The paper uses linear function approximators to bias MCTS for general games.
problem Improving MCTS playing strength for general games.
method Using linear function approximators with local features for self-play training.
result Significantly improved playing strength in multiple board games.
Deep neural networks predict winning moves in Othello, surpassing previous players.
problem Applying deep learning to Othello, a game with unique characteristics.
method Comparing CNN architectures and board encodings, training on extensive data, and evaluating move prediction accuracy and playing strength.
result Best CNNs predict winning moves in Othello and defeat previous players.
Novel method uses information theory to measure causal influences during transient neural events.
problem Characterizing network interactions during transient neural events.
method Structural Causal Models, Information Theory, Transfer Entropy, Dynamic Causal Strength, Relative Dynamic Causal Strength.
result Introduced a novel measure, relative Dynamic Causal Strength, with theoretical and empirical support.
A new method uses randomized trials to estimate the strength of unobserved confounding.
problem Unobserved confounding compromises causal conclusions from non-randomized studies.
method Designs a statistical test to detect unobserved confounding strength and estimates a lower bound.
result Estimates an asymptotically valid lower bound on unobserved confounding strength.
Study shows Elo models fail to accurately measure transitive strength in competitive games.
problem Elo models fail to correctly identify the transitive component in real-world competitive games.
method Investigated the challenge of identifying the transitive component in games, proposed an extension of the Elo score.
result Disc ranking system assigns two scores: skill and consistency.
Language models trained on chess board states outperform those on moves, even with causal masking.
problem Applying causal masking to spatial data for training unimodal language models.
method Trained bidirectional and causal self-attention models on both spatial (board-based) and sequential (move-based) chess data.
result Models trained on spatial board states achieve stronger playing strength than those trained on sequential data, even with causal masking.
A new framework for playing and learning board games.
problem Tackling the tedious and repetitive aspects of coding for board game AI.
method Developed a generic TD(λ)-n-tuple agent for arbitrary board games. result TD(λ)-n-tuple outperforms other generic agents on various games. Bayesian optimization improved AlphaGo's win-rate from 50% to 66.5%.
problem Hyper-parameter tuning for machine learning models.
method Bayesian optimization for hyper-parameter tuning.
result Bayesian optimization improved AlphaGo's performance in self-play games.
This work formalizes guidance in diffusion models and introduces a stochastic control framework.
problem Lack of a solid theoretical foundation for guidance scheduling in diffusion models.
method Introduces a stochastic optimal control framework to cast guidance scheduling as an adaptive optimization problem.
result Establishes a principled foundation for more effective guidance in diffusion models.
The weighted and directed network of countries based on the number of overseas banks is analyzed in terms of its fragility to the banking crisis of one country. We use two different models to describe transmission of shocks, one local and the other global. Depending on the original source of the crisis, the overall siz…
Faster deep reinforcement learning by tightening optimality.
problem Challenging reinforcement learning training times.
method Combines deep Q-learning with constrained optimization.
result Significant improvements in training time and accuracy.
Optimal feature learning strength improves generalization in deep networks.
problem Understanding how feature learning strength affects generalization in practical settings.
method Empirical studies and theoretical analysis of gradient flow dynamics in two-layer ReLU nets.
result Optimal feature learning strength yields substantial generalization gains, contrary to the prevailing intuition.
Paper proposes a self-play method to approximate human evaluation of conversational agents.
problem Challenges in evaluating open-domain dialog systems.
method Self-play scenario with sentiment and semantic coherence proxies.
result Self-play metric correlates significantly with human ratings (r>.7, p<.05).
New fatiguing STDP rule helps SNNs learn spike timing from mixed rate codes.
problem STDP's sensitivity to input spike rates hinders learning from fine temporal correlations.
method Proposes a fatiguing STDP rule with short-term synaptic fatigue dynamics.
result FSTDP helps learn spike timing correlations from mixed rate codes.
New algorithm improves performance in nontransitive games.
problem Nontransitive games lack a clear winner.
method Geometric framework for agent objectives, PSRO_rN algorithm.
result PSRO_rN consistently outperforms alternatives in nontransitive games.
