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

Trend · papers per month

count % of corpus

4 results for “water-filling”

Risk-Based Auto-Deleveragingq-fin.RM

Optimizes cryptocurrency exchanges' risk management by reducing positions based on leverage.

problem Managing risk in cryptocurrency futures exchanges during large price moves.

method Formulates ADL as an optimization problem to minimize risk of loss, using a water-filling rule to equalize leverage.

result The optimal ADL policy minimizes maximum leverage among participants, providing a transparent and implementable benchmark.

cryptocurrency futures risk management auto-deleveraging water-filling rule expected loss

2026-03-16abs ↗pdf ↗

Learning Efficient Representation for Intrinsic Motivationcs.LG

New method calculates empowerment from visual data, solving complex reinforcement learning problems.

problem Calculating empowerment in unknown dynamics from visual observation is challenging.

method Developed a novel approach using stochastic dynamic models in latent space and the Water-Filling algorithm.

result Efficiently computed empowerment in unknown dynamics from visual observation only.

empowerment reinforcement learning information theory latent space water-filling

2019-12-04abs ↗pdf ↗

Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgetsstat.ML

New method tightens federated probe-logit distillation rates under varying bandwidths.

problem Estimating conditional distributions in federated learning with heterogeneous bandwidth constraints.

method Developed a new federated probe-logit distillation (FPLD) method with optimal allocation for varying bandwidths.

result Achieved matching lower and upper bounds for the minimax rate under heterogeneous bandwidths.

federated learning probe-logit distillation heterogeneous bandwidth KL rate optimal allocation

2026-05-28abs ↗pdf ↗

Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedbackcs.LG

Proposes DRRO to mitigate over-optimization in RLHF from human feedback.

problem Over-optimization due to reward misspecification in RLHF.

method Wasserstein distributionally robust regret optimization (DRRO).

result DRRO mitigates over-optimization more effectively than existing baselines.

Reinforcement Learning Human Feedback Distributionally Robust Regret Optimization Over-optimization

2026-04-30abs ↗pdf ↗