Max Lin
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UINav: A Practical Approach to Train On-Device Automation Agents
Wei Li
Fu-Lin Hsu
Will Bishop
Folawiyo Campbell-Ajala
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024) - Industry Track
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Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation costs. We propose UINav, a demonstration-based approach to train automation agents that fit mobile devices, yet achieving high success rates with modest numbers of demonstrations. To reduce the demonstration overhead, UINav, uses a referee model that provides users with immediate feedback on tasks where the agent fails, and automatically augments human demonstrations to increase diversity in training data. Our evaluation shows that with only 10 demonstrations UINav, can achieve 70% accuracy, and that with enough demonstrations it can surpass 90% accuracy.
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SpreadsheetCoder: Formula Prediction from Semi-structured Context
Rishabh Singh
Proceedings of the 38th International Conference on Machine Learning (ICML) (2021)
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Spreadsheet formula prediction has been an important
program synthesis problem with many
real-world applications. Previous works typically
utilize input-output examples as the specification
for spreadsheet formula synthesis, where each
input-output pair simulates a separate row in the
spreadsheet. However, this formulation does not
fully capture the rich context in real-world spreadsheets.
First, spreadsheet data entries are organized
as tables, thus rows and columns are not necessarily
independent from each other. In addition,
many spreadsheet tables include headers, which
provide high-level descriptions of the cell data.
However, previous synthesis approaches do not
consider headers as part of the specification. In
this work, we present the first approach for synthesizing
spreadsheet formulas from tabular context,
which includes both headers and semi-structured
tabular data. In particular, we propose SpreadsheetCoder,
a BERT-based model architecture
to represent the tabular context in both row-based
and column-based formats. We train our model on
a large dataset of spreadsheets, and demonstrate
that SpreadsheetCoder achieves top-1 prediction
accuracy of 42:51%, which is a considerable
improvement over baselines that do not employ
rich tabular context. Compared to a rule-based
system, SpreadsheetCoder assists 82% more
users in composing formulas on Google Sheets.
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Due to its simplicity and desirable incentive aspects, the second-price auction is the most prevalent auction format used by advertising exchanges. However, even with the optimized choice of the reserve prices, this auction is not optimal when the bidders are heterogeneous, i.e., when the bidder valuation distributions differ significantly. We propose a new auction format called the boosted second-price auction, which favors bidders with lower inverse hazard rates (IHRs), roughly speaking, bidders with more stable bidding behavior. Based on our analysis of auction data from Google’s advertising exchange, we found bidders to be heterogeneous and can be ordered based on their IHRs. In this paper, we theoretically analyze and describe how our proposed boosted second-price auctions increase revenue over that of the widely used second-price auctions by favoring bidders with lower IHRs. We also provide practical guidelines for determining boost values and validate these guidelines both theoretically and empirically. Our counterfactuals, based on actual transaction data, show that boosted second-price auctions that follow our guidelines perform almost optimally and obtain up to 3% more revenue than second-price auctions.
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Dynamic Revenue Sharing
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA
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Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction α of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c/(1 − α ) for each item, but it is not the optimal solution on maximizing the profit of the intermediary.
While previous studies (by Mirrokni and Gomes, and by Niazadeh et al) focused on revenue-sharing schemes in static double auctions, in this paper, we take advantage of the repeated nature of the auctions, and present solutions based on dynamic mechanism design. In particular, we introduce dynamic revenue sharing schemes where we balance the two constraints over different auctions to achieve higher profit. This is directly motivated by the practice of advertising exchanges where the fixed-percentage revenue-share should be met across all auctions and not in each auction. In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation. Finally, we empirically evaluate our revenue sharing scheme on real
data.
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