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Found 18 papers, 4 papers with code
Charting the Shapes of Stories with Game Theory
Stories are records of our experiences and their analysis reveals insights into the nature of being human.
Swim till You Sink: Computing the Limit of a Game
During 2023, two interesting results were proven about the limit behavior of game dynamics: First, it was shown that there is a game for which no dynamics converges to the Nash equilibria.
Online Stackelberg Optimization via Nonlinear Control
In repeated interaction problems with adaptive agents, our objective often requires anticipating and optimizing over the space of possible agent responses.
Masked Generative Story Transformer with Character Guidance and Caption Augmentation
Story Visualization (SV) is a challenging generative vision task, that requires both visual quality and consistency between different frames in generated image sequences.
On Limitations of the Transformer Architecture
What are the root causes of hallucinations in large language models (LLMs)?
The complexity of non-stationary reinforcement learning
The problem of continual learning in the domain of reinforcement learning, often called non-stationary reinforcement learning, has been identified as an important challenge to the application of reinforcement learning.
Neuroscience needs Network Science
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
Memory Bounds for Continual Learning
We make novel uses of communication complexity to establish that any continual learner, even an improper one, needs memory that grows linearly with $k$, strongly suggesting that the problem is intractable.
Nash, Conley, and Computation: Impossibility and Incompleteness in Game Dynamics
We also prove a stronger result for $\epsilon$-approximate Nash equilibria: there are games such that no game dynamics can converge (in an appropriate sense) to $\epsilon$-Nash equilibria, and in fact the set of such games has positive measure.
Implementing Permutations in the Brain and SVO Frequencies of Languages
More generally, we also formulate and algorithmically solve the problems of implementing a permutation of the leaves of any binary tree, and of selecting the permutation that is easiest to implement on a given binary tree.
Self-Attention Networks Can Process Bounded Hierarchical Languages
Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as $\mathsf{Dyck}_k$, the language consisting of well-nested parentheses of $k$ types.
Online Stochastic Max-Weight Bipartite Matching: Beyond Prophet Inequalities
This is the best possible ratio for this problem, as it generalizes the original single-item prophet inequality problem.
Data Structures and Algorithms
Learning with Plasticity Rules: Generalization and Robustness
Here we hypothesize that (a) Brains employ synaptic plasticity rules that serve as proxies for GD; (b) These rules themselves can be learned by GD on the rule parameters; and (c) This process may be a missing ingredient for the development of ANNs that generalize well and are robust to adversarial perturbations.
The Platform Design Problem
The Designer may design a platform for each of these activities/states; if a platform is adopted by the Agent, the transition probabilities of the Markov chain are affected, and so is the objective of the Agent.
A New Age of Computing and the Brain
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
α-Rank: Multi-Agent Evaluation by Evolution
We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).
Cycles in adversarial regularized learning
Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science.
Strategic Classification
Jury designs a classifier, and Contestant receives an input to the classifier, which he may change at some cost.
