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Seminar Series

September 16

Multi-sensory perception from top to down

Anna Min
CSAIL

Part Of

4:00P
- 4:30P

Location

32-G882, Hewlett Room
Add to Calendar 2024-09-16 16:00:00 2024-09-16 16:30:00 America/New_York Multi-sensory perception from top to down Abstract: Human sensory experiences, such as vision, hearing, touch, and smell, serve as natural interfaces for perceiving and reasoning about the world around us. Understanding 3D environments is crucial for applications like video processing, robotics, and augmented reality. This work explores how material properties and microgeometry can be learned through cross-modal associations between sight, sound, and touch. I will introduce a method that leverages in-the-wild online videos to study interactable audio generation via dense visual cues. Additionally, I will share recent advancements in multimodal scene understanding and discuss future directions for the field.Bio: Anna is a senior undergraduate in Tsinghua University. Her previous research lies in multi-modal perception, from the perspective of audio and vision. She is an intern in Jim Glass's group. 32-G882, Hewlett Room

September 23

October 07

Contextualizing Self-Supervised Learning: A New Path Ahead

Yifei Wang
CSAIL

Part Of

4:00P
- 4:30P

Location

32-G882 (Hewlett Room)
Add to Calendar 2024-10-07 16:00:00 2024-10-07 16:30:00 America/New_York Contextualizing Self-Supervised Learning: A New Path Ahead Abstract: Self-supervised learning (SSL) has achieved remarkable progress over the years, particularly in visual domains. However, recent advancements have plateaued due to performance bottlenecks, and more focus has shifted towards generative models. In this talk, we step back to analyze existing SSL paradigms and identify the lack of context as their most critical obstacle. To address this, we explore two approaches that incorporate contextual knowledge into SSL: 1. Contextual Self-Supervised Learning: Here, learned representations adapt their inductive biases to diverse contexts, enhancing the flexibility and generality of SSL. 2. Self-Correction: This method allows foundation models to refine themselves by reflecting on their own predictions within a dynamically evolving context.These insights illustrate new paths to craft self-supervision and highlight context as a key ingredient for building general-purpose SSL.Paper Links: * In-Context Symmetries: Self-Supervised Learning through Contextual World Models (https://arxiv.org/pdf/2405.18193) * A Theoretical Understanding of Self-Correction through In-context Alignment (https://arxiv.org/pdf/2405.18634)Both papers to be covered in this talk were accepted to NeurIPS 2024. The theoretical work on understanding self-correction received the Spotlight Award at the ICML 2024 ICL Workshop.Bio: Yifei Wang is a postdoc at CSAIL, advised by Prof. Stefanie Jegelka. He earned his bachelor’s and Ph.D. degrees from Peking University. Yifei is generally interested in machine learning and representation learning, with a focus on bridging the theory and practice of self-supervised learning. His first-author works have been recognized by multiple best paper awards, including the Best ML Paper Award at ECML-PKDD 2021, the Silver Best Paper Award at the ICML 2021 AdvML Workshop, and the Spotlight Award at the ICML 2024 ICL Workshop. 32-G882 (Hewlett Room)

October 16

October 21

Objective Approaches in a Subjective Medical World

Yuexing Hao

Part Of

4:00P
- 5:00P

Location

32-G882
Add to Calendar 2024-10-21 16:00:00 2024-10-21 17:00:00 America/New_York Objective Approaches in a Subjective Medical World Abstract: In today’s healthcare system, patients often feel disconnected from clinical professionals and their care journey. They receive a “one-size-fits-all” plan and are left out of the decision-making process, which can lead to a less satisfying experience. My research focuses on applying advanced AI technologies, including large language models, machine learning, and IoT, to address challenges in healthcare, particularly in patient-centered healthcare delivery. I aim to enhance the accuracy and efficiency of healthcare systems by using these "objective approaches" to navigate the subjective aspects of medical practice, such as clinician notes and patient preferences found in electronic health records. A key aspect of my work is improving the transparency of AI-based healthcare applications, making them more understandable and trustworthy for both clinicians and patients, by addressing critical issues such as building trust in AI systems and ensuring these technologies effectively meet the needs of patients and healthcare providers. Additionally, I emphasize the importance of personalizing healthcare by considering each patient's unique circumstances, including their preferences and socio-economic conditions. This research applies AI across various areas, from specific diseases like cancer to broader healthcare contexts, with the goal of improving both the delivery and experience of healthcare. My work contributes to the development of AI tools that not only enhance clinical decision-making but also foster better human-AI interaction, ultimately leading to improved healthcare outcomes. 32-G882

October 28

Generative Models for Biomolecular Prediction, Dynamics, and Design

Hannes Stärk and Bowen Jing
MIT CSAIL

Part Of

4:00P
- 5:00P

Location

32-G882 (Hewlett)
Add to Calendar 2024-10-28 16:00:00 2024-10-28 17:00:00 America/New_York Generative Models for Biomolecular Prediction, Dynamics, and Design Abstract: We lay out the three avenues in which we think generative models are especially valuable for modeling biomolecules. 1) Hard prediction tasks can be better addressed with generative models that can suggest and rank multiple solutions (e.g. docking). 2) The dynamics and conformations of biomolecules can be captured with generative models (e.g. protein conformational ensembles and MD trajectories). 3) Designing new biomolecules can be accelerated, informed by samples or likelihoods from generative models (e.g. protein binder or regulatory DNA design). 32-G882 (Hewlett)

