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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.

168,695 papers · 148 categories

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23477093 · May 202619922001200920172026
48 results for affine feedback

This paper extends stock trading results to include stop-loss orders.

problem Generalizing stock trading results with stop-loss orders.
method Geometric Brownian motion model, affine feedback controller, closed-form expression for cumulative distribution function.
result Affine feedback controller with stop-loss order generalizes results without stop-loss orders.

Paper presents a new flat triangular form for systems.

problem Creating a structurally flat triangular form for systems.
method Developed a new triangular form based on the extended chained form with conditions for static feedback equivalence.
result Provided conditions for affine input systems to be static feedback equivalent to the new triangular form.

New algorithm mitigates affinity bias in hiring feedback loops.

problem Mitigating affinity bias in hiring decisions to avoid unconscious favoritism.
method Introducing affinity bandits, a new bandit variant that accounts for evolving biased feedback.
result Elimination-style algorithm nearly matches the derived regret bound, outperforming classical algorithms.

A control system q˙=f(q,u)\dot{q} = f(q,u) is said to be trivializable if there exists local coordinates in which the system is feedback equivalent to a control system of the form q˙=f(u)\dot{q} = f(u). In this paper we characterize trivializable control systems and control systems for which, up to a feedback transformation, ff a…

2009-02-13abs ↗pdf ↗

Algorithm provides online learning guarantees against general comparators in full and bandit feedback.

problem Adversarial online learning with data-dependent regret guarantees.
method Completely online algorithm with data-dependent regret guarantees for full and bandit feedback.
result Algorithm achieves expected performance against arbitrary comparator sequences in full and bandit feedback settings.

New algorithm minimizes expert selection regret in partial bandit feedback.

problem Minimizing expert selection regret in partial bandit feedback.
method Develops a sequential minimax optimal algorithm for a generalized partial monitoring setting.
result Second order regret bounds against a general expert selection sequence.

New algorithm solves Schrödinger bridge problem with mismatched channels.

problem Solving Schrödinger bridge problem with input and noise channel mismatch.
method Design of a Sinkhorn recursion with memory for nonlinear PDEs.
result Demonstrates solving control-affine Schrödinger bridge problem.

Investigates mean-variance portfolio selection in non-Markovian markets.

problem Continuous-time Markowitz mean-variance portfolio selection in fake stationary affine Volterra models.
method Stochastic factor solution to a Riccati BSDE, deriving explicit solutions as multi-dimensional Riccati-Volterra equations.
result Analytical closed-form expressions for optimal portfolio policies and mean-variance efficient frontier.

Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by trans…

2018-09-10abs ↗pdf ↗

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.

problem Optimal control from bilinear observations in linear systems is challenging.
method Analytical and numerical methods to study the non-convex cost-to-go and non-affine optimal controllers.
result The Separation Principle does not hold for bilinear observations, leading to non-convex costs and non-affine optimal controllers.

The paper explores how ReLU DNNs can represent MPC policies and vice versa.

problem Representing MPC policies as ReLU DNNs and vice versa.
method Developed an approximate method for identifying input-space in ReLU nets resulting in PWA functions over polyhedral regions. Studied inverse multiparametric linear or quadratic programs for reconstruction of constraints and cost functions given a PWA function.
result Identification and representation of MPC policies as ReLU DNNs and vice versa.

New insights into cascade feedback linearization of control systems.

problem Obtaining a cascade feedback linearization for invariant control systems.
method Introducing truncated versions of operators from the calculus of variations to prove new theorems.
result Established new geometry and foundational theorems for future work.

New method learns from either positive or negative feedback alone.

problem Limited applicability of existing preference optimization methods in scenarios with only unpaired feedback.
method Decouples learning from positive and negative feedback, using expectation-maximization (EM) to optimize probability of positive outcomes and explicitly incorporate negative examples.
result Stable learning from negative feedback alone demonstrated.

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is oft…

2011-09-27abs ↗pdf ↗

This paper improves image retrieval accuracy through novel relevance feedback methods.

problem Improving image retrieval accuracy in Content-Based Image Retrieval (CBIR).
method Novel addition to feature re-weighting and classification techniques, focusing on 0-th iteration improvement.
result Significantly improved retrieval accuracy from relevance feedback.

Develops a new model for RLHF accounting for partially observed states and intermediate feedback.

problem Lack of models for partially observed states and intermediate feedback in RLHF.
method PORRL model with cardinal and dueling feedback methods.
result Demonstrates improved learning and alignment with new model-based and model-free methods.

Classifier learns to ignore unreliable feedback from end users.

problem Improving classifier performance by filtering unreliable feedback.
method Modeling end users as autonomous agents, periodically retraining classifier with filtered feedback.
result Classifier can identify and filter out unreliable feedback, improving performance.

Advocates a local feedback approach for RL in unknown systems.

problem Finding optimal feedback laws in unknown nonlinear dynamical systems.
method Searches over a local feedback representation consisting of an open-loop sequence and an optimal linear feedback law.
result Results in highly efficient training and superior performance compared to global methods.

A method corrects feedback shift in predicting conversion rates with delayed feedback.

problem Delayed feedback leads to mislabeling of positive instances in training data.
method Uses importance weight approach to correct feedback shift.
result Proposed method outperforms existing methods in offline and online experiments.

Study how communication and feedback graphs affect learning outcomes.

problem Understanding the impact of feedback graphs on cooperative online learning.
method Analyzed network regret in terms of the independence number of the strong product of communication and feedback graphs.
result Proved bounds for network regret and demonstrated the non-improvable nature of positive results in pathological cases.

New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.

problem Reducing regret in parameterized bandits with correlated auxiliary feedback.
method Develops a reward estimator using auxiliary feedback with tight confidence bounds.
result Shows significant reduction in regret compared to standard methods.

The problem of feedback equivalence for control systems is considered. An algebra of differential invariants and criteria for the feedback equivalence for regular control systems are found.

2008-12-07abs ↗pdf ↗

Study of reinforcement learning with additional feedback observations.

problem Episodic reinforcement learning in Markov decision processes with feedback observations.
method Formalization of feedback graph, model-based algorithms leveraging feedback, regret bound analysis.
result Regret bound depends only on the size of the maximum acyclic subgraph of the feedback graph.

CausalRM models rewards from user feedback, overcoming noise and bias.

problem Aligning language models with user preferences from noisy, biased feedback.
method Causal-theoretic reward modeling framework addressing noise and bias in observational feedback.
result CausalRM learns accurate reward signals from noisy and biased observational feedback.

Paper tackles noisy bandit feedback for multiclass classification.

problem Learning multiclass classifier with corrupted feedback.
method Proposes an unbiased estimator technique to estimate noise rates and an end-to-end framework.
result Algorithm achieves mistake bounds of O(T)O(\sqrt{T}) in high noise and O(Ticefrac23)O(T^{ icefrac{2}{3}}) in worst case.

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial…

2018-03-29abs ↗pdf ↗

Active learning framework for optimizing human preferences in reinforcement learning.

problem Selecting most informative feedback for training models of human preferences.
method Proposes an active learning framework to collect preferential feedback online or offline.
result Errors in DPO logit estimates diminish with more feedback.

Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.

problem Improving decision-making in contexts with uncertain cost coefficients using partial feedback.
method Unified class of offline learning algorithms for CLO with different types of feedback, using IERM framework.
result Fast-rate regret bound for IERM with partial feedback and misspecified model classes.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

2017-09-12abs ↗pdf ↗