Search Results for author:
Found 158 papers, 62 papers with code
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.
Temporal Logic Point Processes
We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families.
Progress and Opportunities of Foundation Models in Bioinformatics
Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs.
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein
We propose a unified protein language model, xTrimoPGLM, to address these two types of tasks simultaneously through an innovative pre-training framework.
Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein Prediction
Predicting the structure of interacting chains is crucial for understanding biological systems and developing new drugs.
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.
XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical Images
Perturbations in consistency-based semi-supervised models are often artificially designed.
xTrimoABFold: De novo Antibody Structure Prediction without MSA
Its improvement in both accuracy and efficiency makes it a valuable tool for de novo antibody design and could make further improvements in immuno-theory.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
We study the problem of extracting N-ary relation tuples from scientific articles.
Uncovering the Structural Fairness in Graph Contrastive Learning
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world.
SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling
Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative
Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information.
Graph Condensation via Receptive Field Distribution Matching
Thus, we propose Graph Condesation via Receptive Field Distribution Matching (GCDM), which is accomplished by optimizing the synthetic graph through the use of a distribution matching loss quantified by maximum mean discrepancy (MMD).
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.
Learning Temporal Rules from Noisy Timeseries Data
Events across a timeline are a common data representation, seen in different temporal modalities.
Locality Sensitive Teaching
However, as one of the most influential and practical MT paradigms, iterative machine teaching (IMT) is prohibited on IoT devices due to its inefficient and unscalable algorithms.
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning
Deep learning and symbolic reasoning are complementary techniques for an intelligent system.
Molecular Attributes Transfer from Non-Parallel Data
Optimizing chemical molecules for desired properties lies at the core of drug development.
Multi-task Learning of Order-Consistent Causal Graphs
We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.
RoMA: Robust Model Adaptation for Offline Model-based Optimization
We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
We solve the first challenge by reformulating the estimation problem as an equivalent discrete time-inhomogeneous hidden Markov model.
GNN is a Counter? Revisiting GNN for Question Answering
To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability.
Ranked #12 on
Question Answering
on OpenBookQA
ProTo: Program-Guided Transformer for Program-Guided Tasks
Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program.
Ranked #1 on
Visual Question Answering (VQA)
on GQA test-std
Explaining Point Processes by Learning Interpretable Temporal Logic Rules
We propose a principled method to learn a set of human-readable logic rules to explain temporal point processes.
Spanning Tree-based Graph Generation for Molecules
In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.
Neural Temporal Logic Programming
Events across a timeline are a common data representation, seen in different temporal modalities.
Provable Learning-based Algorithm For Sparse Recovery
In this work, we propose PLISA (Provable Learning-based Iterative Sparse recovery Algorithm) to learn algorithms automatically from data.
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.
Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients
We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems.
How to Design Sample and Computationally Efficient VQA Models
In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested.
Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data.
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA
We present a differentiable end-to-end program executor (DePe), which addresses Visual Question Answering (VQA) in a sample and computationally efficient manner.
Molecule Optimization by Explainable Evolution
Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation
We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.
Learning Two-Time-Scale Representations For Large Scale Recommendations
We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.
Improving Learning to Branch via Reinforcement Learning
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).
A Framework For Differentiable Discovery Of Graph Algorithms
Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms.
The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models
Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.
Understanding Deep Architecture with Reasoning Layer
Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders
The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives.
Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on
Question Answering
on DROP Test
Answering Any-hop Open-domain Questions with Iterative Document Reranking
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.
Ranked #16 on
Question Answering
on HotpotQA
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product.
Ranked #5 on
Multi-step retrosynthesis
on USPTO-190
Understanding Deep Architectures with Reasoning Layer
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.
Bandit Samplers for Training Graph Neural Networks
However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Learning to Stop While Learning to Predict
Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already.
HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS
We present a learning-based approach to detect and fix a broad range of bugs in Javascript programs.
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Orthogonal Over-Parameterized Training
The inductive bias of a neural network is largely determined by the architecture and the training algorithm.
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints.
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry.
Ranked #25 on
Single-step retrosynthesis
on USPTO-50k
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
Neural Similarity Learning
By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs.
Learn to Explain Efficiently via Neural Logic Inductive Learning
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.
Deep Interaction Processes for Time-Evolving Graphs
To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.
Language Modeling with Shared Grammar
Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language.
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction
In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.
Can Graph Neural Networks Help Logic Reasoning?
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.
GLAD: Learning Sparse Graph Recovery
Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.
Learning a Meta-Solver for Syntax-Guided Program Synthesis
Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the specification and grammar embedding and adaptive policy.
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.
Particle Flow Bayes' Rule
We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation.
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems.
Generative Adversarial Network
Model-based Reinforcement Learning
+3
Double Neural Counterfactual Regret Minimization
Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).
Meta Architecture Search
Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.
Learning Loop Invariants for Program Verification
A fundamental problem in program verification concerns inferring loop invariants.
Coupled Variational Bayes via Optimization Embedding
This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.
