Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives. This short-paper proposes PODNet, Plannable Option Discovery Network, addressing how to segment an unstructured set of demonstrated traject…
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48 results for “Option Discovery”
MO2 learns useful behaviours from past experience for new tasks.
problem Discovering useful behaviours from past experience and transferring them to new tasks.
method Model-Based Offline Options (MO2) framework supporting sample-efficient bottleneck option discovery over continuous state-action spaces.
result MO2 outperforms recent option learning methods on complex long-horizon continuous control tasks.
The options framework in reinforcement learning models the notion of a skill or a temporally extended sequence of actions. The discovery of a reusable set of skills has typically entailed building options, that navigate to bottleneck states. This work adopts a complementary approach, where we attempt to discover option…
New method learns temporal abstractions by defining interest functions.
problem Learning temporal abstractions with limited, variable durations.
method Introduced interest functions to define initiation sets, enabling gradient-based learning.
result Demonstrated effectiveness in discrete and continuous environments.
Bayesian nonparametrics enables dynamic skill discovery from expert data.
problem Fixed K for offline skill discovery in reinforcement learning.
method Variational inference, continuous relaxations, Bayesian nonparametrics.
result Nonparametric model with dynamically-changing number of options.
Novel online algorithm for hierarchical imitation learning.
problem Scalability issue in reinforcement learning and options discovery.
method Online Baum-Welch algorithm for hierarchical imitation learning.
result The online algorithm outperforms the batch version in both discrete and continuous environments.
EDL discovers state-covering skills without relying on task rewards.
problem Discovering skills in reinforcement learning without a task-oriented reward function.
method EDL optimizes information-theoretic objective using different machinery to address coverage problem.
result EDL discovers state-covering skills more effectively than existing methods.
We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data. We construct practical estimators specific to the high-dimensional regime. Inspired by the ICP algorithm, we use an optional pr…
The paper learns robot skills from demonstrations without supervision.
problem Discovering robotic options from unlabelled demonstrations.
method Temporal variational inference for latent variable learning.
result The framework can learn options across multiple datasets.
Hierarchical reinforcement learning deals with the problem of breaking down large tasks into meaningful sub-tasks. Autonomous discovery of these sub-tasks has remained a challenging problem. We propose a novel method of learning sub-tasks by combining paradigms of routing in computer networks and graph based skill disc…
Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a large number of options or skills, which can potentially be represented succinctly by filtering out any redundant information. Such a reduc…
Python toolbox for causal structure learning from data.
problem Causal structure learning from data.
method Generates data from simulators or real-world datasets, learns causal structure, evaluates graphs, and includes gradient-based methods.
result Convenience and efficiency in causal discovery with GPU acceleration.
A derivation of the Black-Scholes option pricing model using a central limit theorem argumentq-fin.GN
The Black-Scholes model (sometimes known as the Black-Scholes-Merton model) gives a theoretical estimate for the price of European options. The price evolution under this model is described by the Black-Scholes formula, one of the most well-known formulas in mathematical finance. For their discovery, Merton and Scholes…
Broadens Jourdain and Martini's method to non-linear stochastic processes.
problem Applying pricing methods to non-linear stochastic processes.
method Analyzes from probabilistic and analytic viewpoints, extending Jourdain and Martini's method.
result Broadens applicability of pricing methods to non-linear frameworks.
Eigenoptions improve credit assignment in reinforcement learning.
problem Improving credit assignment in reinforcement learning models.
method Investigated eigenoptions for credit assignment in model-free RL, comparing pre-specified and online discovery methods.
result Pre-specified eigenoptions aid exploration and credit assignment, while online discovery can hinder learning.
Generative AI improves stock selection by synthesizing features from diverse data sources.
problem Automating feature discovery in stock market data.
method Used large language models with retrieval-augmented generation and structured prompting to synthesize features from various data sources.
result AI-generated features consistently outperform baselines, with Sharpe improvements ranging from 14% to 91%.
New method for learning causal relationships in PNL models.
problem Learning causal relationships from empirical observations in PNL models.
method Rank-based methods to estimate non-linear functions, disentangling from independence tests.
result Consistent method for PNL causal discovery, validated in experiments.
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic control framework (Gregor et.al., 2016) which maximizes empowerment -- the ability to reliably reach a diverse set of states and show how to ide…
Derives token price process for AMM tokens, finds leverage effect and pricing discrepancies.
problem Derives token price process for AMM tokens.
method Derives CEV process for token price, derives closed-form option prices, introduces liquidity-adjusted Greeks.
result Token price process is CEV, with leverage effect and pricing discrepancies.
We utilize the symmetric thermal optimal path (TOPS) method to examine the dynamic interaction patterns between the VIX and VIX futures markets. We document that the VIX dominates the VIX futures more in the first few years, especially before the introduction of VIX options. We further observe that the TOPS paths show …
We model continuous-time information flows generated by a number of information sources that switch on and off at random times. By modulating a multi-dimensional Lévy random bridge over a random point field, our framework relates the discovery of relevant new information sources to jumps in conditional expectation mart…
New method discovers context effects in choice data.
problem Identifying context effects from choice data is challenging.
method Automatic discovery of context effects from observed choices.
result Automatic discovery of context effects from observed choices.
Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. The information-theoretic principle of empowerment formalizes an unsupervised exploration objective through an agent trying to maximize its influence on the future states of its environme…
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawingcs.LG
New method learns high-quality Laplacian representations for reinforcement learning.
problem Lack of accurate Laplacian representations in large or continuous state spaces.
method Reformulated spectral graph drawing objective to have eigenvectors as unique global minimizer.
result Learned Laplacian representations more faithfully approximate the ground truth.
