Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

Related tags

Deep Learning deep-neural-networks multi-objective-optimization multi-task-learning pareto-optimal-solutions
Overview

Exact Pareto Optimal Search

This repository contains code for all the experiments in the ICML 2020 paper

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization

Video

You can get an intuitive understanding of the algorithm from this video.

Citation

If you find this work useful, please cite our paper.

@incollection{icml2020_3635,
 author = {Mahapatra, Debabrata and Rajan, Vaibhav},
 booktitle = {Proceedings of Machine Learning and Systems 2020},
 pages = {6555--6565},
 title = {Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization},
 year = {2020}
}
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Comments
  • Bump numpy from 1.16.4 to 1.22.0

    Bump numpy from 1.16.4 to 1.22.0

    Bumps numpy from 1.16.4 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump cvxopt from 1.2.3 to 1.2.7

    Bump cvxopt from 1.2.3 to 1.2.7

    Bumps cvxopt from 1.2.3 to 1.2.7.

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    dependencies 
    opened by dependabot[bot] 0
  • why n_tasks * alpha?

    why n_tasks * alpha?

    Hi,

    I noticed that in the experiments, epo_train.py scales alpha (task weight) by the number of tasks but not for the other methods like pmtl_train.py and linscalar_train.py. epo_search.py in toy_experiments also uses a different multiplier x = x - 10. * max(lp.mu_rl, 0.1) * step_size * d_nd.

    Is there any particular reason behind these choices?

    Thanks.

    opened by mlresh 1
Owner
Debabrata Mahapatra
I am a Computer Science PhD student at National University of Singapore
Debabrata Mahapatra
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