Study analyzes broker's gain from trade in repeated context-based trading.
problem Maximizing traders' net utility in repeated context-based brokerage.
method Proposes algorithms achieving tight regret bounds in full and limited feedback settings.
result Achieves tight 1/2-approximation result for gain from trade.
Study optimizes auction pricing for strategic bidders in repeated auctions.
problem Optimizing revenue in auctions with multiple strategic bidders.
method Proposes a novel algorithm with strategic regret bound of O(log log T).
result Algorithm learns strategic buyer's valuation with theoretical guarantees.
A new method combines machine learning with mixed-effects models for better repeated measurement analysis.
problem Inference of linear coefficients in partially linear mixed-effects models with complex interactions and high-dimensional variables.
method Double machine learning approach to estimate nonparametrically nonlinear variables, then use standard linear mixed-effects techniques to estimate the linear coefficient.
result The estimated fixed effects coefficient converges at the parametric rate and is semiparametrically efficient.
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.
Loyalty emerges in markets through repeated interaction and adaptation.
problem Can loyalty arise spontaneously in markets?
method Stylized model of double auction markets with adaptive traders.
result Segregated market states are stable and provide higher rewards.
Study explores algorithmic collusion in repeated games using various learning dynamics.
problem Understanding algorithmic collusion in repeated games with different learning dynamics.
method Examines Q Q Q -learning, gradient learning, and other dynamics in a general repeated game setting. result Characterizes the set of payoff vectors achievable by these dynamics, revealing possibilities for collusion.
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
R-SQAIR adds relational bias to sequential object attention models for better object interactions.
problem Traditional sequential multi-object attention models struggle with relational inferences.
method Proposes R-SQAIR, a relational extension of SQAIR with a parallel pairwise interaction module.
result Demonstrates gains in object relations and combinatorial generalization over sequential mechanisms.
R2-B2 optimizes game interactions with recursive reasoning.
problem Optimizing interactions between boundedly rational agents with unknown payoff functions.
method Recursive Reasoning-Based Bayesian Optimization (R2-B2) for repeated games.
result R2-B2 achieves faster asymptotic convergence to no regret than non-recursive methods.
Interactive interface for specifying complex tasks through demonstrations.
problem Challenges in specifying high-dimensional reward functions for complex tasks.
method Interactive interface with a set of increasingly complex policies trained on demonstrations.
result Successfully learned a complex task (Lunar Lander) with interactive demonstrations.
This paper examines transitions in sniping behavior among algorithmic traders, finding new profitable strategies.
problem Understanding transitions from sure to probabilistic sniping in competitive algorithmic trading environments.
method Reinterpretation and extension of Menkveld and Zoican's stylized game, analysis of repeated games, sequential statistical testing.
result Probabilistic sniping can be profitable in certain conditions, resembling the prisoner's dilemma.
User strategization undermines algorithmic trustworthiness.
problem User strategic behavior corrupts algorithmic data and trust.
method Modeling user-platform interactions as a game, analyzing strategic behavior's short-term benefits and long-term harms.
result User strategization can initially benefit platforms but ultimately harms their ability to make accurate decisions.
Algorithm improves RL model selection for repeated games with utility maximization.
problem Optimal policy learning in repeated games with unknown opponent strategy.
method Proposes MRBEAR for average reward RL, applying to utility maximization in repeated games.
result Regret bound shows linear dependence on number of model classes in average reward RL.
How do individuals accumulate wealth as they interact economically? We outline the consequences of a simple microscopic model in which repeated pairwise exchanges of assets between individuals build the wealth distribution of a population. This distribution is determined for generic exchange rules --- transactions that…
Algorithm for decentralized competition among adaptive agents.
problem Decentralized competition among adaptive networks.
method Developed an algorithm for decentralized competition among teams of adaptive agents.
result Algorithm enables decentralized competition among adaptive agents.
Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities relate…
A study on how a principal can incentivize an agent to make better decisions in a repeated game.
problem Optimizing a principal's utility in a misaligned principal-agent bandit game.
method Developed nearly optimal learning algorithms for the principal's regret in multi-armed and linear contextual settings.
result The principal can iteratively learn an incentive policy to maximize her total utility.
