Posters

This paper introduces Belief-State Query (BSQ) constraints for user-aligned planning in partially observable settings, providing algorithms with guaranteed user alignment and computational feasibility…

BehaviorGPT, a novel autoregressive Transformer, simulates realistic traffic agent behavior by modeling each time step as ‘current’, achieving top results in the 2024 Waymo Open Sim Agents Challenge.

BECAUSE: a novel algorithm for generalizable offline model-based reinforcement learning that leverages bilinear causal representation to mitigate objective mismatch caused by confounders in offline da…

New algorithms conquer adversarial low-rank MDPs, improving regret bounds for unknown transitions and bandit feedback.

Goldfish Loss: A novel training method for LLMs dramatically reduces memorization without impacting performance, addressing key safety, privacy, and copyright concerns.

Bayesian-PEFT boosts vision model accuracy and confidence in few-shot learning by integrating Bayesian components into PEFT, solving the underconfidence problem.

Learners can improve accuracy in strategic classification by selectively revealing partial classifier information to agents, strategically guiding agent behavior and maximizing accuracy.

GraphComBO efficiently optimizes functions defined on node subsets within graphs using Bayesian Optimization. It tackles challenges posed by combinatorial complexity and computationally expensive fun…

Boost machine learning model robustness by minimizing a novel data-driven risk criterion that blends Bayesian nonparametrics and smooth ambiguity aversion, ensuring superior out-of-sample performance.

GMDI: a novel Bayesian domain adaptation algorithm significantly improves adaptation by dynamically modeling domain indices using Gaussian Mixture Models, outperforming state-of-the-art methods.

BACON: a novel Bayesian adaptive calibration and optimal design algorithm maximizes information gain for data-efficient computer model calibration, significantly outperforming existing methods in synt…

This study solves a key challenge in neural network learning, deriving a closed-form expression for the Bayes-optimal test error of extensive-width networks with quadratic activation functions from qu…

BEEBO: a novel acquisition function for Bayesian Optimization, offering superior explore-exploit balance and handling large batches efficiently, even with noisy data.

LLM long-context ability is fundamentally limited by RoPE’s base parameter, which determines an absolute lower bound for achievable context length.

This paper introduces ‘bandits with ranking feedback,’ a novel bandit variation providing ranked feedback instead of numerical rewards. It proves instance-dependent cases require superlogarithmic reg…

MAXMINLCB, a novel game-theoretic algorithm, efficiently solves bandit problems with preference feedback over continuous domains, providing anytime-valid, rate-optimal regret guarantees.

The Confidence-Rated Bandits with Abstentions (CBA) algorithm significantly improves reward bounds for prediction with expert advice by strategically leveraging an abstention action.

This paper reveals the optimal mistake bounds for online multiclass classification under bandit feedback, showing the cost of limited feedback is at most O(k) times higher than full information, where…

This paper introduces BSR, a novel banded square root matrix factorization for differentially private model training. Unlike existing methods, BSR avoids computationally expensive optimization, enabli…

BAN: a novel backdoor defense using adversarial neuron noise for efficient detection and mitigation.