Search Results for author:
Found 44 papers, 5 papers with code
Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain
To the best of our knowledge, this is the first work that solves the problem of predicting event set intensities in the continuous time domain by using TPPs.
Markov Decision Process with an External Temporal Process
Most reinforcement learning algorithms treat the context under which they operate as a stationary, isolated and undisturbed environment.
Neural Temporal Point Process for Forecasting Higher Order and Directional Interactions
Real-world systems are made of interacting entities that evolve with time.
On consistency of constrained spectral clustering under representation-aware stochastic block model
Our work leads to an interesting stochastic block model that not only plants the given partitions in $\mathcal{G}$ but also plants the auxiliary information encoded in the representation graph $\mathcal{R}$.
An Inference Approach To Question Answering Over Knowledge Graphs
The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph.
Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting
As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.
Risk-Aware Algorithms for Combinatorial Semi-Bandits
In this paper, we study the stochastic combinatorial multi-armed bandit problem under semi-bandit feedback.
CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India
Lockdowns are one of the most effective measures for containing the spread of a pandemic.
Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
This graph specifies node pairs that can represent each other with respect to sensitive attributes and is observed in addition to the usual \textit{similarity graph}.
Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by an absolute percentage reduction of $\approx\mathbf{3-25\%}$ on multiple NLP tasks while achieving the same performance with no additional computation overhead.
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm
We demonstrate the utility of our approach on navigation and goal-based tasks in a flexible simulated 3D navigation environment that we have developed.
Autonomous Navigation
Hierarchical Reinforcement Learning
+2
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints
The multi-armed bandits' framework is the most common platform to study strategies for sequential decision-making problems.
Adversarial Context Aware Network Embeddings for Textual Networks
In this paper we propose an approach that achieves both modality fusion and the capability to learn embeddings of unseen nodes.
Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation
To address this, we follow Vapnik's imperative of statistical learning that states any desired problem should be solved in the most direct way rather than solving a more general intermediate task and propose a direct approach to domain adaptation that does not require domain alignment.
Networked Multi-Agent Reinforcement Learning with Emergent Communication
One way to coordinate is by learning to communicate with each other.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
These techniques are different for homogeneous and heterogeneous networks because heterogeneous networks can have multiple types of edges and nodes as opposed to a homogeneous network.
Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates
Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii) explain the discovered communities by identifying the relative importance of different covariates in them.
Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by $\approx \mathbf{3-25\%}$ on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.
Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach
In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain.
Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks
To the best of our knowledge, we are the first to explore emergence of communication for discovering and implementing strategies in a setting where agents communicate over a network.
Deep Discriminative Learning for Unsupervised Domain Adaptation
In contrast, we propose an approach that directly addresses the problem of learning a classifier in the unlabeled target domain.
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
With our proposed cross dataset learning procedure we show that one can achieve competitive/better performance than learning from a single dataset.
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks.
A Generative Model for Dynamic Networks with Applications
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i. e. nodes and edges appear and/or disappear over time.
Unsupervised feature learning with discriminative encoder
How can one use the same discriminative models for learning useful features in the absence of labels?
Learning to segment with image-level supervision
In this paper, we propose a model that generates auxiliary labels for each image, while simultaneously forcing the output of the CNN to satisfy the mean-field constraints imposed by a conditional random field.
Generative Adversarial Residual Pairwise Networks for One Shot Learning
Towards overcoming that, we propose a network design inspired by deep residual networks that allows the efficient computation of this more expressive pairwise similarity objective.
Attentive Recurrent Comparators
Rapid learning requires flexible representations to quickly adopt to new evidence.
On Consistency of Compressive Spectral Clustering
Spectral clustering is one of the most popular methods for community detection in graphs.
Discriminative Neural Topic Models
We propose a neural network based approach for learning topics from text and image datasets.
Image Generation and Editing with Variational Info Generative AdversarialNetworks
We evaluate our model on Labeled Faces in the Wild (LFW), celebA and a modified version of MNIST datasets and demonstrate the ability of our model to generate new images as well as to modify a given image by changing attributes.
Deep Variational Inference Without Pixel-Wise Reconstruction
Variational autoencoders (VAEs), that are built upon deep neural networks have emerged as popular generative models in computer vision.
A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference.
Variational methods for Conditional Multimodal Deep Learning
We use the proposed model to generate faces from attributes.
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
This work is motivated by two issues that arise when a hypergraph partitioning approach is used to tackle computer vision problems: (i) The uniform hypergraphs constructed for higher-order learning contain all edges, but most have negligible weights.
On collapsed representation of hierarchical Completely Random Measures
The aim of the paper is to provide an exact approach for generating a Poisson process sampled from a hierarchical CRM, without having to instantiate the infinitely many atoms of the random measures.
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model
Hypergraph partitioning lies at the heart of a number of problems in machine learning and network sciences.
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
Spectral graph partitioning methods have received significant attention from both practitioners and theorists in computer science.
Spectral Clustering with Jensen-type kernels and their multi-point extensions
Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multi-point' kernels, and study their applications.
To go deep or wide in learning?
To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a two-layered supervised learning model, or learn the features directly using a deep (multi-layered) model.
Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions
This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution.
Generative Maximum Entropy Learning for Multiclass Classification
In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature.
On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and Applications
Motivated by the importance of power-law distributions in statistical modeling, in this paper, we propose the notion of power-law kernels to investigate power-laws in learning problem.
