On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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7 results for “cross-learning”

Multi-task Supervised Learning via Cross-learningcs.LG

Cross-learning improves multi-task learning performance.

problem Improving multi-task learning across different domains.

method Coupling parameters across tasks with a stochastic projected gradient algorithm and primal-dual approach.

result Cross-learned functions outperform task-specific and consensus approaches in image classification.

multi-task learning cross-learning stochastic gradient primal-dual image classification

2020-10-24abs ↗pdf ↗

Contextual Bandits with Cross-learningcs.LG

In the classical contextual bandits problem, in each round $t$ , a learner observes some context $c$ , chooses some action $i$ to perform, and receives some reward $r_{i,t}(c)$ . We consider the variant of this problem where in addition to receiving the reward $r_{i,t}(c)$ , the learner also learns the values of $r_{i,t}(c…

2018-09-25abs ↗pdf ↗

Quantifying Data Similarity Using Cross Learningstat.ML

CLS measures dataset similarity through decision rule performance.

problem Measuring dataset similarity in machine learning, especially for transfer learning and domain adaptation.

method Cross-Learning Score (CLS) measures similarity through bidirectional generalization performance of decision rules, linking to cosine similarity under canonical linear models.

result CLS effectively measures dataset similarity and transferability, validated on synthetic and real-world datasets.

2025-10-13abs ↗pdf ↗

Optimal cross-learning for contextual bandits with unknown context distributionscs.LG

Optimal algorithm for contextual bandits with unknown context distributions.

problem Designing efficient algorithms for contextual bandits with unknown context distributions.

method Cross-learning setting, novel technique for coordinating multiple epochs.

result Nearly tight regret bound of

\widetilde{O}(\sqrt{TK})

for learning to bid in first-price auctions and sleeping bandits.

contextual bandits unknown context distributions regret bounds first-price auctions sleeping bandits

2024-01-03abs ↗pdf ↗

Forecast combinations: an over 50-year reviewstat.ME

This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.

problem Improving forecast accuracy through combining multiple forecasts.

method Evolution of forecast combination methods, from simple to sophisticated.

result Forecast combinations have become a mainstream approach in forecasting.

forecast combinations time-varying weights nonlinear combinations cross-learning probabilistic forecasts

2022-05-09abs ↗pdf ↗

Learning Product Rankings Robust to Fake Userscs.LG

Study tackles ranking fraud in online platforms by learning robust rankings.

problem Fraudulent fake users manipulate product rankings.

method Developed algorithms for robust ranking in two informational environments.

result Our algorithms converge to optimal rankings, robust to fake users.

ranking fraud online platforms product ordering fake users robust learning

2020-09-10abs ↗pdf ↗

Optimal No-regret Learning in Repeated First-price Auctionscs.LG

Algorithm learns to bid optimally in repeated first-price auctions with censored feedback.

problem Learning to bid optimally in repeated first-price auctions with incomplete feedback.

method Developed an algorithm exploiting the specific feedback structure and payoff function of first-price auctions.

result Achieved a near-optimal

\widetilde{O}(\sqrt{T})

regret bound for first-price auctions.

Online Learning First-price Auctions No-regret Algorithms Censored Feedback Cumulative Payoff

2020-03-22abs ↗pdf ↗