Search Results for author:
Found 26 papers, 3 papers with code
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
Human communication often involves information gaps between the interlocutors.
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input.
Abstractive Text Summarization
Natural Language Inference
+2
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
Active Learning with Label Comparisons
We show that there is a natural class where this approach is sub-optimal, and that there is a more comparison-efficient active learning scheme.
Flood forecasting with machine learning models in an operational framework
During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287, 000 km2, home to more than 350M people.
Solving Sokoban with forward-backward reinforcement learning
Despite seminal advances in reinforcement learning in recent years, many domains where the rewards are sparse, e. g. given only at task completion, remain quite challenging.
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image.
Net-DNF: Effective Deep Modeling of Tabular Data
A challenging open question in deep learning is how to handle tabular data.
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm.
HydroNets: Leveraging River Structure for Hydrologic Modeling
In this work we present a novel family of hydrologic models, called HydroNets, which leverages river network structure.
DNF-Net: A Neural Architecture for Tabular Data
A challenging open question in deep learning is how to handle tabular data.
Convex Nonparanormal Regression
Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics.
Globally Optimal Learning for Structured Elliptical Losses
The core of the difficulty is the non-convexity of the objective function, implying that standard optimization algorithms may converge to sub-optimal critical points.
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Given a deep neural network (DNN) for a classification problem, an application of MAD optimization results in MadNet, a version of the original network, now equipped with an adversarial defense mechanism.
Spectral Algorithm for Low-rank Multitask Regression
In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model.
Improved Detection of Adversarial Attacks via Penetration Distortion Maximization
This paper is concerned with the defense of deep models against adversarial at- tacks.
ML for Flood Forecasting at Scale
We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.
Learning Rules-First Classifiers
Complex classifiers may exhibit "embarassing" failures in cases where humans can easily provide a justified classification.
Scalable Learning of Non-Decomposable Objectives
Modern retrieval systems are often driven by an underlying machine learning model.
Speedy Model Selection (SMS) for Copula Models
We tackle the challenge of efficiently learning the structure of expressive multivariate real-valued densities of copula graphical models.
Learning Max-Margin Tree Predictors
We demonstrate the effectiveness of our approach on several domains and show that, despite the relative simplicity of the structure, prediction accuracy is competitive with a fully connected model that is computationally costly at prediction time.
Nonparanormal Belief Propagation (NPNBP)
Importantly, the method is as efficient as standard Gaussian BP, and its convergence properties do not depend on the complexity of the univariate marginals, even when a nonparametric representation is used.
Copula Bayesian Networks
We present the Copula Bayesian Network model for representing multivariate continuous distributions.
Learning Bounded Treewidth Bayesian Networks
In this work we present a novel method for learning Bayesian networks of bounded treewidth that employs global structure modifications and that is polynomial in the size of the graph and the treewidth bound.
