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09/13/2023
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Markov processes are widely used mathematical models for describing dyna...
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02/02/2023
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulat...
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11/09/2021
DistIR: An Intermediate Representation and Simulator for Efficient Neural Network Distribution
The rapidly growing size of deep neural network (DNN) models and dataset...
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06/07/2021
An Information-theoretic Approach to Distribution Shifts
Safely deploying machine learning models to the real world is often a ch...
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01/18/2021
Regularized Policies are Reward Robust
Entropic regularization of policies in Reinforcement Learning (RL) is a ...
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06/15/2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
We analyze the influence of adversarial training on the loss landscape o...
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03/15/2019
On Certifying Non-uniform Bound against Adversarial Attacks
This work studies the robustness certification problem of neural network...
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01/17/2019
Hierarchical Representations with Poincaré Variational Auto-Encoders
The Variational Auto-Encoder (VAE) model has become widely popular as a ...
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05/29/2018
Depth and nonlinearity induce implicit exploration for RL
The question of how to explore, i.e., take actions with uncertain outcom...
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05/27/2017
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
New types of machine learning hardware in development and entering the m...
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05/24/2017
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
We would like to learn a representation of the data which decomposes an ...
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02/10/2017
Batch Policy Gradient Methods for Improving Neural Conversation Models
We study reinforcement learning of chatbots with recurrent neural networ...
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11/07/2016
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
We propose the Gaussian attention model for content-based neural memory ...
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06/02/2016
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Generative neural samplers are probabilistic models that implement sampl...
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09/06/2015
Theoretical and Experimental Analyses of Tensor-Based Regression and Classification
We theoretically and experimentally investigate tensor-based regression ...
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03/18/2015
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
We consider the problem of recovering a low-rank tensor from its noisy o...
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03/05/2015
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Similarity between objects is multi-faceted and it can be easier for hum...
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02/27/2015
Norm-Based Capacity Control in Neural Networks
We investigate the capacity, convexity and characterization of a general...
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12/20/2014
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
We present experiments demonstrating that some other form of capacity co...
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07/07/2014
Spectral norm of random tensors
We show that the spectral norm of a random n_1× n_2×...× n_K tensor (or ...
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03/26/2013
Convex Tensor Decomposition via Structured Schatten Norm Regularization
We discuss structured Schatten norms for tensor decomposition that inclu...
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11/17/2012
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
We present a novel algebraic combinatorial view on low-rank matrix compl...
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06/27/2012
A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion
In this paper, we review the problem of matrix completion and expose its...
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10/13/2011
Discovering Emerging Topics in Social Streams via Link Anomaly Detection
Detection of emerging topics are now receiving renewed interest motivate...
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03/27/2011
Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization
We theoretically investigate the convergence rate and support consistenc...
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03/02/2011
Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization
We investigate the learning rate of multiple kernel leaning (MKL) with e...
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11/13/2010
Regularization Strategies and Empirical Bayesian Learning for MKL
Multiple kernel learning (MKL), structured sparsity, and multi-task lear...
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10/05/2010
Estimation of low-rank tensors via convex optimization
In this paper, we propose three approaches for the estimation of the Tuc...
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01/15/2010
Sparsity-accuracy trade-off in MKL
We empirically investigate the best trade-off between sparse and uniform...
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12/12/2009
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in ...
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11/20/2009
Super-Linear Convergence of Dual Augmented-Lagrangian Algorithm for Sparsity Regularized Estimation
We analyze the convergence behaviour of a recently proposed algorithm fo...
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09/28/2009