14 papers · ranked by Valyu relevance
Harrison Grodin, Robert Harper
Amortized analysis is a cost analysis technique for data structures in which cost is studied in aggregate, rather than considering the maximum cost of a single operation. Traditionally, amortized analysis has been phrased inductively, in terms of finite sequences of operations. Connecting to prior work on coalgebraic…
Harrison Grodin, Robert Harper
Amortized analysis is a program cost analysis technique for data structures in which the cost of operations is specified in aggregate, under the assumption of continued sequential use. Typically, amortized analyses are presented inductively, in terms of finite sequences of operations. We give an alternative coinductive…
Joseph W. Cutler, Daniel R. Licata, Norman Danner
A typical way of analyzing the time complexity of functional programs is to extract a recurrence expressing the running time of the program in terms of the size of its input, and then to solve the recurrence to obtain a big-O bound. For recurrence extraction to be compositional, it is also necessary to extract…
David Kahn, Jan Hoffmann
Automatic amortized resource analysis (AARA) is a typebased technique for inferring concrete (non-asymptotic) bounds on a program's resource usage. Existing work on AARA has focused on bounds that are polynomial in the sizes of the inputs. This paper presents and extension of AARA to exponential bounds that preserves…
Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska + 2 more
Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. Here, the aim is to find a distribution over the parameters that describe the whole population using a model that can generate simulations for an…
Ishita Dasgupta, Eric Schulz, Noah D. Goodman, Samuel J. Gershman
Bayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a…
Tianhan Lu, Bor-Yuh Evan Chang, Ashutosh Trivedi
We consider the problem of automatically proving resource bounds. That is, we study how to prove that an integer-valued resource variable is bounded by a given program expression. Automatic resourcebound analysis has recently received significant attention because of a number of important applications (e.g., detecting…
Ishita Dasgupta, Eric Schulz, Noah D. Goodman, Samuel J. Gershman
Bayesian models of cognition posit that people compute probability distributions over hypotheses, possibly by constructing a sample-based approximation. Since people encounter many closely related distributions, a computationally efficient strategy is to selectively reuse computations – either the samples themselves or…
Ian Covert, Chanwoo Kim, Su‐In Lee, James Zou + 1 more
Data Attribution Authors: ['Ian Covert' 'Chanwoo Kim' 'Su‐In Lee' 'James Zou' 'Tatsunori Hashimoto'] Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and are intractable for large datasets. These methods require efficient…
Zijian Wang, Jan Hasenauer, Yannik Schälte
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies…
Alexander Tscshantz, Beren Millidge, Anil K. Seth, Christopher L. Buckley + 1 more
'Christopher L. Buckley' 'Ulrik R. Beierholm'] Predictive coding is an influential model of cortical neural activity. It proposes that perceptual beliefs are furnished by sequentially minimising “prediction errors”-the differences between predicted and observed data. Implicit in this proposal is the idea that…
Tijn de Vos, Aleksander Christiansen
Tree-packings - collections of spanning trees of a graph - are a fundamental tool in the study of minimum cut and related graph parameters. They have played a central role in the design of algorithms across static, dynamic, and distributed settings. In this paper, we study both tree-packings themselves and their…
Zijian Wang, Jan Hasenauer, Yannik Schälte, James R. Faeder
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies…
Gian Marco Visani, William Galvin, Zac Jones, Michael N. Pun + 4 more
Accurately predicting how amino acid substitutions alter protein function is a central challenge in biology, with applications from interpreting disease variants to designing vaccines and therapeutics. We introduce HERMES, a family of fast, structure-based models that predict mutational effects from the local atomic…