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We study the convergence properties of a pair of learning algorithms (learning with and without memory). This leads us to study the dominant eigenvalue of a class of random matrices. This turns out to be related to the roots of the…

Probability · Mathematics 2007-05-23 Natalia Komarova , Igor Rivin

Polynomial factorization in conventional sense is an ill-posed problem due to its discontinuity with respect to coefficient perturbations, making it a challenge for numerical computation using empirical data. As a regularization, this paper…

Numerical Analysis · Mathematics 2021-03-09 Wenyuan Wu , Zhonggang Zeng

We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties,…

Machine Learning · Computer Science 2024-01-19 Carlo D'Eramo , Davide Tateo , Andrea Bonarini , Marcello Restelli , Jan Peters

We study the statistical-computational trade-offs for learning with exact invariances (or symmetries) using kernel regression. Traditional methods, such as data augmentation, group averaging, canonicalization, and frame-averaging, either…

Machine Learning · Computer Science 2026-02-05 Ashkan Soleymani , Behrooz Tahmasebi , Stefanie Jegelka , Patrick Jaillet

Standard approaches to probabilistic reasoning require that one possesses an explicit model of the distribution in question. But, the empirical learning of models of probability distributions from partial observations is a problem for which…

Artificial Intelligence · Computer Science 2018-07-02 Brendan Juba

We study the problem of fair and efficient allocation of a set of indivisible chores to agents with additive cost functions. We consider the popular fairness notion of envy-freeness up to one good (EF1) with the efficiency notion of…

Computer Science and Game Theory · Computer Science 2021-10-20 Jugal Garg , Aniket Murhekar , John Qin

Over-parameterized deep neural networks are able to achieve excellent training accuracy while maintaining a small generalization error. It has also been found that they are able to fit arbitrary labels, and this behaviour is referred to as…

Machine Learning · Computer Science 2021-12-17 Futong Liu , Tao Lin , Martin Jaggi

A polynomial-time algorithm for computing the permanent in any field of characteristic 3 is presented in this article. The principal objects utilized for that purpose are the Cauchy and Vandermonde matrices, the discriminant function and…

Computational Complexity · Computer Science 2007-08-28 Vadim Tarin

In this paper, we are interested in memoryless computation, a modern paradigm to compute functions which generalises the famous XOR swap algorithm to exchange the contents of two variables without using a buffer. This uses a combinatorial…

Computational Complexity · Computer Science 2015-03-19 Maximilien Gadouleau , Soren Riis

We investigate the minimal number of failures that can partition a system where processes communicate both through shared memory and by message passing. We prove that this number precisely captures the resilience that can be achieved by…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-12-22 Hagit Attiya , Sweta Kumari , Noa Schiller

We study the problem of dividing indivisible chores among agents whose costs (for the chores) are supermodular set functions with binary marginals. Such functions capture complementarity among chores, i.e., they constitute an expressive…

Computer Science and Game Theory · Computer Science 2023-02-23 Siddharth Barman , Vishnu V. Narayan , Paritosh Verma

We study fair allocation of indivisible public goods subject to cardinality (budget) constraints. In this model, we have n agents and m available public goods, and we want to select $k \leq m$ goods in a fair and efficient manner. We first…

Computer Science and Game Theory · Computer Science 2021-07-22 Jugal Garg , Pooja Kulkarni , Aniket Murhekar

Reasoning under uncertainty is a fundamental challenge in Artificial Intelligence. As with most of these challenges, there is a harsh dilemma between the expressive power of the language used, and the tractability of the computational…

Artificial Intelligence · Computer Science 2025-05-08 Luise Ge , Brendan Juba , Kris Nilsson

Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$. In posterior sampling, one is also given a measurement model $p(y \mid x)$ and a measurement $y$, and would like to sample from $p(x \mid…

Machine Learning · Computer Science 2025-11-11 Shivam Gupta , Ajil Jalal , Aditya Parulekar , Eric Price , Zhiyang Xun

Replay methods are known to be successful at mitigating catastrophic forgetting in continual learning scenarios despite having limited access to historical data. However, storing historical data is cheap in many real-world settings, yet…

Machine Learning · Computer Science 2023-11-22 Marcus Klasson , Hedvig Kjellström , Cheng Zhang

The $\lambda$-calculus is a handy formalism to specify the evaluation of higher-order programs. It is not very handy, however, when one interprets the specification as an execution mechanism, because terms can grow exponentially with the…

Logic in Computer Science · Computer Science 2019-07-16 Andrea Condoluci , Beniamino Accattoli , Claudio Sacerdoti Coen

The following questions are often encountered in system and control theory. Given an algebraic model of a physical process, which variables can be, in theory, deduced from the input-output behavior of an experiment? How many of the…

Optimization and Control · Mathematics 2025-10-20 Alexandre Sedoglavic

This is the first part of a series of articles where we are going to develop theory of valuations on manifolds generalizing the classical theory of continuous valuations on convex subsets of a linear space. In this article we still work…

Metric Geometry · Mathematics 2011-11-16 Semyon Alesker

Polynomial interpretations are a useful technique for proving termination of term rewrite systems. They come in various flavors: polynomial interpretations with real, rational and integer coefficients. As to their relationship with respect…

Logic in Computer Science · Computer Science 2015-07-01 Friedrich Neurauter , Aart Middeldorp

Many transfer problems require re-using previously optimal decisions for solving new tasks, which suggests the need for learning algorithms that can modify the mechanisms for choosing certain actions independently of those for choosing…

Machine Learning · Computer Science 2021-07-22 Michael Chang , Sidhant Kaushik , Sergey Levine , Thomas L. Griffiths