The banking systems that deal with risk management depend on underlying risk measures. Following the Basel II accord, there are two separate methods by which banks may determine their capital requirement. The Value at Risk measure plays an important role in computing the capital for both approaches. In this paper we an…
2D-PT improves sampling in constrained optimization problems.
problem Sampling Boltzmann distributions with soft constraints.
method Two-dimensional extension of parallel tempering.
result 2D-PT achieves near-ideal mixing in constrained problems.
Deep RL tested on combinatorial games from Erdos et al.
problem Evaluate reinforcement learning algorithms on challenging combinatorial games.
method Use Erdos-Selfridge-Spencer games with known optimal solutions.
result Demonstrates strengths and limitations of current RL approaches.
Characterizes winding of braided vector fields in tubular domains.
problem Understanding the topology of braided vector fields in complex domains.
method Defines field line winding as a measure of entanglement, proving its uniqueness in classifying vector field topology.
result Field line winding uniquely classifies the topology of braided vector fields.
Unified evaluation framework for sampling methods.
problem Lack of a standardized evaluation framework for sampling methods.
method Introduces a benchmark suite and performance criteria for sampling methods.
result Insights into strengths and weaknesses of existing sampling methods.
New algorithm identifies best arm in rested bandit setting.
problem Best arm identification in rested bandit with decreasing losses.
method Introduced a novel best arm identification problem and analyzed an arm elimination algorithm.
result Regret vanishes as time horizon increases, with convergence rate depending on expected loss function.
Consistent estimator derived for confounding strength in observational data.
problem Estimating confounding strength in observational data is challenging due to unobserved confounders.
method Derived and adapted a consistent estimator using tools from random matrix theory.
result The original estimator is not consistent, but an adapted one is.
Model shows how social norms and individual ethics affect tax evasion.
problem Effects of social norms and individual ethics on tax evasion.
method Agent-based model with simulations of different tax compliance behaviors.
result Threshold levels in society composition explain tax evasion extent.
Engine SixtyFour uses neural networks to play Crazyhouse chess.
problem Developing a neural network-based evaluation function for Crazyhouse chess.
method Created an ensemble model for Crazyhouse chess using a neural network.
result Early versions of the network have a playing level comparable to a strong amateur.
Study on state dynamics in Deep Echo State Networks, revealing the importance of inter-reservoir connections.
problem Understanding state dynamics in multi-layered RNNs.
method Tools from information theory and numerical analysis.
result Inter-reservoir connections enrich representations in higher layers of DeepESNs.
Introduces 'social bow tie' to quantify tie strength in social networks.
problem Understanding tie strength and its influencing factors in social networks.
method Introduced 'social bow tie' framework, defined metrics, used random forests and regression models.
result Bow tie metrics are highly predictive of tie strength, and tie strength is influenced by overlapping and non-overlapping social circles.
Bayesian model infers strengths from noisy tennis match outcomes.
problem Ranking tennis players from match outcomes.
method Bayesian approach to infer unobserved strengths and mapping function.
result Bayesian approach robust to different model specifications.
Neural network memorizes external stimuli through synaptic strength changes.
problem Memory and classification in neural networks.
method One-to-one mapping between stimulus and synaptic strength under synaptic plasticity constraints.
result Neural network can memorize external stimuli through synaptic changes.
A lightweight framework improves convergence and stability of PINNs for complex PDEs.
problem Training instability and reduced accuracy in PINNs for complex PDEs.
method Adaptive curvature correction using secant information to optimize first-order optimizers.
result Consistent improvements in convergence speed, stability, and accuracy over standard optimizers.
Paper shows softmax output misleads in evaluating adversarial example strength.
problem Softmax output misleads in evaluating adversarial example strength.
method Demonstrates how adversarial examples can exploit softmax properties.
result Softmax output is a poor indicator of adversarial example strength.