Reinforcement Learning for Uplift Modeling
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.
Learning Temporal Point Processes via Reinforcement Learning
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space.
Characterizing Malicious Edges targeting on Graph Neural Networks
Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets.
Latent Dirichlet Allocation for Internet Price War
Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
We study instancewise feature importance scoring as a method for model interpretation.
Learning Steady-States of Iterative Algorithms over Graphs
Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.
Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various applications.
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
Our model learns to reason with neural embeddings of both knowledge graphs.
Learning towards Minimum Hyperspherical Energy
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
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.
Decoupled Networks
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.
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.
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data.
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
We introduce instancewise feature selection as a methodology for model interpretation.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.
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.
Deep Hyperspherical Learning
In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds.
Towards Black-box Iterative Machine Teaching
We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.
Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.
On the Complexity of Learning Neural Networks
Moreover, this hard family of functions is realizable with a small (sublinear in dimension) number of activation units in the single hidden layer.
Iterative Machine Teaching
Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.
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.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.
SphereFace: Deep Hypersphere Embedding for Face Recognition
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
Ranked #1 on
Face Verification
on CK+
Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
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.
Deep Semi-Random Features for Nonlinear Function Approximation
For deep models, with no unrealistic assumptions, we prove universal approximation ability, a lower bound on approximation error, a partial optimization guarantee, and a generalization bound.
Variational Policy for Guiding Point Processes
Temporal point processes have been widely applied to model event sequence data generated by online users.
Stochastic Generative Hashing
Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases.
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.
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Matching users to the right items at the right time is a fundamental task in recommendation systems.
GRAM: Graph-based Attention Model for Healthcare Representation Learning
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
Diverse Neural Network Learns True Target Functions
In this paper, we answer these questions by analyzing one-hidden-layer neural networks with ReLU activation, and show that despite the non-convexity, neural networks with diverse units have no spurious local minima.
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.
Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits
We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence.
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation
DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time.
Fast and Simple Optimization for Poisson Likelihood Models
Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems.
Learning from Conditional Distributions via Dual Embeddings
In such problems, each sample $x$ itself is associated with a conditional distribution $p(z|x)$ represented by samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links these conditional distributions to target values $y$.
Smart broadcasting: Do you want to be seen?
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them.
Detecting weak changes in dynamic events over networks
Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics.
Discriminative Embeddings of Latent Variable Models for Structured Data
Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design.
Time-Sensitive Recommendation From Recurrent User Activities
By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services.
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time.
M-Statistic for Kernel Change-Point Detection
Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.
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.
Online Supervised Subspace Tracking
The optimization problem for OSDR is non-convex and hard to analyze in general; we provide convergence analysis of OSDR in a simple linear regression setting.
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.
Scan $B$-Statistic for Kernel Change-Point Detection
A novel theoretical result of the paper is the characterization of the tail probability of these statistics using the change-of-measure technique, which focuses on characterizing the tail of the detection statistics rather than obtaining its asymptotic distribution under the null distribution.
Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters.
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
We propose a simple, computationally efficient, and memory friendly algorithm based on the "doubly stochastic gradients" to scale up a range of kernel nonlinear component analysis, such as kernel PCA, CCA and SVD.
Communication Efficient Distributed Kernel Principal Component Analysis
Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality?
Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters.
Ranked #43 on
Image Classification
on MNIST
A la Carte - Learning Fast Kernels
Kernel methods have great promise for learning rich statistical representations of large modern datasets.
Learning Time-Varying Coverage Functions
Coverage functions are an important class of discrete functions that capture laws of diminishing returns.
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.
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems.
Active Learning and Best-Response Dynamics
We examine an important setting for engineered systems in which low-power distributed sensors are each making highly noisy measurements of some unknown target function.
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
Can we recover the hidden network structures from these observed cascades?
Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.
Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions.
Robust Low Rank Kernel Embeddings of Multivariate Distributions
Kernel embedding of distributions has led to many recent advances in machine 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?
Nonparametric Estimation of Multi-View Latent Variable Models
We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters.
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large.
Learning Networks of Heterogeneous Influence
However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen.
Spectral Methods for Learning Multivariate Latent Tree Structure
The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i. e., the graph of how the underlying hidden variables are connected to each other and to the observed variables).
Kernel Bayes' Rule
A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on kernel representations of probabilities in reproducing kernel Hilbert spaces.
Kernel Embeddings of Latent Tree Graphical Models
Latent tree graphical models are natural tools for expressing long range and hierarchical dependencies among many variables which are common in computer vision, bioinformatics and natural language processing problems.
Sparsistent Learning of Varying-coefficient Models with Structural Changes
In this paper, we investigate sparsistent learning of a sub-family of this model --- piecewise constant VCVS models.
Time-Varying Dynamic Bayesian Networks
In this paper, we propose a time-varying dynamic Bayesian network (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series.
A state-space mixed membership blockmodel for dynamic network tomography
In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes.
Kernel Measures of Independence for non-iid Data
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion.
