Search Results for author: Karl Krauth

Found 9 papers, 2 papers with code

On component interactions in two-stage recommender systems

no code implementations28 Jun 2021 Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus

Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest.

Recommendation Systems

The Stereotyping Problem in Collaboratively Filtered Recommender Systems

no code implementations23 Jun 2021 Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg

First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users.

Collaborative Filtering Recommendation Systems

The Effect of Natural Distribution Shift on Question Answering Models

no code implementations ICML 2020 John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data.

Question Answering

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

no code implementations NeurIPS 2019 Karl Krauth, Stephen Tu, Benjamin Recht

We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand the limits of reinforcement learning (RL) algorithms on continuous control tasks.

Continuous Control

Cloud Programming Simplified: A Berkeley View on Serverless Computing

no code implementations9 Feb 2019 Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E. Gonzalez, Raluca Ada Popa, Ion Stoica, David A. Patterson

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud.

Operating Systems

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

no code implementations18 Oct 2016 Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood.

General Classification

Generic Inference in Latent Gaussian Process Models

1 code implementation2 Sep 2016 Edwin V. Bonilla, Karl Krauth, Amir Dezfouli

We evaluate our approach quantitatively and qualitatively with experiments on small datasets, medium-scale datasets and large datasets, showing its competitiveness under different likelihood models and sparsity levels.

General Classification Stochastic Optimization

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