Search Results for author: Sanjeeb Dash

Found 9 papers, 2 papers with code

Interpretable and Fair Boolean Rule Sets via Column Generation

no code implementations16 Nov 2021 Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.

Fairness

LPRules: Rule Induction in Knowledge Graphs Using Linear Programming

no code implementations15 Oct 2021 Sanjeeb Dash, Joao Goncalves

A major drawback of such methods is the lack of scalability to large datasets.

Knowledge Graphs

AI Descartes: Combining Data and Theory for Derivable Scientific Discovery

no code implementations3 Sep 2021 Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler Josephson, Joao Goncalves, Kenneth Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh

We develop a method for combining logical reasoning with symbolic regression, enabling principled derivations of models of natural phenomena.

Automated Theorem Proving

Integer Programming for Causal Structure Learning in the Presence of Latent Variables

1 code implementation5 Feb 2021 Rui Chen, Sanjeeb Dash, Tian Gao

The problem of finding an ancestral acyclic directed mixed graph (ADMG) that represents the causal relationships between a set of variables is an important area of research on causal inference.

Causal Inference

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping

1 code implementation NeurIPS 2020 Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk

We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.

Classification General Classification

Symbolic Regression using Mixed-Integer Nonlinear Optimization

no code implementations11 Jun 2020 Vernon Austel, Cristina Cornelio, Sanjeeb Dash, Joao Goncalves, Lior Horesh, Tyler Josephson, Nimrod Megiddo

The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of operators, is a hard problem in machine learning, both theoretically and computationally.

Generalized Linear Rule Models

no code implementations5 Jun 2019 Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük

Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one.

General Classification

Boolean Decision Rules via Column Generation

no code implementations NeurIPS 2018 Sanjeeb Dash, Oktay Günlük, Dennis Wei

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.

General Classification

Globally Optimal Symbolic Regression

no code implementations29 Oct 2017 Vernon Austel, Sanjeeb Dash, Oktay Gunluk, Lior Horesh, Leo Liberti, Giacomo Nannicini, Baruch Schieber

In this study we introduce a new technique for symbolic regression that guarantees global optimality.

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