Paper introduces a new framework for MAB processes with arm switch restrictions.
problem Optimizing decisions among multiple options with switching constraints.
method Develops a Gittins index process for MAB with arm switch restrictions.
result Establishes optimality of Gittins index policies for the new framework.
This work addresses identifiability in sequential data with switching dynamics, introducing a new estimator.
problem Identifiability of sequential data with regime-switching dynamics under flexible assumptions.
method Introduces ΩSDS, a flow-based estimator for exact likelihood optimization. result Demonstrates improved disentanglement and more accurate forecasting compared to VAE-based estimators.
Adaptive Bayesian Optimization for resource-constrained experiments with switching costs.
problem Sequential experimental design with varying costs for changing design variables.
method Adapted batch algorithms to sequential problem, proposing cost-aware and cost-ignorant methods.
result Cost-aware algorithm outperforms tuned process-constrained algorithms in all settings considered.
This paper studies the problem of optimally extracting nonrenewable natural resource in light of various financial and economic restrictions and constraints. Taking into account the fact that the market values of the main natural resources i.e. oil, natural gas, copper,...,etc, fluctuate randomly following global and s…
New algorithms tackle adversarial combinatorial bandits with switching costs.
problem Adversarial combinatorial bandits with switching costs.
method Design algorithms operating in batches to restrict switches, proving lower bounds and achieving upper bounds on regret.
result Achieved upper bounds on regret for both bandit and semi-bandit feedback settings.
New regularization method corrects over-shrinkage in small data regression.
problem Over-shrinkage in small data regression leading to underfitting.
method Negative-capable ridge family that permits negative regularization.
result Negative regularization acts as controlled anti-shrinkage, increasing effective complexity.
New algorithm reduces switching costs in multinomial logit bandit problems.
problem Minimizing switching costs in multinomial logit bandit problems.
method Proposed AT-DUCB and FH-DUCB algorithms with low assortment switching costs.
result AT-DUCB and FH-DUCB algorithms achieve almost optimal minimax regret with low switching costs.
The paper provides global optimization algorithms for two particularly difficult nonconvex problems raised by hybrid system identification: switching linear regression and bounded-error estimation. While most works focus on local optimization heuristics without global optimality guarantees or with guarantees valid only…
New polynomial invariants derived from birack and switch structures.
problem Polynomial invariants of braids.
method Switch structures, birack colorings, quiver-valued invariants.
result New polynomial invariants of braids.
In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …
Survey reviews code-switched speech and language processing.
problem Processing code-switched text and speech for multilingual communities.
method Reviews computational approaches and lists available resources.
result Essential for building intelligent agents that interact in multilingual settings.
This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.
problem Identifying latent variables in sequential data models.
method Proved identifiability of Markov Switching Models and established conditions for Switching Dynamical Systems.
result Identifiability of latent variables and non-linear mappings in Switching Dynamical Systems up to affine transformations.
The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…
Study on revenue management with limited switches, achieving strong performance and reduced switch counts.
problem Resource-constrained dynamic pricing with limited switching constraints.
method Developed algorithms for blind network revenue management and bandits with knapsacks, achieving optimal regret rates.
result Optimal regret rates are fully characterized by a piecewise-constant function of the switching budget and resource constraints.
New algorithms improve sampling from complex distributions.
problem Sampling from complex probability distributions efficiently.
method Regime-switching Langevin dynamics and Monte Carlo algorithms.
result Convergence guarantees and iteration complexities provided.
Squirrel switches between optimizers for better performance.
problem Finding the best optimizer for a given problem.
method Switches between different optimizers based on performance.
result Improves performance on various problems.
Study approximates financial market with discrete-time models.
problem Approximating continuous-time financial market models with discrete-time.
method Constructs discrete-time market models with Markov switching and proves convergence.
result Discrete-time models converge to continuous-time Black-Scholes model with Markov switching.
Optimizes control of hybrid systems with multiple switching processes.
problem Optimal control of hybrid systems with multiple Markov switching processes.
method Combines two separate Markov chains into one synthetic chain, derives HJB equations, and solves the portfolio choice problem.
result Derives explicit solutions and value functions for the optimal control problem.