This paper calculates interaction strength for translation surfaces with multiple singularities.
problem Computing the interaction strength of translation surfaces with multiple singularities is challenging.
method The authors study interaction strength of specific families of translation surfaces, including regular polygons and Bouw-Möller surfaces.
result The paper provides exact computations of KVol on translation surfaces with multiple singularities.
Study shows CNNs can perform well with less data using biological synaptic distributions.
problem Training deep neural networks with limited data.
method Synthesizing CNNs using log-normal or correlated center-surround synaptic strength distributions.
result CNNs with biological synaptic strength distributions can perform well with fewer data samples.
Synaptic pruning reduces CNNs by 96% on CIFAR-10.
problem Memory and computation constraints in CNNs for mobile devices.
method Synaptic Pruning: data-driven method to prune connections based on Synaptic Strength.
result Significant size reduction and computation saving with up to 96% pruning on CIFAR-10.
A new approach switches between simple and complex models to handle concept drifts in regression tasks.
problem Handling concept drifts in regression models to maintain accurate predictions over time.
method Error Intersection Approach: switches between simple and complex models based on drift detection.
result The Error Intersection Approach significantly outperforms baselines in handling concept drifts in a real-world taxi demand dataset.
The paper examines how spike strengths and alignments affect overfitting in linear regression models.
problem The impact of spike strengths and alignments on overfitting in linear regression models.
method Characterization of generalization error through exact expressions and analysis of spike strengths, aspect ratio, and target alignment.
result Increasing spike strength can lead to catastrophic overfitting before benign overfitting, especially in well-specified aligned problems.
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. This model was designed to be applied to sports such as soccer or hockey, in which contest outcomes take value in a limi…
We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends…
While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge et al. (2012), it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of d…
A new method automatically and dynamically sets learning rates in deep learning.
problem Determining the appropriate learning rate in deep learning tasks is challenging and often subjective.
method Local Quadratic Approximation (LQA) to automatically and dynamically set learning rates.
result The proposed method leads to nearly optimal learning rates in a computationally efficient way.
Regularization improves portfolio optimization under Expected Shortfall risk measure.
problem Optimizing large portfolios with Expected Shortfall under ℓ2 regularization. method Analytical calculation of portfolio optimization with regularization.
result Regularization significantly reduces estimation error, especially in data-limited scenarios.
Develops new tests for high-dimensional models with mixed signal strengths.
problem Challenges in testing models with many signals and high-dimensional data.
method Moment matching formulation for developing new tests.
result Demonstrates optimality of GRIP test for various model types.
Model shows cascading failures are more severe in multiplex networks than single-layer networks.
problem Underestimation of risks in single-layer network analyses due to overlooked impact of weak layers.
method Simple model of cascading failure on multiplex networks of weight-heterogeneous layers.
result Multiplex model produces more catastrophic cascading failures than single-layer model.
In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting devi…
Financial markets are well known examples of multi-fractal complex systems that have garnered much interest in their characterization through complex network theory. The recent studies have used correlation based distance metrics for defining and analyzing financial networks. In this work the singularity strength is em…
New sampling method makes fictitious play consistent in repeated games.
problem Fictitious play fails to be Hannan consistent in repeated games.
method Introduced sampled fictitious play with Bernoulli sampling, proving it is Hannan consistent.
result Sampled fictitious play is Hannan consistent using anti-concentration results.
Study characterizes community structure in Japanese production network.
problem Characterize community structure in a large-scale production network.
method Directed network analysis of one million Japanese firms.
result Large fraction of firms have local interactions, and community strengths are heterogeneous.
Study bridges GARCH and NN models for volatility forecasting.
problem Lack of interaction between GARCH and NN approaches for volatility forecasting.
method Established equivalence between GARCH and NN models, introduced GARCH-NN approach.
result GARCH-NN approach enhances volatility forecasting compared to standalone models.
This paper analyzes MCMC algorithms on large graphs using Dirichlet forms.
problem Analyzing the behavior of MCMC algorithms in high-dimensional problems.
method Utilizes Mosco convergence of Dirichlet forms to study RWM algorithm on large graphs.
result Demonstrates the advantages of Dirichlet form approach over standard diffusion methods.