New model accounts for sequential dependence in LLM reliability.
problem Uncertainty in LLM reliability assessment due to sequential interactions.
method Extended Bayesian framework with Hidden Markov Model for sequential dependence.
result Ignoring sequential dependence leads to overconfident reliability estimates.
Paper models planar pushing with probabilistic data-driven methods.
problem Predicting the outcomes and variability of planar pushing interactions.
method Variational Heteroscedastic Gaussian processes (VHGP) to capture mean and variance of stochastic function.
result Learned models outperform analytical models with fewer than 1000 samples.
New algorithm learns buyer behavior under realistic price constraints.
problem Learning buyer utility with practical price restrictions.
method Efficient online algorithm for non-linear utility learning.
result Can learn non-linear buyer utility with arbitrary price constraints.
Algorithm learns to play against unknown opponents in sequential games.
problem Designing strategies for a learner to interact with an unknown opponent in repeated sequential games.
method Kernel-based regularity assumptions and a novel algorithm combining bilevel optimization and online learning.
result Algorithm achieves sublinear regret guarantees and is effective in specific game settings.
Study optimal pricing algorithms for strategic buyers in repeated auctions.
problem Optimizing revenue in auctions with strategic buyers over multiple rounds.
method Proposed a novel algorithm that never decreases prices and has a strategic regret bound of Θ(log log T).
result Closed the open research question on no-regret horizon-independent weakly consistent pricing.
Study on HFTs' interactions with a large trader using mean field game theory.
problem Interactions between high-frequency traders and a large trader executing assets at discrete times.
method Modeling HFTs' behavior using a jump process and solving the equilibrium through mean field game approach.
result Inventory-averse HFTs lower LT's costs when market impact is large.
FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.
problem Online time series forecasting challenges in handling abrupt and recurring patterns.
method Inspired by CLS theory, FSNet uses a dynamic balance between fast adaptation and old knowledge retrieval.
result FSNet achieves robustness to both new and recurring patterns through dynamic balancing and associative memory.
A new method speeds up ALS for recommender systems by subsampling key elements.
problem High computational cost of ALS for large-scale datasets.
method Core-elements subsampling method for efficient ALS approximation.
result Achieves similar accuracy with significantly reduced computational time.
TDA improves accuracy of machine learning models for repeated measurements.
problem Limited accuracy of machine learning models for repeated measurements.
method Samples from data space, builds network graph based on data topology.
result TDA classifier achieves high accuracy (up to 96.8%) in repeated measurement datasets.
Optimal strategies are found for a repeated betting game using diffusion approximation.
problem Finding optimal strategies for a repeated betting game with i.i.d. outcomes.
method Constructing a diffusion approximation of the repeated game and analyzing the wealth share process.
result Necessary and sufficient conditions for the wealth share process to be transient or recurrent are derived.
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…
Convolutional networks struggle with repeating patterns in ECGs.
problem Modeling repeating patterns in electrocardiogram signals.
method Demonstrated through ECG examples, highlighting systemic issues in deep learning.
result Counterintuitive effects on generalization in deep networks.
Game theory helps machine learn better from adversarial queries.
problem Adversarial evasion in machine learning prediction.
method Repeated Bayesian Sequential Game to balance classifier selection and query type.
result Learner selects appropriate classifier for clean vs. adversarial queries.
Study geometric properties of symmetric matrices with repeated eigenvalues.
problem Investigate geometric properties of symmetric matrices with repeated eigenvalues.
method Explicitly compute the volume of the intersection with the sphere and prove an Eckart-Young-Mirsky-type theorem.
result Prove connections to Real Algebraic Geometry and Random Matrix Theory.
