to Optimal Dimension Dependence of Langevin Monte Carlo
This bound improves the best previously known $\widetilde{\mathcal{O}}\left(\frac{d}{\epsilon}\right)$ result and is optimal in both dimension $d$ and accuracy tolerance $\epsilon$ for log-smooth and log-strongly-convex target measures.
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.
Random Noise Defense Against Query-Based Black-Box Attacks
We conduct the theoretical analysis about the effectiveness of RND against query-based black-box attacks and the corresponding adaptive attacks.
Mean Field Game GAN
We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework.
Graph-Based Tri-Attention Network for Answer Ranking in CQA
However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked.
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition
In this paper, we introduce a novel notion, the $\delta$-measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies.
Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning
In order to improve the efficiency of cooperation and exploration, we propose a structured diversification emergence MARL framework named {\sc{Rochico}} based on reinforced organization control and hierarchical consensus learning.
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions.
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mechanism.
Inductive Collaborative Filtering via Relation Graph Learning
In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.
Mutual Calibration between Explicit and Implicit Deep Generative Models
Deep generative models are generally categorized into explicit models and implicit models.
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability.
Learning Graphons via Structured Gromov-Wasserstein Barycenters
Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs.
Differentiable Top-k with Optimal Transport
Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.
Learning Strategic Network Emergence Games
Real-world networks, especially the ones that emerge due to actions of animate agents (e. g. humans, animals), are the result of underlying strategic mechanisms aimed at maximizing individual or collective benefits.
A Hypergradient Approach to Robust Regression without Correspondence
Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models.
Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy using logged trajectory data generated from a different behavior policy, without execution of the target policy.
Differentiable Top-$k$ with Optimal Transport
The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging.
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and inference with graph-structured data.
Network Diffusions via Neural Mean-Field Dynamics
We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks.
Hessian-Free High-Resolution Nesterov Acceleration for Sampling
Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed \citep{shi2021understanding}.
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Learning Cost Functions for Optimal Transport
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples.
Transformer Hawkes Process
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index, which is coupled to the MCMC algorithm.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types.
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.
Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks
Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning.
Learning Stochastic Behaviour from Aggregate Data
Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable.
Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption
Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users’ writing style and traits, and is more practical to meet users’ real demands.
Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers.
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSP
More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood.
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
The interpretability of the learned skills show the promise of the proposed method for achieving human-AI cooperation in team sports games.
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network.
Single Episode Policy Transfer in Reinforcement Learning
An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.
Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies.
Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators
To take full advantages of both models and enable mutual compensation, we propose a novel joint training framework that bridges an explicit (unnormalized) density estimator and an implicit sample generator via Stein discrepancy.
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models
Deep generative models are generally categorized into explicit models and implicit models.
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Meta Learning with Relational Information for Short Sequences
This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network.
Modeling Event Propagation via Graph Biased Temporal Point Process
Temporal point process is widely used for sequential data modeling.
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Temporal point process is an expressive tool for modeling event sequences over time.
DyRep: Learning Representations over Dynamic Graphs
We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes).
On Scalable and Efficient Computation of Large Scale Optimal Transport
Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Prior relevant studies recommend treatments either use supervised learning (e. g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e. g. maximizing evaluation signal which indicates cumulative reward from survival rates).
Learning to Optimize via Wasserstein Deep Inverse Optimal Control
We first propose a unified KL framework that generalizes existing maximum entropy inverse optimal control methods.
Iterative Learning with Open-set Noisy Labels
We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.
Representation Learning over Dynamic Graphs
How can we effectively encode evolving information over dynamic graphs into low-dimensional representations?
A Fast Proximal Point Method for Computing Exact Wasserstein Distance
However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms.
Learning to Match via Inverse Optimal Transport
We emphasize that the discrete optimal transport plays the role of a variational principle which gives rise to an optimization-based framework for modeling the observed empirical matching data.
Visually Explainable Recommendation
By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.
Decoupled Learning for Factorial Marked Temporal Point Processes
In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i. e. a marker.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
Hawkes Processes for Invasive Species Modeling and Management
The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses in agriculture and forestry.
Predicting User Activity Level In Point Processes With Mass Transport Equation
Point processes are powerful tools to model user activities and have a plethora of applications in social sciences.
Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
A unified framework for manifold landmarking
Active manifold learning aims to select and label representative landmarks on a manifold from a given set of samples to improve semi-supervised manifold learning.
Learning Registered Point Processes from Idiosyncratic Observations
A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects.
THAP: A Matlab Toolkit for Learning with Hawkes Processes
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena.
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied.
Fake News Mitigation via Point Process Based Intervention
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time.
A constrained clustering based approach for matching a collection of feature sets
In this paper, we consider the problem of finding the feature correspondences among a collection of feature sets, by using their point-wise unary features.
Self-Paced Multi-Task Learning
In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL).
A Self-Paced Regularization Framework for Multi-Label Learning
In this paper, we propose a novel multi-label learning framework, called Multi-Label Self-Paced Learning (MLSPL), in an attempt to incorporate the self-paced learning strategy into multi-label learning regime.
Fractal Dimension Invariant Filtering and Its CNN-based Implementation
By adding a nonlinear post-processing step behind anisotropic filter banks, we demonstrate that the proposed filtering method is capable of preserving the local invariance of the fractal dimension of image.
Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes
By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days.
Learning Granger Causality for Hawkes Processes
In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process.
Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage
Facing to the challenges of trajectory clustering, e. g., large variations within a cluster and ambiguities across clusters, we first introduce an adaptive multi-kernel-based estimation process to estimate the `shrunk' positions and speeds of trajectories' points.
A Matrix Decomposition Perspective to Multiple Graph Matching
Graph matching has a wide spectrum of real-world applications and in general is known NP-hard.
A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media
In our experiments, we demonstrate that our algorithm is able to achieve the-state-of-the-art performance in terms of analyzing, predicting, and prioritizing events.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.
Discrete Hyper-Graph Matching
Evaluations on both synthetic and real-world data corroborate the efficiency of our method.
Joint Active Learning with Feature Selection via CUR Matrix Decomposition
In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling.
A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting
We propose multi-graph matching methods to incorporate the two aspects by boosting the affinity score, meanwhile gradually infusing the consistency as a regularizer.
Shaping Social Activity by Incentivizing Users
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network.
Linear Contour Learning: A Method for Supervised Dimension Reduction
We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface.
Manifold Based Dynamic Texture Synthesis from Extremely Few Samples
In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e. g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning.
Scalable Influence Estimation in Continuous-Time Diffusion Networks
If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages?
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
We propose Dirichlet-Bernoulli Alignment (DBA), a generative model for corpora in which each pattern (e. g., a document) contains a set of instances (e. g., paragraphs in the document) and belongs to multiple classes.
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems.
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment
Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications.
