2021-12
Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models
2021-10
Rademacher Random Projections with Tensor Networks
2021-06
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models.
Understanding Capacity Saturation in Incremental Learning
Extracting Weighted Automata for Approximate Minimization in Language Modelling.
2021-03
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
AISTATS 2021
(2021-03-18)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:2010.04003 (2020-10-07)]Tensor Networks for Probabilistic Sequence Modeling
2021-02
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata
2021-01
Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin.
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application.
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
2020-11
Approximate minimization of weighted tree automata
2020-10
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata.
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning.
2020-08
Laplacian Change Point Detection for Dynamic Graphs
Adaptive Learning of Tensor Network Structures
Adaptive Tensor Learning with Tensor Networks
2020-07
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract).
2020-06
Tensorized Random Projections
AISTATS 2020
(2020-06-03)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:2003.05101 (2020-03-11)]2020-03
Tensor Networks for Language Modeling.
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning.
2020-02
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.
Provably efficient reconstruction of policy networks.
2019-11
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
AISTATS 2019
(2019-11-01)
proceedings.mlr.press[LATEST on arXiv preprint arXiv:1911.05010 (2019-11-12)]2019-09
Neural Architecture Search for Class-incremental Learning.
Recognizable series on graphs and hypergraphs
2019-05
On overfitting and asymptotic bias in batch reinforcement learning with partial observability
2018-12
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss.
2018-10
Hierarchical Methods of Moments
2018-07
Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
AISTATS 2018
(2018-07-04)
proceedings.mlr.pressPDF[Also on arXiv preprint arXiv:1807.01406 (2018-07-04)]2018-03
Nonlinear Weighted Finite Automata
2018-02
Sequential Coordination of Deep Models for Learning Visual Arithmetic
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