10 accepted papers
Recent efforts target spoken language models (SLMs) that not only listen but also speak for more natural human-LLM interaction. Joint text-speech modeling is a promising direction to achieve this. However, the effectiveness of recent speech tokens for joint modeling remains under-explored. To addres…
Bayesian optimization (BO) with preference-based feedback has recently garnered significant attention due to its emerging applications. We refer to this problem as Bayesian Optimization from Human Feedback (BOHF), which differs from conventional BO by learning the best actions from a reduced feedbac…
Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. This research delves into enhancing the function-calling capabilities of LLMs by exploring different approaches, including prompt formats for integrating…
We introduce “Generative Fusion Decoding” (GFD), a novel shallow fusion framework, utilized to integrate large language models(LLMs) into cross-modal text recognition systems inlculding automatic speech recognition (ASR) and optical character recognition (OCR). We derive the formulas necessary to en…
Second-order optimization has been shown to accelerate the training of deep neural networks in many applications, often yielding faster progress per iteration on the training loss compared to first-order optimizers. However, the generalization properties of second-order methods are still being debat…
Achieving sample efficiency in Reinforcement Learning (RL) is primarily hinged on the efficient exploration of the underlying environment, but it is still unknown what are the best exploration strategies in different settings. We consider the *reward-free* RL problem, which operates in two phases: a…
The field of image generation has made significant progress thanks to the introduction of Diffusion Models, which learn to progressively reverse a given image corruption. Recently, a few studies introduced alternative ways of corrupting images in Diffusion Models, with an emphasis on blurring. Howev…
Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are trained on benchmarks with a fixed number of data points per task. This number is usually arbitrary and it is unknown how it affects performance at testing. Since labelling of data is expensi…
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a major downside for kernel-based models is the high computational cost; given a dataset of $n$ samples, the cost grows as…
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$. The problem can be cast as a Gaussian Process (GP) bandit where $f$ lives in a reproducing kernel Hilbert space (RKHS). The state of the art analysis of several learning algor…