740 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Agnostic proper learning of monotone functions: beyond the black-box correction barrier
We give the first agnostic, efficient, proper learning algorithm for monotone
Boolean functions. Given uniformly random
examples of an unknown function , our
algorithm outputs a hypothesis that is
monotone and -close to , where
is the distance from to the closest monotone function. The running time of
the algorithm (and consequently the size and evaluation time of the hypothesis)
is also , nearly matching the lower bound
of Blais et al (RANDOM '15). We also give an algorithm for estimating up to
additive error the distance of an unknown function to
monotone using a run-time of . Previously,
for both of these problems, sample-efficient algorithms were known, but these
algorithms were not run-time efficient. Our work thus closes this gap in our
knowledge between the run-time and sample complexity.
This work builds upon the improper learning algorithm of Bshouty and Tamon
(JACM '96) and the proper semiagnostic learning algorithm of Lange, Rubinfeld,
and Vasilyan (FOCS '22), which obtains a non-monotone Boolean-valued
hypothesis, then ``corrects'' it to monotone using query-efficient local
computation algorithms on graphs. This black-box correction approach can
achieve no error better than
information-theoretically; we bypass this barrier by
a) augmenting the improper learner with a convex optimization step, and
b) learning and correcting a real-valued function before rounding its values
to Boolean.
Our real-valued correction algorithm solves the ``poset sorting'' problem of
[LRV22] for functions over general posets with non-Boolean labels
Trivial Automorphisms of Reduced Products
We introduce a general method for showing under weak forcing axioms that
reduced products of countable models of a theory have as few automorphisms
as possible. We show that such forcing axioms imply that reduced products of
countably infinite or finite fields, linear orders, trees, or random graphs
have only trivial automorphisms.Comment: 36 page
Proper conflict-free list-coloring, odd minors, subdivisions, and layered treewidth
Proper conflict-free coloring is an intermediate notion between proper
coloring of a graph and proper coloring of its square. It is a proper coloring
such that for every non-isolated vertex, there exists a color appearing exactly
once in its (open) neighborhood. Typical examples of graphs with large proper
conflict-free chromatic number include graphs with large chromatic number and
bipartite graphs isomorphic to the -subdivision of graphs with large
chromatic number. In this paper, we prove two rough converse statements that
hold even in the list-coloring setting. The first is for sparse graphs: for
every graph , there exists an integer such that every graph with no
subdivision of is (properly) conflict-free -choosable. The second
applies to dense graphs: every graph with large conflict-free choice number
either contains a large complete graph as an odd minor or contains a bipartite
induced subgraph that has large conflict-free choice number. These give two
incomparable (partial) answers of a question of Caro, Petru\v{s}evski and
\v{S}krekovski. We also prove quantitatively better bounds for minor-closed
families, implying some known results about proper conflict-free coloring and
odd coloring in the literature. Moreover, we prove that every graph with
layered treewidth at most is (properly) conflict-free -choosable.
This result applies to -planar graphs, which are graphs whose coloring
problems have attracted attention recently.Comment: Hickingbotham recently independently announced a paper
(arXiv:2203.10402) proving a result similar to the ones in this paper. Please
see the notes at the end of this paper for details. v2: add results for odd
minors, which applies to graphs with unbounded degeneracy, and change the
title of the pape
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
GPT Semantic Networking: A Dream of the Semantic Web – The Time is Now
The book presents research and practical implementations related to natural
language processing (NLP) technologies based on the concept of artificial
intelligence, generative AI, and the concept of Complex Networks aimed at creating
Semantic Networks.
The main principles of NLP, training models on large volumes of text data, new
universal and multi-purpose language processing systems are presented. It is shown
how the combination of NLP and Semantic Networks technologies opens up new
horizons for text analysis, context understanding, the formation of domain models,
causal networks, etc. This book presents methods for creating Semantic Networks
based on prompt engineering. Practices are presented that will help build semantic
networks capable of solving complex problems and making revolutionary changes in
the analytical activity.
The publication is intended for those who are going to use large language
models for the construction and analysis of semantic networks in order to solve
applied problems, in particular, in the field of decision making.У книзі представлені дослідження та практичні реалізації технологій обробки природної мови (НЛП), заснованих на концепції штучного
інтелект, генеративний ШІ та концепція складних мереж, спрямована на створення семантичних мереж. Представлено основні принципи НЛП, моделі навчання на великих обсягах текстових даних, нові універсальні та багатоцільові системи обробки мови. Показано, як поєднання технологій NLP і семантичних мереж відкриває нові горизонти для аналізу тексту, розуміння контексту, формування моделей домену, причинно-наслідкових мереж тощо. У цій книзі представлені методи створення семантичних мереж
на основі оперативного проектування. Представлені практики, які допоможуть побудувати семантичні мережі, здатні вирішувати складні проблеми та вносити революційні зміни в аналітичну діяльність. Видання розраховане на тих, хто збирається використовувати велику мову
моделі побудови та аналізу семантичних мереж з метою вирішення прикладних задач, зокрема, у сфері прийняття рішень
Disinformation and Fact-Checking in Contemporary Society
Funded by the European Media and Information Fund and research project PID2022-142755OB-I00
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Asymptotics for Palette Sparsification
It is shown that the following holds for each . For an
-vertex graph of maximum degree and "lists" () chosen
independently and uniformly from the ()-subsets of , with probability tending to 1 as .
This is an asymptotically optimal version of a recent "palette
sparsification" theorem of Assadi, Chen, and Khanna.Comment: 29 page
- …