New method recovers BSDE from financial data without ergodicity.
problem Discovering probabilistic laws from financial data.
method Stochastic SINDy method under risk-neutral measure.
result Recovery of BSDE from limited financial data.
Differentiable causal discovery methods perform robustly under model violations.
problem Causal discovery algorithms struggle with real-world data due to unverifiable causal assumptions.
method Benchmarked differentiable causal discovery methods under eight model assumption violations.
result Differentiable causal discovery methods exhibit robust performance under Structural Hamming Distance and Structural Intervention Distance metrics.
New framework uses background knowledge to speed up causal discovery.
problem Scalable causal discovery for large datasets.
method Utilizes background knowledge during causal discovery process.
result Background knowledge reduces computational requirements and improves structure quality.
New method prevents invalid inference after causal discovery.
problem Invalid inference after causal discovery.
method Developed tools for valid post-causal-discovery inference.
result Our method provides reliable coverage while achieving more accurate causal discovery.
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairsstat.ML
LDP speeds up causal discovery by partitioning, improving VAS recall and runtime.
problem Hard causal discovery in nonparametric settings with exponential complexity.
method Local Discovery by Partitioning (LDP) for causal inference around exposure-outcome pairs.
result LDP yields less biased and more precise estimates than baseline methods.
Proposes LLM-DCD for improved causal discovery from data.
problem Challenges in discovering causal relationships from observational data.
method Uses LLM to initialize DCD optimization, incorporating priors.
result Higher accuracy on benchmark datasets compared to state-of-the-art.
Probabilistic grammars improve equation discovery from data.
problem Discovering scientific laws from data using equations.
method Proposed probabilistic context-free grammars to encode soft constraints and a Monte-Carlo algorithm.
result Probabilistic grammars lead to more efficient equation discovery.
Causal discovery improves fMRI analysis, but faces challenges.
problem Challenges in applying causal discovery to fMRI data.
method Identifying and addressing nine challenges in fMRI causal discovery.
result Current methods for fMRI causal discovery need improvement.
New methods for Markov Blanket discovery using MML outperform existing approaches.
problem Causal discovery from large datasets.
method Developed three new methods of Markov Blanket discovery using Minimum Message Length.
result Our best MML method is consistently competitive and has advantageous features.
Framework separates chemical and structural contributions to aqueous solubility.
problem Merging chemical and structural information in solubility models obscures their relative importance.
method Additive MLP-GNN framework with separate chemical and structural branches.
result Framework reveals distinct roles of chemical and structural information in solubility.
L2D-CD learns to defer expert recommendations in causal discovery.
problem Combining expert knowledge with data-driven results in causal discovery when expert recommendations may contradict data.
method Adapting learning-to-defer algorithms for pairwise causal discovery, L2D-CD learns a deferral function to select between expert recommendations and data-driven methods.
result L2D-CD outperforms both causal discovery methods and the expert used in isolation, identifying domains where the expert's performance is strong or weak.
Review of automation's role in chemical discovery, emphasizing future challenges.
problem Improving automation's contribution to chemical discovery.
method Analysis of exemplary studies and open research directions.
result Future autonomous systems need improvement in data handling, model building, and experiment automation.
New method controls false discoveries in financial asset pricing.
problem Controlling false discoveries in time series with unknown correlations.
method Double bootstrapping method to control false discovery rate.
result Superior statistical power and controlled false discovery rate.
Interpretable ML helps discover insights from big data.
problem Validating data-driven discoveries from complex datasets.
method Statistical and machine learning techniques for interpretable models.
result Challenges in validating data-driven discoveries remain.
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
KEEL improves causal discovery with fuzzy knowledge and complex data.
problem Challenges in causal discovery due to prior knowledge, domain inconsistencies, and small sample sizes.
method Weakly-supervised fuzzy knowledge and data co-driven causal discovery method (KEEL).
result KEEL outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.
This paper provides fast estimates for complex option types.
problem Estimating prices for constrained multiple exercise American options.
method Lookahead search for lower estimates and nearest-neighbor martingale for upper estimates.
result Probabilistic convergence guarantees for the algorithms.
Cluster-DAGs improve causal discovery with prior knowledge.
problem Finding cause-effect relationships from high-dimensional data.
method Cluster-DAGs as prior knowledge framework, modified constraint-based algorithms Cluster-PC and Cluster-FCI.
result Cluster-PC and Cluster-FCI outperform baselines without prior knowledge.
Study bounds for prices of European and American options with optional termination.
problem Bounding prices of options with potential termination.
method Duality results linking upper prices of vulnerable options to American options with constrained exercise times.
result Linking upper prices of vulnerable options to American options and game options.
The paper examines how timing of observations affects causal discovery methods.
problem The sensitivity of causal discovery methods to mismatched observation timing.
method Empirical and theoretical analysis of classical and recent causal discovery methods.
result Causal discovery methods are sensitive to sampling rate and window length.
New algorithm improves materials discovery using max K-Armed Bandit.
problem Maximizing material breakthroughs in materials discovery.
method Proposed a search algorithm based on max K-Armed Bandit (MKB) for materials discovery.
result Stable performance in late search stages, outperforming other bandit algorithms.
This work integrates domain knowledge into A*-based causal discovery methods.
problem Efficiently incorporating domain knowledge into A*-based causal discovery methods.
method Integrates various types of domain knowledge into A*-based causal discovery methods, reducing the graph search space and improving computational gains.
result Small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.
New option pricing formulas for American and Bermudan options.
problem Traditional option pricing models assume constant volatility and interest rate.
method Relaxing assumptions, using square root of Brownian motion, providing closed-form formulas.
result Simple, closed-form pricing formulas for American and Bermudan options.