We study two systems of tangle equations that arise when modeling the action of the Integrase family of proteins on DNA. These two systems--direct and inverted repeats--correspond to two different possibilities for the initial DNA sequence. We present one new class of solutions to the tangle equations. In the case of i…
Scorio.jl ranks systems from repeated tasks using various methods.
problem Evaluating and ranking systems from repeated responses to shared tasks.
method Common tensor-based interface for multiple ranking methods.
result Pilot experiments show stability and runtime scaling.
We investigate the question of when distinct branched surfaces in the complement of a 2-bridge knot support essential surfaces with identical boundary slopes. We determine all instances in which this occurs and identify an infinite family of knots for which no boundary slopes are repeated.
LAFF algorithm balances adaptability and non-exploitability in repeated games.
problem Low regret in repeated games against unknown opponent classes.
method LAFF algorithm searches within sub-algorithms optimal for each opponent class and uses a punishment policy for exploitation.
result LAFF guarantees sublinear regret uniformly over possible opponents, except exploitative ones, for which it guarantees linear regret.
Improves generative Visual Dialog by asking diverse questions.
problem Generative Visual Dialog models degrade after a few rounds of interaction.
method Introduce a simple auxiliary objective to incentivize Qbot to ask diverse questions.
result Better dialog diversity, consistency, fluency, and detail with improved image relevance.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
problem Adaptation of users and services to reduce risk affects learning and performance.
method Analyzed a class of dynamics where users allocate participation and services update parameters.
result Repeated myopic updates with multiple learners lead to better outcomes than repeated risk minimization.
Link concordance and Whitney towers linked to Milnor invariants.
problem Link concordance and Whitney towers classification.
method Clasper surgeries, Whitney towers, and Milnor invariants.
result Link concordance and Whitney towers classified in terms of Milnor invariants.
This paper shows hedging algorithms improve performance in repeated matrix games.
problem Improving multi-agent learning algorithms in repeated matrix games.
method Develops and experiments with hedging algorithms combining a top-level and a set of basic algorithms.
result Well-selected hedging algorithms outperform previous MAL algorithms on repeated matrix games.
Algorithm learns to prune search space for repeated computations.
problem Exploit common structure in repeated similar problems.
method Exploit explore-exploit technique for pruning search space.
result Reduces runtime while provably outputting correct solutions.
AgensFlow learns multi-agent coordination policies from experience.
problem Difficult coordination choices in multi-agent systems built on LLMs.
method Online policy learning from repeated trajectories, treating decisions as learnable.
result Learned routing improves coordination-heavy workflows over static wiring.
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.
COBRAS uses super-instances to quickly cluster data with user queries.
problem Clustering data with user-defined pairwise constraints efficiently.
method Top-down construction of super-instances, iterative refinement based on user queries.
result COBRAS produces high-quality clusterings at fast run times.
It has long been known that a Milnor invariant with no repeated index is an invariant of link homotopy. We show that Milnor's invariants with repeated indices are invariants not only of isotopy, but also of self C_k-moves. A self C_k-move is a natural generalization of link homotopy based on certain degree k clasper su…
New method accelerates energetic variational inference using particle dynamics.
problem Efficiently solving variational inference problems with reduced computational cost.
method Particle-based variational inference with implicit scheme, inspired by energy quadratization and operator splitting.
result Significantly reduces computational cost compared to existing methods.
Study optimizes product assortment for retailers with repeated exposures and patience costs.
problem Optimizing product assortment for online retailers with repeated exposures and varying consumer patience.
method Developed a cascade multinomial logit model to capture repeated exposures and patience costs.
result Proposed an approximation solution to the assortment optimization problem.
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
problem Interaction between principal and agent in unknown environments with learning and exploration behaviors.
method Developed algorithms for self-interested and exploratory learning agents with bandit feedback, achieving regret bounds.
result Achieved O ~ ( T 2 / 3 ) \widetilde{O}(T^{2/3}) O ( T 2/3 ) regret bound for exploratory learning agent in i.i.d. reward setup. Incentive-aware recommender system for online platforms.
problem Myopic agents exploit optimal arms, not exploring alternatives.
method Model as multi-agent bandit problem, incentivizes exploration.
result Asymptotically optimal performance with ex-post fairness.