[Research] Auton Lab meeting: Wednesday April 21 at 12noon

[Artur Dubrawski]

Dear Autonians,

The Lab meeting next week will involve practice talks by Liang and Yi
before their appearances at the upcoming SDM conference.
Abstracts are provided below.
Please come along and provide them with your feedback!

Time: Wednesday April 21, 12noon
Karen will confirm the location.

See you,
Artur

---
Liang:

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor
Factorization

  Real-world relational data are seldom stationary, yet traditional
  collaborative filtering algorithms generally rely on this assumption.
  Motivated by our sales prediction problem, we propose a factor-based 
algorithm
  that is able to take time into account. By introducing additional 
factors for
  time, we formalize this problem as a tensor factorization with a special
  constraint on the time dimension. Further, we provide a fully Bayesian
  treatment to avoid tuning parameters and achieve automatic model 
complexity
  control. To learn the model we develop an efficient sampling procedure 
that is
  capable of analysing large-scale data sets. This new algorithm, called
  Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on
  several real-world problems including sales prediction and movie
  recommendation. Empirical results demonstrate the superiority of our 
temporal
  model.

---
Yi:
Learning Compressible Models.

In this paper, we study the combination of compression and L1-norm 
regularization in a machine learning context: learning compressible 
models. By including a compression operation into the L-1 
regularization, the assumption on model sparsity is relaxed to 
compressibility: model coefficients are compressed before being 
penalized, and sparsity is achieved in a compressed domain rather than 
the original space. We focus on the design of different compression 
operations, by which we can encode various compressibility assumptions 
and inductive biases, e.g., piecewise local smoothness, compacted energy 
in the frequency domain, and semantic correlation. We show that use of a 
compression operation provides an opportunity to leverage auxiliary 
information from various sources, e.g., domain knowledge, coding 
theories, unlabeled data. We conduct extensive experiments on 
brain-computer interfacing, handwritten character recognition and text 
classification. Empirical results show clear improvements in prediction 
performance by including compression in L-1 regularization. We also 
analyze the learned model coefficients under appropriate compressibility 
assumptions, which further demonstrate the advantages of learning 
compressible models instead of sparse models.