Paper introduces a new multi-kernel algorithm for better gradient approximation.
problem Improving gradient approximation in high-dimensional problems.
method Develops a multi-kernel passive stochastic gradient algorithm with variance reduction.
result The multi-kernel algorithm performs better in high-dimensional problems.
New RL method learns from passive data by modeling intentions.
problem Learning from passive data like videos without rewards or actions.
method Model intentions using temporal difference learning, learning representations from raw data.
result Successfully learns features from passive data that accelerate downstream RL tasks.
Active learning can't improve over passive in certain settings.
problem Active learning vs. passive learning in nonparametric settings.
method Analyzing margin conditions and their effects on active learning performance.
result Nuances in margin conditions determine whether active learning can outperform passive learning.
This paper aims to incorporate passive symmetries in machine learning for better generalization.
problem Machine learning's reliance on arbitrary choices leads to passive symmetries that can limit generalization.
method Translation among physics, mathematics, and machine learning to understand and implement passive symmetries.
result Respecting passive symmetries can improve machine learning's ability to generalize.
This paper investigates the market impact of passive orders.
problem Understanding the market impact of passive orders executed through limit orders.
method Developed a microstructure model linking liquidity dynamics and price moves, replacing the constant information content assumption with a function dependent on available volume.
result Derived useful approximations for market impact curves, leading to closed-form formulas.
Model for optimal execution with passive market impact.
problem Optimizing execution strategies in markets with passive price impact.
method Developed a mesoscopic model incorporating empirical price impact features.
result Obtained a passive impact rate that decays exponentially with quote distance.
We study the role of active and passive investors in an investment market with uncertainties. Active investors concentrate on a single or a few stocks with a given probability of determining the quality of them. Passive investors spread their investment uniformly, resembling buying the market index. In this toy market …
Passive investing can incur hidden costs due to market timing inefficiencies.
problem Hidden costs in passive investing due to market timing inefficiencies.
method Analysis of passive investing strategies, including gradual share acquisition and post-announcement trading.
result Post-announcement trading can earn significant profits, often exceeding 1%.
We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any passive learning algorithm can be transformed into an active learning algorithm with asymptotically strictly superior label complexity for all nontrivial targe…
Model predicts active and passive cosponsorship motivations in Congress.
problem Identifying motivations behind cosponsorship in U.S. Congress.
method Encoder+RGCN model learning from bill texts and speeches.
result F1-score of 0.88 for predicting active and passive cosponsorship.
Study shows big winner stocks significantly impact passive and active investment strategies.
problem Impact of big winner stocks on passive and active investment strategies.
method Numerical and analytical techniques applied to historical stock price data.
result Concentrated portfolios underperform equally weighted indexes due to missing big winner stocks.
JIT liquidity providers can sometimes reduce overall market liquidity by crowding out passive LPs.
problem JIT liquidity providers can reduce overall market liquidity by crowding out passive LPs.
method Game-theoretic model with asymmetrically informed agents to analyze JIT liquidity provision in blockchain-based decentralized exchanges.
result JIT LPs only provide liquidity to uninformed orders and crowd out passive LPs when order volume is not sufficiently elastic to pool depth, potentially reducing overall market liquidity.
Finsler space is differentiable manifold for which Minkowski space is the fiber of the tangent bundle. To understand structure of the reference frame in Finsler space, we need to understand the structure of orthonormal basis in Minkowski space. In this paper, we considered the definition of orthonormal basis in Minkows…
Mean representations of VAEs are correlated but still useful for tasks.
problem Correlation between mean and sampled representations of VAEs.
method Selective posterior collapse to identify active and passive variables.
result Passive variables in mean representations are correlated but uncorrelated in sampled ones.
In this paper we study a symmetry group of vector space. Basis manifold is a homogeneous space of a symmetry group. This concept leads us to the definition of active and passive transformations on basis manifold. Active transformation can be expressed as a transformation of vector space. Passive transformation gives ab…
Novel framework for efficient entity alignment labeling.
problem Efficient labeling of entity alignments in knowledge graphs.
method Active and passive learning strategies to select informative instances.
result Passive learning achieves comparable performance to active learning.
The Heston model optimizes portfolio management based on real market data.
problem Choosing between active and passive investment strategies.
method Calibrated Heston model to real stock market data.
result Passive strategy can outperform active strategy depending on market parameters.
We provide new results concerning label efficient, polynomial time, passive and active learning of linear separators. We prove that active learning provides an exponential improvement over PAC (passive) learning of homogeneous linear separators under nearly log-concave distributions. Building on this, we provide a comp…
In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation of the belief. We present a variational principle for the problem of maintaining the information which is most useful for minimizing the cost…
Physics-informed learning framework for pH systems and EB-PBC control.
problem Control of port-Hamiltonian systems from trajectory data.
method Co-learning of pH system model and EB-PBC through alternating optimization.
result Proven stability and robustness of the learned controller.
Paper introduces active and passive causal inference techniques.
problem Causal inference in machine learning.
method Categorizes causal inference techniques into active and passive approaches.
result Describes and discusses various causal inference methods.
Stabilizes online learning by using weighted reservoir sampling.
problem Real-world deployment sensitivity to outliers causes low accuracy in final solutions.
method Weighted reservoir sampling to stabilize ensemble model without additional data passes.
result Risk of ensemble classifier is bounded with respect to the underlying online learning method's regret.
Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for im…
This study proposes an equal-weight portfolio strategy to reduce risk compared to traditional ETFs.
problem Risk of passive ETFs not matching optimal portfolio weights.
method Introduced an equal-weight portfolio strategy to reduce idiosyncratic risk.
result Equal-weight portfolio has lower risk than traditional ETFs, especially during idiosyncratic events.
Algometrics analyzes how predictive models affect their own forecasts in algorithmic markets.
problem How predictive models affect their own forecasts in algorithmic markets.
method Introduces algometrics, a framework for time series with feedback, proving three results on deployment risk.
result Deployment risk cannot be identified from passive historical data alone, and historical rankings can invert under crowding.
FeDXL tackles federated learning for X-risk optimization.
problem Optimizing a family of X-risks with federated learning, where existing algorithms are not applicable.
method Active-passive decomposition framework, federated averaging and merging, novel theoretical analysis.
result FeDXL algorithms for linear and nonlinear f are developed, with established complexities and improved performance. SparseSense improves HAR from sparse sensor data, outperforming state-of-the-art models.
problem Learning activity recognition from highly sparse sensor data streams.
method Set-based neural networks for end-to-end learning from sparse data.
result Significant performance improvements in HAR from passive sensor datasets.
Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well jus…
Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.
problem Learn linear utility functions from pairwise comparison queries.
method Analyzes passive and active learning settings, considering noise-free and noisy query responses.
result Efficient learnability of linear utilities in passive learning, but not for utility parameters without strong assumptions.
New online learning algorithm combines PA and TER for binary classification.
problem Binary classification with non-separable data and data imbalance.
method Online Passive-Aggressive (PA) and Total-Error-Rate (TER) learning combined into PATER algorithm.
result PATER algorithms outperform existing online learning algorithms in efficiency and effectiveness.
Study stability of selective SSMs with discontinuous gating.
problem Challenges in stability analysis of selective SSMs with discontinuous gating.
method Passivity and Input-to-State Stability (ISS) analysis of continuous-time selective SSMs.
result Derivation of sufficient conditions for global ISS with respect to the port input.
Optimal fees protect passive LPs in AMMs under varying market conditions.
problem Adverse selection losses in AMMs are not offset by static trading fees.
method Dynamic reduced-form model with parallel AMM and CEX, large-scale simulations, real market data analysis.
result Optimal AMM fees are stable under normal conditions but high in volatile periods to protect LPs.
Neural networks model COVID-19 spread with partial isolation data.
problem Modeling the spread of COVID-19 with limited data.
method Semi-supervised neural networks solving inverse problems.
result Estimates optimal conditions for virus spread and passive population.
Paper analyzes PSGLD for adaptive IRL with finite-sample bounds.
problem Estimating cost function of a forward learner using noisy gradients.
method Passive stochastic gradient Langevin dynamics (PSGLD) algorithm.
result Explicit bounds on 2-Wasserstein distance between PSGLD sample measure and stationary measure.
Novel active learning algorithm with improved convergence rate under local smoothness condition.
problem Improving convergence rates in active learning under specific smoothness assumptions.
method Developed a novel active learning algorithm with a rate of convergence better than in passive learning, using a local smoothness assumption for k-nearest neighbors.
result The algorithm achieves a better convergence rate than passive learning algorithms, avoiding strong density assumptions.
Recent studies using data on social media and stock markets have mainly focused on predicting stock returns. Instead of predicting stock price movements, we examine the relation between Facebook data and investors' decision making in stock markets with a unique data on investors' transactions on Nokia. We find that the…
Active data collection improves convergence rates in operator learning.
problem Improving convergence rates in operator learning with linear target and stochastic input.
method Active data collection strategies with mean-zero stochastic process and continuous covariance kernels.
result Achieves arbitrarily fast error convergence rates with eigenvalue decay of covariance kernels.
Model A outperforms passive investment in stock index prediction with less exposure.
problem Predicting short-term stock index movements with high accuracy.
method Dynamic Deep Neural Networks (DNN) for trading decisions.
result Model A outperforms passive investment and conventional ML methods.
Improved k-NN active learning with local smoothness assumption.
problem Active learning convergence rates under smoothness assumptions.
method Designing an active learning algorithm with better convergence rate using local smoothness assumption for k-NN.
result Better convergence rate than in passive learning.
In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of…
New algorithm learns Markov decision processes from test data.
problem Learning Markov decision processes from limited test data.
method L*-based active learning with sampling.
result Sampling-based algorithm outperforms passive learning.
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objective is to produce a classifier of relatively low risk, as measured under the 0-1 loss, ideally usin…
This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an…
Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.
problem Estimating reward functions from noisy gradient estimates of stochastic gradient agents.
method Generalized Langevin dynamics algorithm for IRL.
result Proposed algorithms asymptotically generate samples proportional to exp(R(θ)).
Optimizes active and passive portfolio management using latent factors.
problem Combining active and passive portfolio objectives in a semimartingale market model.
method Convex analysis and filtering techniques to find optimal portfolio allocation.
result Closed-form solution for optimal portfolio with proven uniqueness.
We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se…
In this paper, we introduce the first principled adaptive-sampling procedure for learning a convex function in the L∞ norm, a problem that arises often in the behavioral and social sciences. We present a function-specific measure of complexity and use it to prove that, for each convex function f⋆, our …
In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learn…