Study tackles balancing policy switching costs in offline RL.
problem Balancing the cost of policy switching in offline RL.
method Optimal transport ideas and Net Actor-Critic algorithm.
result Demonstrated efficiency on multiple RL benchmarks.
New algorithm learns switching dynamics from multiple neural signals.
problem Learning accurate switching dynamical system models from multimodal neural data.
method Unsupervised learning algorithm for multiscale switching dynamical system models.
result Switching multiscale dynamical system models outperform single-scale models in behavior decoding.
Paper tackles utility maximization with job-switching and retirement constraints.
problem Maximizing utility with job-switching and retirement constraints.
method Dual-martingale approach and double obstacle problem theory.
result Characterization of optimal job-switching strategy and wealth boundaries.
Solves label switching in mixture models using optimal transport.
problem Label switching in mixture model posterior inference prevents meaningful statistics assessment.
method Proposes an algorithm leveraging optimal transport to compute posterior statistics in a quotient space.
result Demonstrates advantages over alternative approaches on simulated and real data.
New RL algorithm reduces policy switching cost to loglog(T) with similar regret.
problem Low policy switching cost in real-life RL applications.
method Stage-wise exploration and adaptive policy elimination.
result Regret of O(HSAloglogT) with O(HSAloglogT) switching cost. The paper explores dynamic regret with switching cost in online decision making.
problem The relation between dynamic regret and switching cost in online decision making.
method Investigates two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). Provides a new theoretical analysis framework.
result The switching cost impacts dynamic regret differently in OA and has no impact in OCO.
Study on adaptivity constraints in linear contextual bandits with optimal design.
problem Impact of adaptivity constraints on linear contextual bandits.
method Two models of limited adaptivity: batch learning and rare policy switches. Proposed distributional optimal design.
result Achieves minimax-optimal regret with optimal number of policy switches and batches.
A new network learns market conditions and predicts stock performance.
problem Optimizing stock portfolio performance in the US equities market.
method Residual Switching Network combining two ResNets: a switching module and a main module.
result The residual switching network strategy outperformed other models with an average annual Sharpe ratio of 2.22.
This paper investigates the effects of the "uptick rule" (a short selling regulation formally known as rule 10a-1) by means of a simple stock market model, based on the ARED (adaptive rational equilibrium dynamics) modeling framework, where heterogeneous and adaptive beliefs on the future prices of a risky asset were f…
Paper analyzes minimax regret in constrained online convex optimization with limited switching opportunities.
problem Minimizing regret in online convex optimization with limited switching opportunities.
method Introduced fugal game relaxation and mini-batching algorithm to establish minimax regret bounds.
result Minimax regret of switching-constrained OCO is Θ(T / √K).
Paper presents an efficient algorithm for linear MDP with low switching cost.
problem Large state space reinforcement learning problems with low switching cost.
method First algorithm for linear MDP with low switching cost, achieving near-optimal regret and switching cost.
result Regret bound of $\widetilde{O}\left(\sqrt{d^3H^4K}
ight)$ and near-optimal switching cost of $O\left(d H\log K
ight)$.
Solves risk-aware optimal switching problems in discrete time.
problem Non-Markovian optimal switching problems with risk awareness and general filtration.
method Solves reflected backward stochastic difference equations.
result Existence and uniqueness of solutions for the problems.
Study online learning with feedback graphs and switching costs, providing algorithms and optimal regret bounds.
problem Online learning with partial feedback and switching costs.
method Analysis of feedback graphs, lower bound on expected regret, new algorithms (Threshold Based EXP3, EXP3. SC).
result Order optimal algorithms for specific cases and Threshold Based EXP3 outperforms in empirical evaluations.
Study strategic competition in commodity markets using impulse-switching controls.
problem Strategic competition between upstream and downstream firms in commodity markets.
method Non-zero-sum stochastic differential game with mixed impulse/switching controls.
result Multiple Nash equilibria found, depending on the number of switches by the downstream firm.
