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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10 results for “Inverse-weighting”

Inverse-Weighted Survival Gamescs.LG

Innovative game theory approach optimizes survival analysis metrics.

problem Survival analysis models trained with maximum likelihood do not directly optimize criteria like Brier score or Bernoulli log likelihood.

method Inverse-Weighted Survival Games: Construct objectives from re-weighted estimates featuring the other model, holding the latter fixed during training.

result Games optimize Brier score on simulations and real-world data.

Survival analysis Maximum likelihood Brier score Bernoulli log likelihood Inverse-weighting

2021-11-16abs ↗pdf ↗

Stabilized Inverse Probability Weighting via Isotonic Calibrationstat.ME

Proposes stabilized weights for causal inference using isotonic calibration.

problem Stability and bias issues in inverse propensity weighting.

method Post-hoc isotonic calibration of inverse propensity weights.

result Improves performance of doubly robust estimators of average treatment effect.

causal inference inverse propensity weighting isotonic regression stability bias

2024-11-10abs ↗pdf ↗

Achieving Adversarial Robustness via Sparsitycs.LG

Adversarial robustness improved by sparsity in network weights.

problem How sparsity affects adversarial robustness in neural networks.

method Theoretical proof and experimental validation of adversarial pruning methods.

result Weights sparsity improves adversarial robustness, especially through inheritance from smaller networks.

Adversarial robustness Network pruning Sparsity Lottery ticket hypothesis Inverse weights inheritance

2020-09-11abs ↗pdf ↗

Bandit and Delayed Feedback in Online Structured Predictioncs.LG

New algorithms handle online prediction with bandit and delayed feedback, improving regret bounds.

problem Achieving finite bounds on surrogate regret with limited feedback.

method Proposed algorithms for bandit and delayed feedback, including inverse-weighted gradient and pseudo-inverse matrix estimators.

result Achieved improved surrogate regret bounds of

O(\sqrt{KT})

and

O(T^{2/3})

online prediction bandit feedback delayed feedback surrogate regret inverse-weighted gradient

2025-02-26abs ↗pdf ↗

Locally constrained flows and geometric inequalities in spheremath.DG

The paper proves geometric inequalities in sphere using locally constrained flows.

problem Deriving geometric inequalities in sphere.

method Established the longtime existence and convergence of a locally constrained flow.

result Proved new families of three-term geometric inequalities in sphere.

sphere locally constrained flows geometric inequalities three terms inequalities curvature integrals

2024-03-12abs ↗pdf ↗

Learning Adversarial MDPs with Bandit Feedback and Unknown Transitioncs.LG

We consider the problem of learning in episodic finite-horizon Markov decision processes with an unknown transition function, bandit feedback, and adversarial losses. We propose an efficient algorithm that achieves $\mathcal{\tilde{O}}(L|X|\sqrt{|A|T})$ regret with high probability, where $L$ is the horizon, $|X|$ is t…

2019-12-03abs ↗pdf ↗

Active preference learning based on radial basis functionscs.LG

This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as "this is better than that" between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimiz…

2019-09-28abs ↗pdf ↗

Kernel Single-Index Bandits: Estimation, Inference, and Learningstat.ML

Kernelized bandit algorithm tackles adaptive contextual bandits with single-index models.

problem Adaptive contextual bandits with single-index models and unknown link functions.

method Kernelized ε-greedy algorithm combining Stein-based index estimation and kernel ridge regression for reward functions.

result Unified framework for simultaneous learning and inference in single-index contextual bandits.

Kernel bandits Single-index models Adaptive sampling Inference Contextual bandits

2026-03-19abs ↗pdf ↗

Price of Fairness in Bandits: A Tight Minimax Characterizationstat.ML

New algorithm reduces unfairness in bandit problems by balancing exploration and exploitation.

problem Fairness in bandit problems where early participants can be unfairly disadvantaged.

method Introduces extsf{UCB-HARE} algorithm that balances exploration and exploitation using inverse-weighted harmonic rank schedule.

result Algorithm extsf{UCB-HARE} achieves regret matching the lower bound

Ω(σ\sqrt{k^{\max(1,q)}/T})

for

q>1

Fairness Bandit Problems UCB-HARE Regret Minimization Harmonic Rank

2026-07-15abs ↗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 ↗