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07/17/2023
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
Restricting the variance of a policy's return is a popular choice in ris...
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07/11/2023
Attribute Controlled Dialogue Prompting
Prompt-tuning has become an increasingly popular parameter-efficient met...
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12/12/2022
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization
Knowledge Distillation (KD) has been extensively used for natural langua...
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11/27/2022
Label Alignment Regularization for Distribution Shift
Recent work reported the label alignment property in a supervised learni...
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06/30/2022
Learning Functions on Multiple Sets using Multi-Set Transformers
We propose a general deep architecture for learning functions on multipl...
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06/20/2022
Benchmarking Constraint Inference in Inverse Reinforcement Learning
When deploying Reinforcement Learning (RL) agents into a physical system...
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06/20/2022
Robust One Round Federated Learning with Predictive Space Bayesian Inference
Making predictions robust is an important challenge. A separate challeng...
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06/13/2022
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
In typical scenarios where the Federated Learning (FL) framework applies...
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06/02/2022
Learning Soft Constraints From Constrained Expert Demonstrations
Inverse reinforcement learning (IRL) methods assume that the expert data...
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05/27/2022
FedFormer: Contextual Federation with Attention in Reinforcement Learning
A core issue in federated reinforcement learning is defining how to aggr...
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05/25/2022
Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models
Knowledge Distillation (KD) is a prominent neural model compression tech...
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04/15/2022
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation
Knowledge distillation (KD) is an efficient framework for compressing la...
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12/23/2021
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models fo...
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09/21/2021
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation
Intermediate layer knowledge distillation (KD) can improve the standard ...
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09/09/2021
NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge
We propose a score-based DAG structure learning method for time-series d...
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06/07/2021
Quantifying and Improving Transferability in Domain Generalization
Out-of-distribution generalization is one of the key challenges when tra...
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04/17/2021
Robust Embeddings Via Distributions
Despite recent monumental advances in the field, many Natural Language P...
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03/01/2021
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map
While supervised learning is widely used for perception modules in conve...
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12/25/2020
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning
In autonomous driving (AD), accurately predicting changes in the environ...
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06/25/2020
Newton-type Methods for Minimax Optimization
Differential games, in particular two-player sequential games (a.k.a. mi...
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03/08/2020
Complete Hierarchy of Relaxation for Constrained Signomial Positivity
In this article, we prove that the Sums-of-AM/GM Exponential (SAGE) rela...
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03/07/2020
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory
State-of-the-art neural dialogue systems excel at syntactic and semantic...
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02/27/2020
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization
Convergence to a saddle point for convex-concave functions has been stud...
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02/25/2020
Batch norm with entropic regularization turns deterministic autoencoders into generative models
The variational autoencoder is a well defined deep generative model that...
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02/21/2020
Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games
Playing text-based games requires skill in processing natural language a...
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02/06/2020
Multi Type Mean Field Reinforcement Learning
Mean field theory provides an effective way of scaling multiagent reinfo...
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01/28/2020
Unsupervised Multilingual Alignment using Wasserstein Barycenter
We study unsupervised multilingual alignment, the problem of finding wor...
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01/09/2020
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
We present MatrixNets (xNets), a new deep architecture for object detect...
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08/13/2019
Matrix Nets: A New Deep Architecture for Object Detection
We present Matrix Nets (xNets), a new deep architecture for object detec...
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07/11/2019
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that ...
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07/08/2019
Comparing EM with GD in Mixture Models of Two Components
The expectation-maximization (EM) algorithm has been widely used in mini...
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07/06/2019
Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating relat...
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05/27/2019
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
Graphs arise naturally in many real-world applications including social ...
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01/11/2019
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
We introduce SPFlow, an open-source Python library providing a simple in...
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11/01/2018
Progressive Memory Banks for Incremental Domain Adaptation
This paper addresses the problem of incremental domain adaptation (IDA)....
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05/20/2018
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
We present a new technique for deep reinforcement learning that automati...
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04/17/2018
On Improving Deep Reinforcement Learning for POMDPs
Deep Reinforcement Learning (RL) recently emerged as one of the most com...
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12/21/2017
Variational Attention for Sequence-to-Sequence Models
The variational encoder-decoder (VED) encodes source information as a se...
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12/06/2017
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
This paper addresses the question: Why do neural dialog systems generate...
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09/12/2017
Affective Neural Response Generation
Existing neural conversational models process natural language primarily...
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09/01/2017
Order-Planning Neural Text Generation From Structured Data
Generating texts from structured data (e.g., a table) is important for v...
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02/10/2017
Generative Mixture of Networks
A generative model based on training deep architectures is proposed. The...
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01/19/2017
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
Sum-product networks have recently emerged as an attractive representati...
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12/12/2016
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model...
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12/08/2016
Discovering Conversational Dependencies between Messages in Dialogs
We investigate the task of inferring conversational dependencies between...
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09/19/2016
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching
The Gaussian mixture model is a classic technique for clustering and dat...
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01/03/2016
A Unified Approach for Learning the Parameters of Sum-Product Networks
We present a unified approach for learning the parameters of Sum-Product...
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11/13/2015
Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)
Sum-Product Networks (SPN) have recently emerged as a new class of tract...
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04/20/2015
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e.g., word...
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01/06/2015