November 13

November 18

Dependence Induced Representation Learning

Xiangxiang Xu
EECS/RLE, MIT

Part Of

4:00P
- 5:00P

Location

Room 32-G882
Add to Calendar 2024-11-18 16:00:00 2024-11-18 17:00:00 America/New_York Dependence Induced Representation Learning Abstract: Despite the vast progress in deep learning practice, theoretical understandings of learned feature representations remain limited. In this talk, we discuss three fundamental questions from a unified statistical perspective:(1) What representations carry useful information?(2) How are representations learned from distinct algorithms related?(3) Can we separate representation learning from solving specific tasks?We formalize representations that extract statistical dependence from data, termed dependence-induced representations. We prove that representations are dependence-induced if and only if they can be learned from specific features defined by Hirschfeld–Gebelein–Rényi (HGR) maximal correlation. This separation theorem signifies the key role of HGR features in representation learning and enables a modular design of learning algorithms. Specifically, we demonstrate the optimality of HGR features in simultaneously achieving different design objectives, including minimal sufficiency (Tishby's information bottleneck), information maximization, enforcing uncorrelated features (VICReg), and encoding information at various granularities (Matryoshka representation learning). We further illustrate that by adapting HGR features, we can obtain representations learned by distinct practices, from cross-entropy or hinge loss minimization, non-negative feature learning, and neural density ratio estimators to their regularized variants. We also discuss the applications of our analyses in interpreting learning phenomena such as neural collapse, understanding existing self-supervised learning practices, and obtaining more flexible designs, e.g., inference-time hyperparameter tuning.Bio: Xiangxiang Xu received the B.Eng. and Ph.D. degrees in electronic engineering from Tsinghua University, Beijing, China, in 2014 and 2020, respectively. He is a postdoctoral associate in the Department of EECS at MIT. His research focuses on information theory, statistical learning, representation learning, and their applications in understanding and developing learning algorithms. He is a recipient of the 2016 IEEE PES Student Prize Paper Award in Honor of T. Burke Hayes and the 2024 ITA (Information Theory and Applications) Workshop Sand Award.

November 25

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Yosuke Tanigawa
MIT CSAIL

Part Of

4:00P
- 5:00P

Location

Room 32-G882
Add to Calendar 2024-11-25 16:00:00 2024-11-25 17:00:00 America/New_York Power of inclusion: Enhancing polygenic prediction with admixed individuals Zoom Link: https://mit.zoom.us/j/94204370795?pwd=eFZwYXVuWmVsQzE1UTRZN2VtY0lkUT09 with passcode 387975Abstract: Predicting heritable traits and genetic liability of disease from individuals’ genomes has important implications for tailoring medical prevention and intervention strategies in precision medicine. Polygenic score (PGS), a statistical approach, has recently attracted substantial attention due to its potential relevance in clinical practice. Admixed individuals offer unique opportunities for addressing limited transferability in PGSs. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals in developing more equitable PGS models.Bio: Yosuke Tanigawa, PhD, is a research scientist at MIT’s Computer Science and Artificial Intelligence Lab. To incorporate interindividual differences in disease prevention and treatment, he develops computational and statistical methods, focusing on predictive modeling with high-dimensional human genetics data, multi-omic dissection of disease heterogeneity, and therapeutic target discovery. His recent works focus on inclusive training strategies for genetic prediction algorithms and dissecting the molecular, cellular, and genetic basis of phenotypic heterogeneity in Alzheimer’s disease. He received many awards, including the Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research and MIT Technology Review’s Innovators Under 35 Japan.

December 02

Truthfulness of Calibration Measures

Mingda Qiao
MIT CSAIL

Part Of

4:00P
- 4:30P

Location

32-G575
Add to Calendar 2024-12-02 16:00:00 2024-12-02 16:30:00 America/New_York Truthfulness of Calibration Measures Abstract: We initiate the study of the truthfulness of calibration measures in sequential prediction. A calibration measure is said to be truthful if the forecaster (approximately) minimizes the expected penalty by predicting the conditional expectation of the next outcome, given the prior distribution of outcomes. Truthfulness is an important property of calibration measures, ensuring that the forecaster is not incentivized to exploit the system with deliberate poor forecasts. This makes it an essential desideratum for calibration measures, alongside typical requirements, such as soundness and completeness.We conduct a taxonomy of existing calibration measures and their truthfulness. Perhaps surprisingly, we find that all of them are far from being truthful. That is, under existing calibration measures, there are simple distributions on which a polylogarithmic (or even zero) penalty is achievable, while truthful prediction leads to a polynomial penalty. Our main contribution is the introduction of a new calibration measure termed the Subsampled Smooth Calibration Error (SSCE) under which truthful prediction is optimal up to a constant multiplicative factor. Bio: Mingda Qiao a FODSI postdoc hosted by Ronitt Rubinfeld at the MIT Theory of Computation (TOC) Group, and an incoming assistant professor at UMass Amherst (starting Fall'25). His research focuses on the theory of prediction, learning, and decision-making in sequential settings, as well as collaborative federated learning. Prior to MIT, Mingda was a FODSI postdoc at UC Berkeley, received his PhD in Computer Science from Stanford University, and received his BEng in Computer Science from Tsinghua University.