Multivariate probability calibration is the problem of predicting class membership probabilities
from classification scores of multiple classifiers.
To achieve better performance, the calibrating function is often required to be coordinate-wise non-decreasing;
that is, for every classifier, the higher the score, the higher the probability of the class labeling being positive.
To this end, we propose a multivariate regression method based on shape-restricted Bernstein polynomials.
This method is universally flexible: it can approximate any continuous calibrating function with any specified error,
as the polynomial degree increases to infinite.
Moreover, it is universally consistent: the estimated calibrating function converges to any continuous
calibrating function, as the training size increases to infinity.
Our empirical study shows that the proposed method achieves better calibrating performance than benchmark methods.
**Related:**
IJCAI-20 paper.

Due to the preordering nature of PLP-trees, we define a variant of Kendallâ€™s τ distance metric to be used to
compute distances between PLP-trees for clustering.
To this end, extending the previous work by Li and Kazimipour (Li and Kazimipour 2018), we propose a polynomial
time algorithm PlpDis to compute such distances, and present empirical results comparing it against the brute-force baseline.
Based on PlpDis, we use various distance-based clustering methods to cluster PLP-trees learned from a car evaluation dataset.
Our experiments show that hierarchical agglomerative nesting (AGNES) is the best choice for clustering PLP-trees,
and that the single-linkage variant of AGNES is the best fit for clustering large numbers of trees.
**Related:**
FLAIRS-33 paper.

We design and implement a decision analysis system using human-in-the-loop learning to
learn interpretable predictive decision models (e.g., lexicographic preference trees
and conditional preference networks) to provide insight into agents'
decision making process.
**Related:**
FLAIRS-32 paper.

We designed and developed
a smart multi-modal transportation planner that allows
user-specific metrics (e.g., crime rates and crash
data), to specify constraints as a theory in the linear
temporal logic, and to express preferences as a preferential
cost function.
In the demo, an optimal trip is computed for Alice who doesn't
have a car but has a bike, and she wants to bike at least 1 and at most 2 hours.
Moreover, she prefers biking and public transits over uber.
**Related:**
AAAI Worshop paper.

To facilitate preference learning, we are building a library of various practical preferential datasets useful for conducting preference learning experiments on real-world data.

We introduced the preference formalism of partial lexicographic preference trees, or PLP-trees, over combinatorial domains of alternatives. We study the problem of passive learning, that is, the problem of learning preference models given a set of pairwise preferences between alternatives, called training examples, provided by the user upfront. Specifically, for several classes of PLP-trees, we study how to learn (i) a PLP-tree, preferably of a small size, consistent with a dataset of training examples, and (ii) a PLP-tree correctly ordering as many of the examples as possible in case of inconsistency. Then, we evaluate the predictive power of our model empirically in comparison with other ranking systems in the setting of instance ranking, corresponding to ordinal classification in machine learning.

When candidates are combinations of values
from domains of features, there are just too many of them for humans to
express preferences as strict total orders (or votes) over all candidates.
However, the system of lexicographic preference trees (LP-trees)
often provide compact representations of preferences over combinatorial
domains. Our work focuses on two preference-aggregation problems, the
winner problem and the evaluation problem, based on positional scoring
rules (such as k-approval and Borda) when preferences are represented
as LP-trees. We obtain new computational complexity results of these
two problems and provide empirical analysis in two programming formalisms,
answer set programming (ASP) and weighted partial maximum
satisfiability (WPM).
**Related:**

Preferences over sets can be modeled as weighted propositional formulas. Given a database (the space of possible outcomes), constraints (filtering the database to get the space of feasible outcomes) and preferences (soft constraints indicating personal likings and dislikings), we design and implement a preference reasoning system that automatically produces optimal solutions based on multiple criteria: possibilistic logic, leximin ordering, discrimin ordering and pareto dominance.