Given the GPS coordinates of a large collection of human agents over time, how can we model their mobility behavior toward effective anomaly detection (e.g. for bad-actor or malicious behavior detection) without any labeled data? Human mobility and trajectory modeling have been studied extensively with varying capacity to handle complex input, and performance-efficiency trade-offs. With the arrival of more expressive models in machine learning, we attempt to model GPS data as a sequence of stay-point events, each with a set of characterizing spatiotemporal features, and leverage modern sequence models such as Transformers for un/self-supervised training and inference. Notably, driven by the inherent stochasticity of certain individuals' behavior, we equip our model with aleatoric/data uncertainty estimation. In addition, to handle data sparsity of a large variety of behaviors, we incorporate epistemic/model uncertainty into our model. Together, aleatoric and epistemic uncertainty enable a robust loss and training dynamics, as well as uncertainty-aware decision making in anomaly scoring. Experiments on large expert-simulated datasets with tens of thousands of agents demonstrate the effectiveness of our model against both forecasting and anomaly detection baselines.

Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that continual personalization (CP) aims to solve. Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as diffusion classifier (DC) scores, for continual personalization of text-to-image diffusion models. Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization. Using several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art.

Deep reinforcement learning (RL) policies, although optimal in terms of task rewards, may not align with the personal preferences of human users. To ensure this alignment, a naive solution would be to retrain the agent using a reward function that encodes the user's specific preferences. However, such a reward function is typically not readily available, and as such, retraining the agent from scratch can be prohibitively expensive. We propose a more practical approach - to adapt the already trained policy to user-specific needs with the help of human feedback. To this end, we infer the user's intent through trajectory-level feedback and combine it with the trained task policy via a theoretically grounded dynamic policy fusion approach. As our approach collects human feedback on the very same trajectories used to learn the task policy, it does not require any additional interactions with the environment, making it a zero-shot approach. We empirically demonstrate in a number of environments that our proposed dynamic policy fusion approach consistently achieves the intended task while simultaneously adhering to user-specific needs.

Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.

We study which machine learning algorithms have tight generalization bounds. First, we present conditions that preclude the existence of tight generalization bounds. Specifically, we show that algorithms that have certain inductive biases that cause them to be unstable do not admit tight generalization bounds. Next, we show that algorithms that are sufficiently stable do have tight generalization bounds. We conclude with a simple characterization that relates the existence of tight generalization bounds to the conditional variance of the algorithm's loss.

We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set $S \subseteq \mathbb{R}^d$. Kontonis, Tzamos, and Zampetakis (FOCS'19) gave a $d^{\mathrm{poly}(1/\varepsilon)}$ time algorithm for finding $\varepsilon$-accurate parameters for the special case of Gaussian distributions with diagonal covariance matrix. Recently, Diakonikolas, Kane, Pittas, and Zarifis (COLT'24) showed that this exponential dependence on $1/\varepsilon$ is necessary even when $S$ belongs to some well-behaved classes. These works leave the following open problems which we address in this work: Can we estimate the parameters of any Gaussian or even extend beyond Gaussians? Can we design $\mathrm{poly}(d/\varepsilon)$ time algorithms when $S$ is a simple set such as a halfspace? We make progress on both of these questions by providing the following results: 1. Toward the first question, we give a $d^{\mathrm{poly}(\ell/\varepsilon)}$ time algorithm for any exponential family that satisfies some structural assumptions and any unknown set $S$ that is $\varepsilon$-approximable by degree-$\ell$ polynomials. This result has two important applications: 1a) The first algorithm for estimating arbitrary Gaussian distributions from samples truncated to an unknown $S$; and 1b) The first algorithm for linear regression with unknown truncation and Gaussian features. 2. To address the second question, we provide an algorithm with runtime $\mathrm{poly}(d/\varepsilon)$ that works for a set of exponential families (containing all Gaussians) when $S$ is a halfspace or an axis-aligned rectangle. Along the way, we develop tools that may be of independent interest, including, a reduction from PAC learning with positive and unlabeled samples to PAC learning with positive and negative samples that is robust to certain covariate shifts.

Edge signal processing facilitates distributed learning and inference in the client-server model proposed in federated learning. In traditional machine learning, clients (IoT devices) that acquire raw signal samples can aid a data center (server) learn a global signal model by pooling these distributed samples at a third-party location. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of sensitive private data and communication rate constraints. This necessitates a learning approach that communicates a processed approximation of the distributed samples instead of the raw signals. Such a decentralized learning approach using signal approximations will be termed distributed signal analytics in this work. Overpredictive signal approximations may be desired for distributed signal analytics, especially in network demand (capacity) planning applications motivated by federated learning. In this work, we propose algorithms that compute an overpredictive signal approximation at the client devices using an efficient convex optimization framework. Tradeoffs between communication cost, sampling rate, and the signal approximation error are quantified using mathematical analysis. We also show the performance of the proposed distributed algorithms on a publicly available residential energy consumption dataset.

Beyond debuting new features for Copilot AI PCs and Windows 11's 2024 update, Microsoft is also giving its Copilot AI a makeover on the web, mobile and desktop. That includes a slightly friendlier interface wherever you access it, along with new capabilities like Copilot Voice, which allows you to talk conversationally with the AI assistant. Ultimately, Microsoft is aiming for Copilot to be seen as more than just a party trick for generative AI search and image creation -- it's trying to make it a core part of your daily workflow. That starts with a cleaner and simpler UI that makes Copilot look different than a boring old search engine. You'll also be able to access Copilot from within Whatsapp, which could be useful if you want to avoid Meta's AI assistant.

Microsoft deleted the over-eager office assistant Clippy some 17 years ago, but the vision for an friendly and optimistic AI helper has apparently found its way out of the Recycle Bin. The company is overhauling Copilot, the text-based artificial intelligence tool bundled with Windows and other software, with the addition of vision, voice, and the ability to solve more complex problems--along with a more "encouraging" personality. "We really are at this amazing kind of transition point," says Mustafa Suleyman, CEO of Microsoft AI. "AI companions now see what we see, hear what we hear, and speak in the same language that we use to communicate with one another." Copilot has so far met with a mixed response, with some users complaining of lag or vagueness in its responses, but Microsoft is betting that the tool could eventually become an integral part of Windows, Office, and beyond. By incorporating OpenAI's AI algorithms into software that is used by hundreds of millions of people, the company is also at the forefront of testing the potential for AI to boost productivity in office work.

Logical reasoning systems are essential for rigorous automatic reasoning. The focus of the 2024 Logic and Practice of Programming workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints. Trustworthy AI requires programming with rules and constraints for expressing and solving knowledge-intensive inference and combinatorial problems. A wide range of programming models have been proposed, including but not limited to the following, and essentially all of them require or support imperative programming for use in practical applications.