One type of switch simplifies operations on lattice knots.
problem Operations on lattice knots are complex.
method Reduced operations to one type of local switch.
result Simplified set of operations on lattice knots.
Optimal switching regret for all segmentations in online convex optimisation.
problem Non-stationary online convex optimisation problems.
method Developed an efficient algorithm to achieve optimal switching regret on every possible segmentation.
result Achieved asymptotically optimal switching regret on every possible segmentation simultaneously.
New Q-Learning algorithm reduces switching cost in MDPs.
problem Reducing adaptivity in real-world applications.
method Q-Learning with UCB2 exploration, quantified by local switching cost.
result Achieves sublinear regret with low switching cost.
This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.
problem Adversarial RL with switching costs, where loss distribution can be non-stationary or adversarial.
method Developed novel switching-reduced algorithms with matching lower bounds for known and unknown transition functions.
result Achieved near-optimal performance in adversarial RL with switching costs, matching theoretical lower bounds.
The paper provides guarantees for learning switching non-linear systems from a single trajectory.
problem Learning non-linear dynamical systems with switching dynamics.
method Non-asymptotic bounds derived under stability assumptions for i.i.d. switching modes.
result Explicit convergence rates for Hölder and linear function classes based on effective sample size.
This paper addresses parameter estimation for wave equations with Markovian switching.
problem Parameter estimation for wave equations with abrupt changes.
method Bayesian statistical framework using discrete sparse Bayesian learning.
result Strong performance in parameter estimation for variable coefficient PDEs.
Algorithm for bandits with switching costs achieves optimal regret bounds.
problem Optimal regret bounds for stochastic and adversarial bandits with switching costs.
method Adaptation of Tsallis-INF algorithm with no prior knowledge of regime or time horizon.
result Achieves minimax optimal regret bounds in various settings.
In this paper, we derive the family switching formula of -n two-sphere fiber bundle embedded in a smooth four-manifold fiber bundle. In the smooth category, it is a partial generalization of Fintushel-Stern's argument for four-manifolds. We also derive an algebraic analogue of the family switching formula, allowing the…
Audit fees change based on company and economic factors during auditor switching.
problem Understanding how audit fees change when auditors switch firms.
method Examined the impact of auditor switching on audit fees, considering company characteristics and economic data.
result The direction and magnitude of audit fee changes during switching depend on economic stability and company characteristics.
Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. …
Study on bandit problems with switching constraints, revealing phase transitions in regret.
problem Stochastic multi-armed bandit problem with switching cost constraints.
method Proved matching upper and lower bounds on optimal regret, provided efficient algorithms.
result Phase transitions in optimal regret rate with respect to switching budget.
New invariant for virtual links using multi-switches and algebraic systems.
problem Creating invariants for virtual links.
method Introducing a general approach to construct invariant algebraic systems from multi-switches and virtual links.
result Introduces a new quandle invariant for virtual links.
LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.
problem Optimizing systems with costly variable updates in a sequence of modules.
method Lazy Modular Bayesian Optimization (LaMBO) that minimizes switching costs.
result LaMBO achieves vanishing regret and improves over existing cost-aware Bayesian optimization algorithms.
Optimal futures trading strategy in a changing market model.
problem Dynamic trading in a regime-switching market.
method Utility maximization approach with HJB equations reduced to linear ODEs.
result Optimal futures positions and portfolio value across market regimes.
New algorithm reduces switching costs in RL beyond linear MDPs.
problem Costly policy switching in reinforcement learning.
method ELEANOR-LowSwitching algorithm for linear Bellman-complete MDPs.
result Achieves near-optimal regret with logarithmic switching cost.
Develops identifiability theory for multi-lag regime-switching models.
problem Ensuring interpretability of deep latent variable models with multi-lag dependencies.
method Formulates a general theoretical framework for multi-lag Regime-Switching Models (RSMs), proving identifiability of number of regimes and multi-lag transitions.
result Establishes identifiability conditions for multi-lag regime-switching models, including Markov Switching Models and Switching Dynamical Systems.