Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Joglekar, Manas"'
Autor:
Burns, Collin, Izmailov, Pavel, Kirchner, Jan Hendrik, Baker, Bowen, Gao, Leo, Aschenbrenner, Leopold, Chen, Yining, Ecoffet, Adrien, Joglekar, Manas, Leike, Jan, Sutskever, Ilya, Wu, Jeff
Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outpu
Externí odkaz:
http://arxiv.org/abs/2312.09390
Recommendation problems with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep neural networks are being increasingly used for these recommendation problems. These models use embed
Externí odkaz:
http://arxiv.org/abs/1907.04471
We study the problem of finding and monitoring fixed-size subgraphs in a continually changing large-scale graph. We present the first approach that (i) performs worst-case optimal computation and communication, (ii) maintains a total memory footprint
Externí odkaz:
http://arxiv.org/abs/1802.03760
Autor:
Joglekar, Manas, Rekatsinas, Theodoros, Garcia-Molina, Hector, Parameswaran, Aditya, Ré, Christopher
We focus on data fusion, i.e., the problem of unifying conflicting data from data sources into a single representation by estimating the source accuracies. We propose SLiMFast, a framework that expresses data fusion as a statistical learning problem
Externí odkaz:
http://arxiv.org/abs/1512.06474
We study a class of aggregate-join queries with multiple aggregation operators evaluated over annotated relations. We show that straightforward extensions of standard multiway join algorithms and generalized hypertree decompositions (GHDs) provide be
Externí odkaz:
http://arxiv.org/abs/1508.07532
Autor:
Joglekar, Manas, Re, Christopher
We optimize multiway equijoins on relational tables using degree information. We give a new bound that uses degree information to more tightly bound the maximum output size of a query. On real data, our bound on the number of triangles in a social ne
Externí odkaz:
http://arxiv.org/abs/1508.01239
We present {\em smart drill-down}, an operator for interactively exploring a relational table to discover and summarize "interesting" groups of tuples. Each group of tuples is described by a {\em rule}. For instance, the rule $(a, b, \star, 1000)$ te
Externí odkaz:
http://arxiv.org/abs/1412.0364
Worker quality control is a crucial aspect of crowdsourcing systems; typically occupying a large fraction of the time and money invested on crowdsourcing. In this work, we devise techniques to generate confidence intervals for worker error rate estim
Externí odkaz:
http://arxiv.org/abs/1411.6562
Evaluating workers is a critical aspect of any crowdsourcing system. In this paper, we devise techniques for evaluating workers by finding confidence intervals on their error rates. Unlike prior work, we focus on "conciseness"---that is, giving as ti
Externí odkaz:
http://arxiv.org/abs/1411.3377
User Defined Function(UDFs) are used increasingly to augment query languages with extra, application dependent functionality. Selection queries involving UDF predicates tend to be expensive, either in terms of monetary cost or latency. In this paper,
Externí odkaz:
http://arxiv.org/abs/1411.3374