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Found 33 papers, 16 papers with code
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
The main challenge in this estimation task is the potential confounding of treatment assignment with an individual's covariates in the training data, whereas during inference ICTE requires prediction on independently sampled treatments.
Generator Assisted Mixture of Experts For Feature Acquisition in Batch
However, sequential acquisition is not feasible in some settings where time is of the essence.
Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus.
Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks
A common consideration for scoring similarity is the maximum common subgraph (MCS) between the query and corpus graphs, usually counting the number of common edges (i. e., MCES).
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
We do not draw the concave function from a restricted family, but rather learn from data using a highly expressive neural network that implements a differentiable quadrature procedure.
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes
In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences.
Learning to Select Exogenous Events for Marked Temporal Point Process
Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a wide variety of applications which are characterized using discreteevents localized in continuous time.
Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control
Owing to the growth of interest in Reinforcement Learning in the last few years, gradient based policy control methods have been gaining popularity for Control problems as well.
Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy
In recent years, inductive graph embedding models, \emph{viz.
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.
Counterfactual Explanations in Sequential Decision Making Under Uncertainty
In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time.
Group Testing under Superspreading Dynamics
Testing is recommended for all close contacts of confirmed COVID-19 patients.
Training Data Subset Selection for Regression with Controlled Generalization Error
First, we represent this problem with simplified constraints using the dual of the original training problem and show that the objective of this new representation is a monotone and alpha-submodular function, for a wide variety of modeling choices.
Differentiable Learning Under Triage
However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood.
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework.
Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach
The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc.
Long Horizon Forecasting With Temporal Point Processes
In recent years, marked temporal point processes (MTPPs) have emerged as a powerful modeling machinery to characterize asynchronous events in a wide variety of applications.
Adversarial Permutation Guided Node Representations for Link Prediction
Sequence encoders are more expressive, but are permutation sensitive by design.
Classification Under Human Assistance
Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes
Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.
Regression Under Human Assistance
In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.
Can A User Anticipate What Her Followers Want?
As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback.
Differentially Private Link Prediction With Protected Connections
Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage.
Consequential Ranking Algorithms and Long-term Welfare
Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones?
Stochastic Optimal Control of Epidemic Processes in Networks
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.
Deep Reinforcement Learning of Marked Temporal Point Processes
In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes.
On the Complexity of Opinions and Online Discussions
Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.
Steering Social Activity: A Stochastic Optimal Control Point Of View
At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users.
NeVAE: A Deep Generative Model for Molecular Graphs
Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates.
Optimizing Human Learning
Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?
Cheshire: An Online Algorithm for Activity Maximization in Social Networks
Can we design an algorithm that finds when to incentivize users to take actions to maximize the overall activity in a social network?
Discriminative Link Prediction using Local Links, Node Features and Community Structure
Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.
